AN ANALYSIS of an ADVANCED SOFTWARE BUSINESS MODEL for MAGNETIC RESONANCE IMAGING DATA POST PROCESSING by NICHOLAS BARRON Submi

Home , Medical procedure

AN ANALYSIS OF AN ADVANCED SOFTWARE BUSINESS MODEL FOR

MAGNETIC RESONANCE IMAGING DATA POST PROCESSING

NICHOLAS BARRON

Submitted in partial fulfillment of the requirements

For the degree of Master of Science

Adviser: Edward Caner

Department of Physics

CASE WESTERN RESERVE UNIVERSITY

May 2016

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Nicholas Barron

candidate for the degree of Master of Science*.

Committee Chair

Edward Caner

Committee Member

Robert W. Brown

Committee Member

Michael Martens

Date of Defense

March 21, 2016

*We also certify that written approval has been obtained

for any proprietary material contained therein.

Dedication

I would like to dedicate this paper to the memory of my grandfather, Allen Friedman. From my elementary school days until my last semester in the STEP program, he was my biggest supporter. He passed away just weeks before the completion of my thesis.

iii

Table of Contents

Dedication ...... iii Table of Contents ...... iv List of Tables ...... viii List of Figures ...... ix Acknowledgements ...... x 1 Abstract ...... 1 2 Introduction ...... 2 3 MRI Post Processing Software Industry Analysis ...... 4 3.1 Users ...... 4 3.2 Customers ...... 4 3.2.1 Hospitals ...... 5 3.2.2 Imaging Centers ...... 6 3.3 Purchasing Considerations ...... 6 3.3.1 Cost of Care ...... 6 3.3.1.1 Affordable Care Act ...... 7 3.3.1.2 Reimbursement ...... 7 3.3.2 Clinical Evidence ...... 9 3.4 Market Size Estimates ...... 9 3.4.1 DICOM Market ...... 9 3.4.2 PACS Market ...... 10 4 Magnetic Resonance Innovations, Inc...... 12 4.1 History ...... 12 4.2 Team ...... 12 4.2.1 Management ...... 13 4.2.2 Advisors ...... 13 4.3 Key Activities ...... 14 4.3.1 Research ...... 14 4.3.2 Products ...... 14 4.3.2.1 SWI ...... 14 4.3.2.2 SPIN, NeuSPIN ...... 15 4.3.3 Consulting Services ...... 16 4.3.4 Patent Portfolio ...... 16 4.3.4.1 Siemens License ...... 16

4.3.4.2 Pending Patents ...... 16 4.4 New Business Model ...... 17 4.4.1 Advanced Modules ...... 17 4.4.1.1 White Matter Hyperintensity ...... 17 4.4.1.2 Iron Quantification ...... 18 4.4.1.3 Cerebral Microbleed Detection ...... 19 4.4.1.4 Flow Quantification ...... 19 4.4.1.5 Perfusion Weighted Imaging ...... 20 4.4.2 Value Proposition Analysis ...... 20 4.4.2.1 Need ...... 21 4.4.2.2 Approach ...... 21 4.4.2.3 Benefits per Cost ...... 22 4.4.2.4 Competition ...... 22 4.4.3 Go to Market Strategy ...... 23 4.4.3.1 Package Sales ...... 23 4.4.3.1.1 Target Customers ...... 24 4.4.3.1.2 Pricing ...... 24 4.4.3.2 Cloud Processing ...... 24 4.4.3.2.1 Target Customers ...... 25 4.4.3.2.2 Pricing ...... 25 4.4.4 Cost Structure ...... 26 4.4.4.1 Staffing Needs ...... 26 4.4.4.2 FDA Costs ...... 27 4.4.4.3 Other Expenses ...... 27 4.4.5 Pro-Forma Financial Models ...... 27 4.4.5.1 Worst Case ...... 28 4.4.5.2 Likely Case ...... 29 4.4.5.3 Best Case ...... 30 4.4.6 Capital Needs ...... 30 4.4.6.1 NIH Grants ...... 31 4.4.6.2 Private Sources ...... 31 4.4.7 Development Timeline ...... 31 4.4.8 Traction ...... 32 5 Medical Background ...... 33 5.1 Brain Biology ...... 33

5.1.1 Fixed Anatomical Structures...... 33 5.1.1.1 Iron ...... 33 5.1.1.2 White & Grey Matter ...... 34 5.1.2 Dynamic Anatomical Structures ...... 34 5.1.2.1 Blood...... 34 5.1.2.2 Cerebrospinal Fluid ...... 35 5.2 Magnetic Resonance Imaging ...... 35 5.2.1 Physics ...... 35 5.2.1.1 Basic Principles ...... 36 5.2.1.1.1 Spin ...... 36 5.2.1.1.2 Relaxation ...... 36 5.2.1.1.3 Phase ...... 37 5.2.1.1.4 Acquisition Time ...... 38 5.2.1.2 Advanced Principles ...... 38 5.2.1.2.1 Susceptibility ...... 38 5.2.1.2.2 Sequence Design ...... 39 5.2.2 Hardware Components ...... 40 5.2.2.1 Main Magnet ...... 40 5.2.2.2 RF Coil ...... 41 5.2.2.3 Gradient Coil ...... 41 5.2.3 Software Market Segmentation ...... 41 5.2.3.1 System Operation ...... 41 5.2.3.2 Image Processing ...... 42 5.2.3.3 DICOM Viewers ...... 42 5.2.3.4 PACS Systems ...... 43 6 Conclusions ...... 44 6.1 Industry Direction ...... 44 6.2 MRI Inc...... 44 6.2.1 Recruitment ...... 45 6.2.2 Timeline ...... 45 6.2.3 Overall ...... 45 7 Appendices ...... 47 7.1 Patents owned by MRI Inc...... 47 7.1.1 Issued Patents ...... 47 7.1.2 Pending ...... 49

7.2 Breakeven Calculations ...... 49 End Notes ...... 51 References ...... 53

vii

List of Tables

Table 1 – Worst Case financial summary 28

Table 2 – Likely Case financial summary 29

Table 3 – Best Case financial summary 30

viii

List of Figures

Figure 1 – In phase bulk magnetization, after an initial 90-degree RF pulse. 37

Figure 2 – Dephased spins 37

Figure 3 – Single Spin Echo at t = 2τ. The laboratory signal profile is limited by T2 effects,

while the signal itself is dependent on the T2* effects, and re-phase at the echo. 39

Figure 4 – Like the single spin echo experiment, a signature echo time dictates when the

signal profile recovers due to re-phasing. Additional RF-pulses increase the number of points that can be collected per sequence repetition. 40

Figure 5 – Flow of information throughout the MRI process 42

Acknowledgements

I would like first to recognize the role that CWRU has played over the past few years. Since my freshman year as an undergraduate in the Physics Department, I have been surrounded by a group of dedicated educators, advisors, and staff. I have been guided through curricular and extracurricular endeavors, career advising, financial counseling, and had access to a vast network of individuals in every time of need.

Specifically, I would like to acknowledge the guidance provided by Ed Caner and Bruce

Terry. As a sophomore undergraduate student, I was invited to Bruce’s office to share an idea. That day, I gained one of the most critical, detailed, challenging, and thoughtful mentors I’ve ever had. That same year, I was introduced to Ed Caner, who became a constant source of feedback, on most every aspect of my life. From a mentor, he turned recruiter, and now finally academic advisor. I have spent countless hours learning, listening, challenging, and thanking them both for their unending support.

Finally, I would like to acknowledge the guidance provided by individuals at BioEnterprise and Magnetic Resonance Innovations, Inc. Immediately putting to use the lessons learned through the STEP program has been an invaluable experience.

An Analysis of an Advanced Software Business Model for

Magnetic Resonance Imaging Data Post Processing

Abstract

NICHOLAS BARRON

1 Abstract

Over the past few decades, many of the most significant contributions to the field of magnetic resonance imaging have been made by Dr. E. Mark Haacke, his collaborators, and most recently his company. In 1994, Dr. Haacke founded Magnetic Resonance Innovations

Inc., which now creates software for advanced MRI postprocessing, DICOM viewer software, sequence design, and more. The company is in the process of pivoting toward a direct sales business model to hospitals and imaging centers. This paper will present multiple models concerning revenue, staffing, and investment strategies being considered for the immediate future. It is the opinion of the author that assuming key milestones are reached, executive staffing needs are satisfied, and clinical research supporting the need for advanced analysis continues, that the proposed business plan accurately represents the future of the company, specifically reaching over $20MM in revenue selling software to hospitals and imaging centers within five years.

2 Introduction

When doctors cannot diagnose patients based on immediately observable symptoms, their

medical history, or other diagnostic lab test, they may employ a number of medical imaging

modalities to examine the patient internally without performing invasive and expensive

surgeries. Commonly employed medical imaging modalities include X-Ray, magnetic

resonance imaging (MRI), computed tomography (CT), and positron emission tomography

(PET). Each modality has its strengths and weaknesses, cost drivers, and risks. Despite the

high cost associated with MRI, its functionality is superior to other modalities in many ways.

If a doctor orders an MRI, a number of anatomical structures of interest are identified, and a

technician prepares the machine. The patient lies within the MRI machine for no less than

30 minutes, and in the case of longer exams, over an hour. After an MRI exam concludes,

data from one or more sequences is stored in a central location, to be later reviewed by a

radiologist or software engineer to prepare the images. The collected data may be analyzed by a variety of software packages available for sale to interpret, label, quantify, and render

images in a variety of ways to make diagnosing and treating patients easier, faster, and,

therefore, less expensive.

In August 2015, the author was engaged by Magnetic Resonance Innovations Inc., a Detroit

based company in the business of MRI data processing to assist with the development of a

commercialization plan for an upcoming Small Business Innovation Research (SBIR) grant

application. MRI Inc. is developing a set of advanced software modules that have the ability

to detect and quantify white matter lesions, cerebral microbleeds, and iron, as well as

monitor and quantify blood flow and perfusion to the brain. The company is growing its

2 business from IP licensing, consulting services, data processing services, and contract research to include a direct sales model in order to bring new software modules to hospitals and imaging centers.

3 MRI Post Processing Software Industry Analysis

Advances in image processing capabilities combined with large data sets made available by a growing MRI industry have set the stage for a new industry to emerge. As a result, a large number of new players have entered the market in the last five years, many competing in the same space. Chapter three will provide an overview of the market at present, its drivers, and approximations of its size.

This chapter contains industry specific language, without detailed explanation. For anatomical and physical definitions and descriptions, please see chapter 5.

3.1 Users

After an MRI exam takes place, the data is stored on a hospital’s PACS (Picture Archiving

and Communication System). Today, radiologists view imaging data in dedicated suites full

of workstations with access to all or most of the archived data. At that point in time, the

physician or technician will prepare and view the data, and conduct any postprocessing

necessary to view the images. At the end of the process, as many as five individuals may

process data, accounting for approximately 75% of the total variable cost for a procedure1.

3.2 Customers

Two types of clinical institutions own the majority of MRI machines in the United States:

Hospitals and imaging centers. With more than 10,000 locations total, hospitals and imaging centers conduct millions of scans every year. In 2015, over 30MM exams were performed2, and at 25% of all scans, brain scans account for over 7.5MM of those exams3. Within each

exam, multiple sequences may be performed, leading to many data sets and multiple

opportunities to learn about the patient’s condition. Hundreds of images from MRI scans

and other imaging modalities may be examined in parallel, providing the viewer a

comprehensive view of the structures of interest.

3.2.1 Hospitals

The working day for an MRI machine spans 14 hours on average4,5, and can perform

thousands of scans every year. Admitted patients in critical condition who urgently need

scans take priority over patients without time-sensitive ailments. Therefore, demand for

machine time can be unpredictable, and cause delays. Improvements in healthcare

scheduling systems have drastically decreased the time between ordering an MRI and the

actual exam. Before an exam takes place, previous lab tests and medical records are

examined to ensure patient safety, insurance is checked, and the sequences must be set. As

recently as 2011, an average of 4.8 days elapsed between the ordering of an exam and its

execution. Over $120,000 was saved per site annually by reducing this time to 33 hours6.

Inefficiencies like these are constantly being eliminated by innovative companies, and patients stand to benefit greatly.

Hospital purchasing power, decision making, scheduling, and operations have been heavily influenced by the surge in healthcare information technology innovations. A recent study conducted in Germany showed that by analyzing purchasing data, hospitals as a whole can leverage existing information to have a significant impact on future decision making, contract negotiation, and budget justifications7. Hospitals, as well as pharmaceutical, medical

5 device, and insurance companies, all continuously move towards data-driven decision making as regulations increase over time.

3.2.2 Imaging Centers

Imaging centers typically have a more predictable demand for imaging services, due to their locations and general practices. Their disconnection from emergency situations typically eliminates unscheduled patients from disrupting the predictable daily flow. Purchasing decisions at imaging centers rely much more on covering expenses. A 2015 Statement of

Decision and Order from the State of Vermont’s Green Mountain Care Board regarding

Vermont Open MRI, LLC’s request to purchase a new 1.2T MRI scanner reveals each aspect of the purchase in detail. Covering expenses, repaying debts, and making a profit are the highest priority for the board, and the request is ultimately denied for lack of an up to date quote for construction costs8. Many expenses both at hospitals and imaging centers are analyzed with the same rigor.

3.3 Purchasing Considerations

Because the customer and user have different objectives within the healthcare system, a number of purchasing considerations must occur before new software or services are employed. Two primary factors are generally considered, the cost of delivering care to patients, and the clinical evidence supporting the technology.

3.3.1 Cost of Care

The cost of delivering care to patients due to two main factors influences many purchasing decisions in medical institutions: Insurance reimbursement and detailed instructions in the

Affordable Healthcare Act. Insurance reimbursement is closely tied to clinical evidence while legislation is more focused on patients’ rights.

3.3.1.1 Affordable Care Act

On March 23, 2010, Public Law 111-148 passed through Congress. More commonly known as the Patient Protection and Affordable Care Act, 111-148 provides over 900 pages of guidance for the United States healthcare industry to reform its practices.

Section 2717 titled, “Ensuring the Quality of Care,” provides a hook on which many medical device, drug, healthcare information technology, and insurance companies hang their hats.

Specifically, 2717.a.1.C provides motivation to, “…reduce medical errors through the appropriate use of best clinical practices, evidence based medicine, and health information technology…”.9

The following section provides the basis for reducing the cost of healthcare by accounting for costs related to reimbursements, “activities that improve health care quality,” and overhead. Sections 2717 and 2718 provide an incentive to entrepreneurs to create technology on all fronts to reduce the cost of care while ensuring a high quality experience for the patient.10 Hospitals have the incentive to adopt technology that appeals to patients

while complying with the law.

3.3.1.2 Reimbursement

When a medical procedure is carried out, guidelines provided by the American Medical

Association, insurance companies, and organizations like the Center for Medicare &

Medicaid Services dictate which services are considered reimbursable, as well as the rate doctors and hospitals get paid. The rate varies by state, making certain policies paramount for companies to understand while creating their business model.

One particular code of interest to MRI Inc. was established in 2005 by Public Law 109-171, better known as the Deficit Reduction Act of 2005. Section 5102(b) describes ‘imaging services’ as, “…computer assisted imaging services, including … magnetic resonance imaging….”. Under this law, the code of interest, 76377, was implemented.

The code description for 76377 covers 3D rendering with interpretation and reporting of

CT, MRI, ultrasound, or other modality with image postprocessing conducted on a separate workstation. Many intricacies are involved in the transaction between insurance companies and hospitals, such as the determination regarding where the postprocessing occurs, who is supervising it, whether it is medically relevant, and more. If the insurance company deems the procedure medically irrelevant, then the cost of postprocessing must be included in the base MRI service fee.

There are many questions concerning specific uses of postprocessing because of the rapid evolution of the image post processing industry. Insurance companies often publish statements about their codes with respect to certain policies. The AMA also publishes clarifications to aid in the billing and coding process. For example, a publication in 10/2015 delineates the ICD-10 diagnosis codes that provide justification for medical necessity for these procedures.

3.3.2 Clinical Evidence

Strong clinical evidence is needed before insurance companies and regulatory bodies to allow

reimbursement and provide certain codes for this reimbursement. Entire industries have

been upended due to regulatory decisions made by insurance companies based on clinical

evidence, or the lack thereof. For example, a great deal of literature exists regarding the insurance industry’s unwillingness to reimburse for therapies associated with peripheral vascular diseases due to the uncertainties associated with diagnosis and treatment outcomes.

Many insurance forums have long detailed discussions over the nuances of how and when to bill for certain types of procedures, under certain conditions in various locations around the country. It is evident that upon inspection, there exists a disconnect between physician practices and medical billing notation to receive reimbursement. Therefore, a strong case exists for companies to create cost saving software that is approved by the FDA before attempting to pursue a business model that assumes reimbursement.

3.4 Market Size Estimates

Approximating the size of an emerging market like MRI software for postprocessing can be

difficult. New companies are constantly emerging, pursuing different revenue models and

reimbursement strategies, and concrete adoption data is not yet available. Therefore, to gain insight into the market for healthcare IT companies that directly handle MRI data processing, the DICOM market and PACS markets will be examined closely.

3.4.1 DICOM Market

Over 100 unique applications exist for mobile devices alone (available through Apple and

Google’s app stores only). Adding to the manufacturer’s products, hyper-specialized

9 applications, and small companies, the state of the market is unclear. What is clear is that approximately 11,200 MRI scanners were in operation in 2013, and each one has some viewing software associated with it11.

The fastest growing service in the field, DICOM Grid, has raised over $40MM in funding since its founding in 2007 and has been generating revenue since 2010. DICOM Grid is a software service that communicates with existing software like EMR systems and workflows, giving the user a custom application to best suit their needs. Monthly subscription costs range from over $25,000 per month to less than $10,000, depending on the size of the installation. Currently, over 1.2BN images are stored in their cloud based system, and their track record is overall positive. They have received many awards, such as the CODiE award, recognition by KLAS Research for their top performing systems, and “Best New Radiology

Software” by Minnines12. Their success is no coincidence; lower costs to hospitals for image storage and transfer via a cloud based platform service is the direction healthcare IT companies are heading.

Using DICOM Grid as a baseline for the future of DICOM viewing services, and an approximate 12,000 hospitals and imaging centers combined13,14, the market can be safely approximated to be between $1.5BN and $5BN.

3.4.2 PACS Market

The PACS market is less saturated than the DICOM market, and, therefore, is better documented. BCC research projects the North American market for PACS systems to grow at a CAGR of 5.2% until 2019, reaching nearly $2BN. The United States makes up almost

90% of the market and is comprised of three broad categories: Radiology, cardiology, and other PACS. Radiology covers the MR applications of interest, and growing at 5.4% annually to $1.3BN by 2019, is sizeable15. PACS systems are made up of software and hardware and require regular maintenance or services. Software represents approximately one third of the cost, hardware one quarter, and maintenance and services the remainder. These systems may be integrated with Radiology Information Systems (RIS), whose market is expected to grow at an average of 7.7% annually, reaching $362MM in the US by 2019. Globally, the PACS and RIS markets are expected to reach $4BN and $750MM by 2019, respectively16.

4 Magnetic Resonance Innovations, Inc.

During the months of July to December 2015, the author had the privilege to work with

Magnetic Resonance Innovations Inc., a Detroit-based company in the business of MRI

sequence design, post-processing software, and producing a custom MRI DICOM standard

viewer. This chapter serves to review MRI Inc.’s current activities and to present a

foundation for the conclusions drawn in chapter 6.

4.1 History

Founded on November 8, 1994 in St. Louis, MO, Magnetic Resonance Innovations Inc.

sought to improve the field of clinical, academic, and industrial MRI by designing imaging

protocols, sequence design, data processing, obtaining grants, training new talent, hardware

design, and more. The original founders were E. Mark Haacke (President), Robert W. Brown

(Vice President), Weili Lin (Treasurer) and Debiao Li (Secretary). Their combined over 50

years of experience in hardware, software, and pharmaceutical companies, over 250

publications, and numerous patents provided a wealth of expertise covering most of the

MRI field17.

4.2 Team

Today, MRI Inc. employs four data processors with two data processing managers, four software developers, two administrators, and a systems analyst. Many of the data processors

are students at Wayne State University, as were many of the other full time employees.

4.2.1 Management

Today, there exist two equity owners of the company: Dr. Haacke with 99%, and Dr. Brown with the remaining 1% of 30,000 authorized shares. Dr. Haacke actively manages the day to day activities out of the Detroit office. Dr. Brown, although sitting as the Vice President, does not actively participate in operations from Cleveland, where he sits as an Institute and

Distinguished University Professor at Case Western Reserve University.

Brian Haacke has recently joined the management team as the need for investment capital developed. His experience with software sales stretches back more than 15 years in more than 10 roles in global organizations. Dr. Charbel Habib, a former student of Dr. Haacke’s, has taken responsibility as the company’s Information Management Advisor.

4.2.2 Advisors

Dr. Haacke’s network includes hundreds of clinicians and scientists stretches around the world. For practical purposes, four primary advisors comprise their informal board. John

Russell and Randall Benson, MD from the Center for Neurological Studies provide business and clinical feedback, respectively. After over 15 years as an executive at a fortune 500 manufacturing company, John provides a large organization perspective. Dr. Benson’s work as a Neurologist spans TBI, white matter tracts, and stroke. J. Joseph Hewett, MD advises from Pedes Orange County on clinical applications as well. Lastly, Edward Caner, Director of CWRU’s STEP program in Cleveland advises the company on their commercialization plan and business case.

4.3 Key Activities

There are many sources of revenue for MRI Inc. These include consulting services, data processing services, and patent licensing. In the future, potential sources of revenue will

include the sales of software services through either cloud based services or cloud

processing. Their activities all support the company’s founding principles: to further the field

of MRI research through clinical and academic advancements.

4.3.1 Research

A subset of the MRI Inc. employees work with clinical partners to conduct research. Papers

on Multiple Sclerosis, Parkinson’s, blood flow quantification, and more have been published

in a variety of journals. Dr. Haacke alone has over 21,000 citations and over 300 peer-

reviewed papers. Although their research does not translate into revenue, the studies being

conducted support their products still in development. Completing this research will provide

instrumental data to prove the clinical and business case for their advanced software

algorithms.

4.3.2 Products

A number of internal innovations have led to software products that the company has

monetized. Two of the most recent priorities, SWI and NeuSPIN, provide a foundation for

the proposed new products: advanced software modules.

4.3.2.1 SWI

Work in the late 1990s led by J.R. Reichenbach at the University of Jena set the stage for

what would later be called Susceptibility Weighted Imaging, or SWI. A paper published in

the Proceedings of the 10th Annual Meeting of ISMRM by Dr. Haacke et al. gave an early

look at the potential SWI contained. Six potential applications were presented in the paper,

including separating water and fat, veins from arteries, imaging vessel walls,

microhemorrages, iron, and improved T1 contrast.18

Research presentations and papers on SWI have accumulated a long list of prizes and awards

since 1998. Its clinical value and international recognition spurred the creation of a new

industry standard and research field that remains an essential tool in the MRI toolbox

today.19

4.3.2.2 SPIN, NeuSPIN

SPIN is an MRI DICOM data processing software. Anyone may download SPIN Lite for 90 days for free via the company’s website. It comes with a long list of basic imaging tools, including color mapping, shape annotation, and dynamic multi-view reconstructions.

Processing tools and filters like 3D rendering, scaling and offsetting, and custom mathematical operations also come standard with the software. SPIN has received accolades from hundreds of its thousands of users for its customizability, intuitive user interface, and breadth of options.

An agreement between the company and Neusoft establishes the creation of a new DICOM software, NeuSPIN. Neusoft, one of the largest healthcare imaging businesses in China, will contribute its expertise in overseas markets, as well as create an FDA cleared product to enhance the offering of SPIN Lite. The new product is a collaboration between the two companies, and will be a new source of revenue for both companies upon its completion.

4.3.3 Consulting Services

In addition to their existing software offering, MRI Inc. offers custom solutions for

companies conducting independent research or clinical trials, as well as companies

manipulating big data for a variety of applications. Clients have included large

pharmaceutical companies, imaging sites, and large coil manufacturers.

4.3.4 Patent Portfolio

Since the mid-1980s, Dr. Haacke has accumulated over 15 patents. Notably, a method for

SWI was patented first in 200320, followed by a number of sequence specific algorithms and software techniques. In total, MRI Inc. owns 13 patents with at least two pending applications. A list of patents can be found in the appendix.

4.3.4.1 Siemens License

All 13 issued patents and the rights to the pending applications have been licensed to

Siemens Healthcare GmbH. This license provides Siemens and its partners the right to use the algorithms and sequences in the software delivered to buyers, straight from the factory.

The license represents one of the company’s many revenue streams.

4.3.4.2 Pending Patents

A number of new algorithms associated with the new business model to be presented in the next section have the potential to secure a series of new patents. Additionally, the next generation of SWI technology is on the horizon, providing a new opportunity for IP generation.

4.4 New Business Model

As the physics of imaging advances and the human brain is studied more closely, the

capabilities of MRI increase accordingly. Previously, the software generated by MRI Inc. was

not FDA cleared; SPIN has no FDA clearance, and the patents licensed to Siemens refer

only to methods, not the actual software implementation. Additionally, outside of services,

the company has not created a product to be installed in either hospitals or imaging centers.

4.4.1 Advanced Modules

Five advanced modules are currently in development. The intent is to market the five modules as software packages for installation on existing workstations in hospitals and imaging centers that collect neurological data, or as a cloud processing service. Each

approach offers the same functionality, with different pricing, revenue, and sales models.

4.4.1.1 White Matter Hyperintensity

White matter lesions have been shown to correlate with a variety of neurological conditions.

Traditional white matter lesion quantification is a time consuming process, requiring the

reviewer to manually draw regions of interest for the software to evaluate. In the past,

cerebrospinal fluid (CSF) was difficult to distinguish from white matter, but the commonly

used fluid-attenuated inversion recovery (FLAIR) sequence suppresses CSF signal.

Therefore, MRI Inc. plans to take advantage of the benefits provided by FLAIR and will

develop a module to count and quantify the volume of cerebral white matter lesions

automatically, which will eventually lead to improved diagnostic capabilities.

A 2014 paper published by MRI Inc. with help from academic collaborators at three university biomedical engineering departments details a multi-step algorithm to detect, count, classify, and label WMH in MS patients. When compared with manual segmentation, the algorithm produced sufficiently similar results 96% of the time21.

With regard to competition, there exist many pseudo-automated lesion detection software packages. Image Analysis, a UK based company with a history of creating imaging algorithms for clinical trials and hospital use, has the most competitive lesion quantification software. Since 2004, Image Analysis has not received FDA clearance for any of their software. Their focus today is almost exclusively on supporting clinical research by offering digital services22. MRI Inc. plans to offer similar services, but on the opposite end of the consumer spectrum; their latest SBIR Phase IIB grant application details a plan to improve further their software and make it more accessible for adoption by any institution.

4.4.1.2 Iron Quantification

Quantifying the iron deposited in the brain is relevant to the characterization of a variety of diseases, such as Huntington’s and Parkinson’s disease, even though the fundamental mechanism describing iron’s role in the development, progression, or treatment of these conditions has yet to be determined.23

The algorithm designed to detect and quantify iron in specific regions of interest in the brain is similar to the algorithm implemented to detect and quantify white matter lesions. The study of a large group of 180 patients has provided information to refine further the automated segmentation and create a baseline for iron volume in a normal adult. Although

other research groups have replicated the science of iron quantification, no competitors

currently claim to quantify or analyze iron content. A collaboration between Stanford and

MIT researchers has led to a 2009 paper on the comparison of two methods of estimating

iron concentration with respect to age by using two different techniques, SWI and FDRI

(field-dependent R2 increase)24. No commercially available product resulted from this

research.

4.4.1.3 Cerebral Microbleed Detection

Patients suffering from dementia, stroke, and traumatic brain injury have been observed to

have cerebral microbleeds. The advanced software module detects and quantifies the

presence of microbleeds in the brain critical to the diagnosis of these diseases. Like the iron

quantification module, although other researchers have replicated these results, no other

company offers microbleed detection or quantification in this fashion.

4.4.1.4 Flow Quantification

The flow quantification module aims to increase the capacity of MRI, Inc.’s existing flow

module, adding the ability to segment automatically and interpret every vasculature geometry

possible. Monitoring the perfusion to the brain is currently done via contrast agents,

especially for patients with dementia, stroke, and TBI. A recent study including 138 MS

patients and 67 healthy patients have been analyzed using a prototype of the flow module25, the results of which can be used to verify the automated final product, ultimately producing a better overall product.

Many companies offer flow quantification services, but it is the opinion of the company that none are as feature rich or simple to use. Like other MRI Inc. software modules, common sequence data is used for processing, so the barrier for sites to adopt this software is low.

4.4.1.5 Perfusion Weighted Imaging

The perfusion weighted imaging module seeks to provide increased capabilities to study the

delivery of oxygen to the brain, which is critical for diagnosing stroke and other neurological

diseases. PWI is an established science, but, like the flow module, MRI Inc.’s software is

expected to offer a more complete and useful set of metrics for clinicians to diagnose

patients. Other companies claim to be able to do the same quantification but lack the clinical

experience that MRI Inc. has backing its product.

4.4.2 Value Proposition Analysis

Curtis Carlson and William Wilmot published Innovation: The Five Disciplines for Creating What

Customers Want in 2006, where they present NABC, a framework for analyzing a company’s

‘innovation opportunity’.26 To start, an important customer need has to be established. A

unique approach with quantitative customer benefits should be determined. Finally, if the competitive landscape is favorable, a compelling and quantitative value proposition may be turned into a full-fledged business plan.27

The following analysis will be conducted as if the five advanced modules are completed according to the goals set forth in their SBIR Phase IIB proposal submitted for the period starting 4-1-2016 and ending 3-31-2017. The application will be made public pending the

awarding of the grant.

4.4.2.1 Need

Many of the topics addressed above constitute a new field of study for neurology, anatomy, and physiology. The role that iron plays in the brain, how white matter lesions predict decay due to degenerative diseases, or what threshold cerebral microbleeds must cross to be considered useful for diagnosis are unknown. MRI Inc. and its global network of academicians and clinicians continue to search for correlation and causation, but strong evidence does not yet exist.

The obvious support from clinical partners combined with the track record of MRI Inc. suggests that although the need is emerging, it will soon be well established. With respect to building a business case, however, strong clinical evidence for the majority of the advanced modules would need to be present in order to classify the need as strong as well. Considering that every sales strategy relies on either a purchasing decision to be made at a hospital, or an insurance company’s decision to reimburse, strong clinical data is essential to substantiate the need outside of the academic circle where the products are developed.

4.4.2.2 Approach

The approach of the company to create the most advanced software on the market without a strong clinical need would normally indicate that the approach was also weak. In the case of

MRI Inc., such a strong focus is placed on the data driven quantitative approach that the value will be apparent, as soon as the clinical evidence is there to back it. Additionally, generating new algorithm and software based patents would improve the quality of their approach.

4.4.2.3 Benefits per Cost

The benefits per cost of each software module are most easily examined in terms of the time

savings to hospitals and physicians. On average, each day spent in a hospital bed costs

approximately $2,00028. MRI Inc. claims to be able to process data that previously took two

hours in less than 30 minutes. Processing that data could be done by neurologists, medical

computer scientists, or radiologists. With an average salary of approximately $86/hour29, each analysis conducted using advanced techniques saves hospitals approximately $65 in human costs and $125 of bed time costs.

The benefits of freeing up time for clinicians and a smaller window between scan and analysis is obvious for the patient. Their results come in quicker, and the probability of suffering one of any number of hospital acquired illnesses decreases dramatically.

4.4.2.4 Competition

MRI Inc. has worked alongside many of the top companies, clinicians, and researchers in the

MRI software industry. Dr. Haacke also has good relationships with the executives at the closest competing companies, who share experiences, approaches, failures, and successes.

Therefore, a great deal of the guesswork in creating their new business model was removed by studying others’ experiences.

Examining their actual products closely, it is obvious why MRI Inc. has produced such an overwhelming volume of support. Competitors’ software requires a great deal of users’ input to initialize the program, from parameters to patient information and more. Then, the user

must also define regions of interest, and even outline by hand the specific structures to be

analyzed.

In terms of direct functionality, the cerebral microbleed, white matter hyperintensity, and

iron quantification modules have the least competition. The flow and perfusion modules are

slightly older concepts, and, therefore, have mild competition.

4.4.3 Go to Market Strategy

After an extensive investigation into the closest competitors, including many discussions with chief officer level managers, the following strategies have been developed. A breakeven

analysis for each sales strategy may be found in the appendix, along with different pricing

models. Pricing the software requires analyzing multiple revenue models. The software may

be offered either via a case by case service, or a package purchase. Each strategy has its pros

and cons, making selecting a single path difficult. Considering the criteria hospitals and imaging centers use while making purchasing decisions suggests that the following strategies may benefit one institution over another.

Moving forward, the company will likely test both strategies, giving flexibility to the sales projections and de-risking investment in both the company and their products.

4.4.3.1 Package Sales

Once installed on a clinic’s PACS, the software will be used by radiologists and technicians to process data within the hospital. The benefits of an internal installation include a lower

risk of confidentiality breaches between MRI Inc. and the client, faster processing, less

bandwidth to transfer images, and a better reimbursement position.

4.4.3.1.1 Target Customers

Customers with a high volume of cases generally include specialized imaging centers and hospitals attached to medical schools. There exist over 140 accredited medical schools, and over 4500 imaging centers30. Imaging centers can be further broken down into chains, of

which there are over 800. The 20 largest chains represent over 20% of all centers, the majority located in California31.

4.4.3.1.2 Pricing

The price per package has been set at $50,000 for modeling purposes. Based on the price of

MRI hardware, competitor’s software offerings, and hospital budgets, it has been

hypothesized that $50,000 will be insignificant compared to the cost of a PACS system, or

updated radiological viewing stations. Once installed on the viewing stations, the variable

cost per exam only costs the hospital the time it takes a technician to run the software, and

the electricity to execute the program. Compared to the variable cost per unit time using the

MRI hardware, running processing software does not impact the hospital. An additional

maintenance cost of $5,000 per year that includes service and upgrades guarantees some

recurring revenue at each site.

4.4.3.2 Cloud Processing

Without a local installation, cloud processing has fewer barriers for setup than traditional

software packages. Maintenance costs, upgrades, computation, and troubleshooting all occur

outside of hospitals, preventing system downtime or use outside of the current workload. At

the same time, the risk of confidentiality increases, as does the need for increased data

transfer and internet access.

4.4.3.2.1 Target Customers

The cost to set up a cloud service is negligible, eliminating the barriers for lower volume imaging centers and smaller hospitals to benefit from advanced software processing. It is

predicted that most of the imaging centers, regardless of specialization, would be more open

to a pay-per-use model, considering the strict guidelines regarding large capital expenditures,

debt, and so on. Smaller hospitals with a low volume of patients or without large budgets for

new equipment or packages also stand to benefit from this model.

4.4.3.2.2 Pricing

Pricing for cloud services would likely follow reimbursement rates. For the purpose of the

financial models, smaller institutions are expected to order approximately two reports per

day, at a rate of $50 per report, or approximately $36,500 yearly. If insurance companies

reimburse an average of $65 per patient, the imaging center or hospital can expect to take

$15, or charge the patient a higher rate to increase their own income to cover their costs.

Since the reimbursement rates for full exams and postprocessing are separate, a breakeven

analysis was conducted at different pricing levels. Eleven small northeastern Ohio imaging

centers were surveyed to gauge the average rate of patients that required head and brain

scans. The breakeven analysis was then rated on a scale of very likely, moderately likely, or

unlikely to breakeven based on the packaging cost. These ratings were the foundation to

25 characterizing and distributing the hospitals and imaging centers into groups for market size approximation.

4.4.4 Cost Structure

Today, the activities of MRI Inc. are primarily software based, so operating costs closely follow salaries. Moving forward, a more extensive software package offering has additional costs including fees imposed by the FDA, a sales staff, and customer support staff. For more details on the following staffing needs, pro forma income statements, and case scenarios, see the appendix.

4.4.4.1 Staffing Needs

In the three financial cases presented, a summary of staffing needs was developed. In any sales strategy, similar personnel are essential to the growth and management of the company.

Hiring a full time, industry-experienced CEO will be essential to bringing other personnel on board. Customer support and administration staff to support the CEO and sales efforts scale according to the number of salesmen and accounts.

As the number of accounts increase, more R&D personnel may be supported by the company, including principal investigators and research programmers. Constant needs for systems architects, software developers, and IT support will exist at all sales levels. The likely case for staffing needs is presented below in section 4.4.5.2.

4.4.4.2 FDA Costs

For the first time in the company’s history, the proposed new software offering will be submitted to the FDA for approval. Both NeuSPIN and the advanced modules need the approval of the FDA before a strong clinical case can be presented to justify a large purchase or ongoing subscription at most institutions. To account for filing costs, hiring consultants to assist with the preparations, and maintenance fees, an annual $50,000 expense was allocated in each scenario.

Because other companies have submitted software to the FDA for approval in the past, and post processing software does not present a significant risk to patients, a low cost 510(k) pathway can be followed. For a small business, the cost of an application is $2,614. The standard fee for companies without a small business determination (SBD) is $5,22832.

Therefore, the $50,000 expense more than covers application and consulting costs related to obtaining approvals.

4.4.4.3 Other Expenses

Other negligible expenses from these analyses include capital expenditures by employees, office expenses, legal, and marketing activities. In each scenario, the total cost of wages and benefits outweighs the total cost of all other expenses.

4.4.5 Pro-Forma Financial Models

In order to assess the immediate capital needs of the company, a very detailed financial model was prepared by Brian Haacke, and edited by the author. Three cases were created in an attempt to model the worst, likely, and best scenarios. The prevailing assumption was that

27 market penetration was directly proportional to the number of sales staff, as well as the ability to reach hospitals and imaging centers.

4.4.5.1 Worst Case

As a proof of concept for the package based sales model alone, a worst case scenario was designed. Only six salesmen that worked exclusively with hospitals were assumed, each adding only one account per month. At this operating level, breakeven was achieved after year three, and only $2MM was needed to finance the company’s growth. The salary expense exceeded 50% of all costs for the company, and by year five, the company’s operating margin only reached 11%.

Table 1 – Worst Case financial summary

4.4.5.2 Likely Case

The most likely case was decided to be a hybrid sales model. A modest but reasonable sales staff of 8 imaging center representatives and 12 hospital representatives are expected to generate similar rates of sales, closing approximately only one site per month. Each imaging center revenue estimates are based on roughly two scans per day, at a rate of $50 per scan.

Using these assumptions, $1.4MM in financing is needed in the first year, and the company breaks even in year two. By year five, recurring revenues from imaging centers push the operating margin to 60% and over $10MM in net income.

Table 2 – Likely Case financial summary

4.4.5.3 Best Case

In the best case scenario, the size of the sales team is significantly increased to 36 in year two and on to 97 in year five. This results in huge revenues of over $100MM in year five, with a margin of nearly 70%. The investment needed is also $1.4MM in year one, and breakeven occurs in year two.

Table 3 – Best Case financial summary

4.4.6 Capital Needs

In each financial scenario, $2MM or less is the required investment to fund hiring and expansion needs. A combination of private and public funding will be used to satisfy this requirement, in approximately equal parts.

4.4.6.1 NIH Grants

Most recently ending in January 2015, MRI Inc. has been awarded a phase one and two

STTR grant from the NIH/NHLBI with Wayne State University in Detroit totaling approximately $1.1MM (Award reference number R42 HL112580). The grant sponsored the creation of SPIN Lite, up to but not including applications to the FDA for approval. To date, SPIN Lite has not been submitted for approval.

In 3Q15, an SBIR Phase IIB grant application was filed with the NIH totaling $3MM regarding RFA-HL-16-009, a bridge funding award to accelerate commercial development.

The term of this award stretches into 2019, to support the development of all five modules and their approvals.

4.4.6.2 Private Sources

If the NIH applications were unsuccessful, the next step for MRI Inc. is to pursue private sources of professional funding. Because the investment needed to execute the most ambitious staffing plan did not exceed $3MM, it is likely that a moderately sized Angel group would provide the appropriate quantity and type of funding rounds needed to scale quickly.

4.4.7 Development Timeline

The proposed duration of product releases from the SBIR application indicated that development of the advanced modules would stretch into 2019, resulting in the publication of approximately one module per year.

4.4.8 Traction

Currently, the advanced modules are in development, and formal sales efforts have not yet commenced. In anticipation of the products, many letters of support were submitted to the

NIH along with the recent SBIR application from Pedes Orange County, the Center for

Neurological Studies, Abbvie, CorTechs Labs, ImageIQ, Wayne State University’s

Department of Computer Science, Wayne State University School of Medicine, and the

Detroit Receiving Hospital. These supporters represent a small fraction of MRI Inc.’s clinical partners, collaborating universities, and collaborating private companies around the world.

5 Medical Background

To understand fully the concepts discussed in earlier chapters, the following chapter provides a primer including the biology, physics, and engineering that goes into magnetic resonance imaging.

5.1 Brain Biology

The average human brain weighs approximately 1.2 kilograms, consisting primarily of water, lipids, and proteins with smaller quantities of carbohydrates, salts, metals, and other organic substances33. Knowing the chemical properties of each structure enables clinicians and scientists to explore the body’s most complex organ non-invasively.

5.1.1 Fixed Anatomical Structures

A significant fraction of the brain is static on the macro level. The brain structure components described below serve to shed light on the motivation behind the work of MR

Innovations Inc.

5.1.1.1 Iron

Two types of iron may be found in the body; heme iron, and non-heme iron. Heme iron comes exclusively from the consumption of meat and non-heme iron from both meat and plants. About 57% of the iron in meat is non-heme. The body’s ability to metabolize iron dictates how much is stored in the body, and can vary by as much as a factor of four in rate from person to person. Diet modifications have the ability to regulate iron absorption, as well as a number of medications.34

A 2005 publication by Dr. Haacke set the stage for the imaging and then quantification of

iron buildup in the brain. Since that time, a strong correlation between iron buildup and degenerative neurological disorders has been shown by many research groups35. A 2009

study by Dr. Haacke characterized iron deposits in MS patients using MRI, further

motivating the product of MR Innovations36.

5.1.1.2 White & Grey Matter

Neuron cell bodies originate in the grey matter, found throughout the brain. White matter

found in deep brain tissue contains the axons.37 Many clinical studies measuring white matter

hyperintensities have found that their presence is linked to multiple sclerosis and aging

related degenerative diseases38.

5.1.2 Dynamic Anatomical Structures

Proper brain function requires a constant circulation of blood and cerebrospinal fluid, in

roughly equal parts. Approximately 300ml of fluid constantly refreshes our brains, which

consumes approximately 60% of our energy at rest.39 Poor fluid ratios, volumes, or

circulation lead to significant neurological disease.

5.1.2.1 Blood

The blood in our brain completes many of the same functions as the blood that flows

throughout the rest of the body: It delivers nutrients and oxygen to the brain tissue, regulates

the pressure in the skull cavity, and removes toxins and waste. The brain also requires a very

precise volume of fluids at all times. Blood flows in and out of the brain at approximately

630 ml/min and reaches flow speeds of 80cm/sec.40 Imaging the blood in the brain often

leads to a variety of diagnoses. Compromised flow may indicate stroke in some patients; in

others, early signs of MS may present themselves. Studies of the flow rates, volumes,

patterns, and pressures continue to provide scientists with valuable data to allow doctors to

make better diagnoses.

5.1.2.2 Cerebrospinal Fluid

Cerebrospinal fluid, or CSF, forms in and is secreted by epithelial cells in the brain. It

circulates in a well-defined way, from the lateral ventricles around the brain and down into

the rest of the central nervous system. CSF flows at a much slower rate than blood; flow out

of the brain often falls below 10% of the rate of blood. Therefore, it gains the ability to be

seen using specially designed MR sequences and creates a valuable measure for neurologists

to study its properties with respect to anatomy and physiology, as well as its role in neurological diseases.41

5.2 Magnetic Resonance Imaging

Since the first papers published concerning the possibility of two- and three-dimensional

nuclear magnetic resonance images, the medical field has slowly adopted the technology as a diagnostic and research tool. Its description in two papers published in 1973 by Paul

Lauterbur and Peter Mansfield led to their 2003 Nobel Prize in Physiology or Medicine.42

5.2.1 Physics

In July 1946, Felix Bloch submitted a paper titled, “Nuclear Inductance” to Physical Review.

His presentation of what would later be called the Bloch equations earned him a Nobel Prize

in Physics, shared with Edward Purcell.

5.2.1.1 Basic Principles

Collecting data from organic tissue based on its magnetic resonance properties can be

described by a few basic principles.

5.2.1.1.1 Spin

In the human body, a large collection of nuclear spins, each of which give rise to a magnetic

dipole moment, results in a net magnetization (magnetic dipole moment density). This

overall magnetization can be readily manipulated by externally applied magnetic fields, and

read by detector coils.

Once the external fields have been applied in a predetermined sequence, the protons begin

to precess (a classical picture of the proton motion is accurate for MRI in which we are really considering large numbers of molecules) at a predictable frequency that is proportional to

the strength of the applied magnetic field, called the Larmor frequency. As mentioned, this motion was first described by Felix Bloch, in the form of three Bloch equations. By using these equations, scientists develop specifications of the necessary hardware components to read the spins, and eventually produce images.

5.2.1.1.2 Relaxation

Nuclear spins, when aligned with (longitudinally) an external magnetic field, create a different signal than when they are perpendicular (transverse) to the external field. When the spins become oriented perpendicularly, the signal is lost. Therefore, the spins are said to

‘relax.' The laws of motion dictate that some force must cause the spins to relax. In this case,

two electromagnetic forces are at play. The interaction between spins that causes ‘dephasing’

(loss of coherent motion) is called T2 decay. The interaction between spins as their

longitudinal component grows back along the external field is called T1 decay. These two

signature times dictate the signal that can be read from a specific region at a specific time.

5.2.1.1.3 Phase

When many spins are aligned by a series of pulses from the main and RF coils, they are said to be ‘in-phase.' Phase is measured as the spread of spins from a reference point, as an angle.

When spins are in-phase, their spread is small, close to 0 degrees. When spins are out of phase, the signal is weak, and may be spread across the transverse plane.

Figure 1 – In phase bulk magnetization, after an initial 90-degree RF pulse.

Figure 2 – Dephased spins

5.2.1.1.4 Acquisition Time

Typical acquisition times for MRI exams last between 15 minutes and 2 hours.43 During that

time period, it is important that the patient remains still in order to reduce the number of

artifacts that otherwise need to be corrected during the image reconstruction. For example,

every time the patient breathes, a spatial displacement of the chest or head adds information

to a slice of the image that was previously not present. Complex algorithms compensate for these additions (and subtractions), producing useful images that radiologists can interpret.

The acquisition time may be shortened by sacrificing resolution, image size, the strength of

the magnetic field used, and many other variables. Clever sequences designed to waste less

time improve the efficiency of data collection, and reduce exam time.

Other modalities such as CT and X-Ray create similar images in a shorter time frame, but

cannot do what any of the MRI Inc. products propose to do; a comparison of capabilities is

outside the scope of this study.

5.2.1.2 Advanced Principles

Since the first implementation of MRI in the 1970s, collaboration between physicists and

clinicians produced a wide variety of techniques to push the fundamental boundaries that

limit acquisition time and image quality.

5.2.1.2.1 Susceptibility

Variations in properties of adjacent materials leads to phase variations when examined by traditional MRI techniques. The science of these variations has been refined to the point that

susceptibility has become an additional form of contrast that is different from T1, T2, T2*,

or other existing contrast. Susceptibility Weighted Imaging, or SWI, is used clinically to

visualize vasculature, iron, white and grey matter, and more.

5.2.1.2.2 Sequence Design

In order to decrease collection time, eliminate or increase the signal from particular tissue types, decrease echo time, and achieve clinically valuable data in a practical time frame, sequences are designed with precise parameters and sometimes in counterintuitive order.

As examples of sequence diagrams, a single spin echo and a repeated spin echo experiment are shown below. In the first diagram, a single line of data is collected per rf-pulse. The second diagram shows a repeated spin echo, where many lines of data are collected in the same time period. Over time, the spin relaxation times dictate a loss in signal for each tissue type, represented by the exponentially decaying signal function below the sequence diagram.

Figure 3 – Single Spin Echo at t = 2. The laboratory signal profile is limited by T2 effects, while the signal itself is dependent on the T2* effects, and re-phase at the echo.

Figure 4 – Like the single spin echo experiment, a signature echo time dictates when the signal profile recovers due to re-phasing. Additional RF- pulses increase the number of points that can be collected per sequence repetition.

Developments like these have made MRI a competitive imaging modality and guarantee its

clinical use for decades to come.

5.2.2 Hardware Components

At the time of sale, most MRI units cost over $200,000. Cost varies by main magnet field

strength, with higher strength systems costing at least $1,000,000 in most cases44. Systems to store and recondense liquid helium or nitrogen, many kilometers (main magnet coils) of superconducting cables, electronic controls, monitors, and safety features take up entire

suites in radiology departments at hospitals. The highlighted components are necessary to

understand the way machines are priced and how the size dictates function.

5.2.2.1 Main Magnet

The main magnetic coil produces a strong, spatially constant magnetic field oriented along

the patient from head to toe. The signal to noise ratio is proportional to the strength of the

main magnet, making stronger magnets more desirable. Modern magnets consist of many

solenoidal bundles of superconducting wire spaced with their symmetry axes along the

length of the patient to ensure a uniform field with minimal fringe and edge effects.

5.2.2.2 RF Coil

Radio frequency (RF) coils serve many purposes. The RF coil may transmit an oscillating signal, receive a signal from the sample, or act as both a transmitter and receiver. Receiving a

signal from a sample, in a typical MRI case, the hydrogen in the human body, requires

measuring an oscillating magnetization by monitoring induced currents in the coil due to an

oscillating flux.

5.2.2.3 Gradient Coil

Applying gradients to a patient spatially encodes the spin information that the MRI reads.

The magnetic field decreases in strength in one direction. Turning the coils on and off while

collecting data equates to collecting lines of data in k-space (frequency). This data is then

converted to three-dimensional space, and can be interpreted by radiologists.

5.2.3 Software Market Segmentation

The software market can be segmented and characterized by function. Three primary pieces

of software come into contact with imaging data: DICOM viewers, PACS, and image

processing software (Note to reader: acronyms are explained below).

5.2.3.1 System Operation

After exams are ordered by physicians and executed by technologists, the data are stored on

a PACS system accessible through a DICOM viewing software station. The data are then

41 verified by either computer scientists or technicians, and archived for the review by radiologists or neurologists. Images may be transmitted through electronic health records, central PACS, or via raw files.

Obtain Imaging DICOM Quality DICOM Viewing Archiving (PACS) Transmission of Images Data Conversion Assurance Stations

Figure 5 – Flow of information throughout the MRI process

5.2.3.2 Image Processing

Image processing includes any activities related to interpreting the image post-acquisition, between archiving and the time the final diagnostician makes a judgment. Neurologists, radiologists, computer scientists, and machine technicians may all be involved in this process. Today, many images are processed by hand, which includes a laborious process of manually highlighting a region of interest, and applying a set of filters and tools at dedicated viewing stations.

5.2.3.3 DICOM Viewers

Digital Imaging and Communications in Medicine, or DICOM, is an international standard that governs formatting for multiple medical imaging modalities.45 Accessing data via

DICOM viewing software allows physicians to manipulate images using a variety of tools.

Many viewers with unique applications exist and are constantly being upgraded, improved, and customized to physician’s specifications.

5.2.3.4 PACS Systems

Picture Archiving and Communication Systems, or PACS, store most or all of a hospital’s

imaging data from multiple modalities including CT, MR, X-Rays, and PET. The PACS communicates with a hospital’s electronic medical records (EMR), transmits data between medical facilities, and in some cases interprets data. Network security to protect confidential patient information is a primary concern for hospital IT departments, leading to heavily encrypted files and extensive storage protocols.46

6 Conclusions

The following conclusions represent the author’s opinions only.

6.1 Industry Direction

The reimbursement pathway for a product or service is simultaneously one of the most important determinants of a company’s business case while being the aspect that is in the most constant state of flux. Insurance companies regularly change their policies on whether or not to reimburse for certain procedures, based on new clinical evidence, or the failure of existing therapies. This area is the biggest risk for MRI Inc. Until a notable instance of reimbursement occurs, not even the most conservative financial model imaginable that relies on pay-per-use can be accepted by an investor.

6.2 MRI Inc.

Each document generated by the company, regardless of purpose, highlights the achievements of Dr. Haacke extensively. This is not done without good reason; Dr. Haacke’s name is well known in the MRI academic world due to his work, especially concerning SWI.

A company whose primary activities are consulting, research, and licensing a patent portfolio should revolve around such an individual’s talent. Recognizing the differences and challenges presented by pivoting to a direct sales model leads to the following barriers to success, quality recruitment, and accurate timelines.

6.2.1 Recruitment

Many of the key business activities such as fundraising, sales, and FDA clearances rely on

having industry-experienced personnel on the team. To maintain the timeline proposed, Dr.

Haacke will have to go beyond the traditional recruitment channels of local institutions like

Wayne State University and personal relationships to obtain the best talent possible.

Executing the proposed business model is radically different from any activities the company has pursued in the past 22 years. The new business plan positions MRI Inc. essentially as a startup company, raising its first professional funding while attempting to make its first product and sale in a new industry.

6.2.2 Timeline

The original timeline proposed by MRI Inc. requires investment in year one, spurring a steady release of new software modules each year. I do not believe that the proposed timeline is reasonable. Until formal recruitment for a full time CEO and executive team begins, the company is not properly staffed to scale at the proposed rate.

6.2.3 Overall

I do not believe that the approach being taken is representative of a company whose stage is essentially at the startup level. In order for their business plan to be carried out successfully,

MRI Inc. must move away from having their lead scientist serve as CEO. Large organizations with thousands of employees benefit from scientifically literate CEOs, but this is not necessarily sustainable for a startup-stage company47.

At the 2016 Healthcare Information and Management Systems Society (HIMSS) conference

in Las Vegas, government representatives from CMS emphasized the importance of adding

value to (or reducing costs in) the healthcare system while building healthcare technologies,

and to shift the current focus from process and collection to concrete propositions to

improve the patient’s experience. In her closing remarks on the conference, Executive Vice

President of HIMSS Carla Smith said that double digit increases in executive attendance to

the conference was likely due to the fact that the industry is changing so rapidly that even the

most experienced industry experts are constantly learning more about their complex

environment.

Considering Dr. Haacke’s past success, the company’s history, and level of support for the

products in development, I believe that the business plan will be successfully executed but

only at a fraction of the proposed speed.

7 Appendices

7.1 Patents owned by MRI Inc.

7.1.1 Issued Patents

Application-Specific Optimization of Echo Time in MR Pulse Sequences for Investigating Materials with Susceptibilities Different from that of the Background in which they are Embedded (USA) Patent #: US 6,501,272 B1 Date of Patent: December 31, 2002 Inventor(s): E. Mark Haacke, Juergen Reichenbach, Yi Wang Licensed: Siemens Healthcare GmbH

Method of MRI Image Reconstruction from Partially Acquired data in Two or More Dimensions Using a Multidimensional Inverse Transform Technique (USA) Patent #: US 6,560,353 B1 Date of Patent: May 6, 2003 Inventor(s): E. Mark Haacke and Yingbiao Xu Licensed: Siemens Healthcare GmbH

Susceptibility Weighted Imaging (USA) Patent #: US 6,658,280 B1 Date of Patent: December 2, 2003 Inventor(s): E. Mark Haacke Licensed: Siemens Healthcare GmbH

Iterative Method for Correction of Geometric Distortion resulting from Phase Evolution during Segmented Echo Planar Nuclear Magnetic Resonance Imaging and Apparatus therefore (USA) Patent #: US 7,154,269 B1 Date of Patent: December 26, 2006 Inventor(s): Yingbiao Xu and E. Mark Haacke Licensed: Siemens Healthcare GmbH

Complex Threshold Method for Reducing Noise in Nuclear Magnetic Resonance Images (USA) Patent #: US 7,573,265 B2 Date of Patent: August 11, 2009 Inventor(s): E. Mark Haacke Licensed: Siemens Healthcare GmbH

Method of and Software Application for Quantifying Objects in Magnetic Resonance Images via Multiple Complex Summations (USA) Patent #: US 7,692,424 B2 Date of Patent: April 6, 2010 Inventor(s): Yu-Chung Norman Cheng, Ching-Yi Hsieh and E. Mark Haacke Licensed: Siemens Healthcare GmbH

Geometry Based Field Prediction Method for Susceptibility Mapping and Phase Artifact Removal (USA) Patent #: US 7,782,051 B2; Date of Patent: August 24, 2010 Inventor(s): E. Mark Haacke, Jaladhar Neelavalli and Yu-Chung Norman Cheng Licensed: Siemens Healthcare GmbH

Susceptibility Weighted Imaging (CHINA) Patent #: Chinese Patent No. ZL03810532.2 Patent #: Chinese Patent No. ZL200810094962.2 (Div of CN ZL03810532.2) Patent #: Chinese Patent No. ZL200810094961.8 (Div of CN ZL03810532.2) Date of Patent: October 24, 2012 Inventor(s): E. Mark Haacke Licensed: Siemens Healthcare GmbH

A Method of Generating Nuclear Magnetic Resonance Images Using Susceptibility Weighted Imaging and Susceptibility Mapping (SWIM) (USA) Patent #: US 8,422,756 B2 Date of Patent: April 16, 2013 Inventor(s): E. Mark Haacke and Jaladhar Neelavalli Licensed: Siemens Healthcare GmbH

A Method of Generating Nuclear Magnetic Resonance Images Using Susceptibility Weighted Imaging and Susceptibility Mapping (SWIM) – Provisional (USA) Patent #: US 8,693,761 B2 Date of Patent: April 8, 2014 Inventor(s): E. Mark Haacke and Jaladhar Neelavalli Licensed: Siemens Healthcare GmbH

Tissue Similarity Maps (TSM): A Method of Improving Contrast in Dynamic Contrast Enhanced Imaging (USA) Patent #: US 9,008,396 B2 Date of Patent: April 14, 2015 Inventor(s): E. Mark Haacke Licensed: Siemens Healthcare GmbH

A Method of Phase Unwrapping from Multi-Echo Gradient Data: Catalytic Multi-echo Phase Unwrapping Scheme (CAMPUS) (USA) Patent #: US 9,097,782 B2 Filing Date: May 1, 2012 Date of Patent: August 4,2015 Inventor(s): E. Mark Haacke and Wei Feng Licensed: Siemens Healthcare GmbH

A Method of Generating Nuclear Magnetic Resonance Images Using Susceptibility Weighted Imaging and Susceptibility Mapping (SWIM) (CHINA) Patent #: Chinese Patent No. ZL201180029050.7 Date of Patent: October 14, 2015 Inventor(s): E. Mark Haacke and Jaladhar Neelavalli Licensed: Siemens Healthcare GmbH

7.1.2 Pending

A Method of Phase Unwrapping from Multi-Echo Gradient Data: Catalytic Multi-echo Phase Unwrapping Scheme (CAMPUS) (CHINA) PCT International Application No.: PCT/US2012/035989 Filing Date: PENDING Inventor(s): E. Mark Haacke and Wei Feng Licensed: Siemens Healthcare GmbH

Tissue Similarity Maps (TSM): A Method of Improving Contrast in Dynamic Contrast Enhanced Imaging (CHINA) PCT International Application No.: PCT/US2012/035569 Chinese Patent Application No: 201280031494.9 Filing Date: PENDING Inventor(s): E. Mark Haacke Licensed: Siemens Healthcare GmbH

7.2 Breakeven Calculations

End Notes

1 Christopher Hancock et al., “Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain,” Open Journal of Radiology 04, no. 03 (2014): 260–69, doi:10.4236/ojrad.2014.43034. 2 Peter G. Peterson Foundation, “The U.S. Performs a High Number of MRI Exams Compared to Other Countries,” Peter G. Peterson Foundation, August 16, 2015, http://www.pgpf.org/chart-archive/0052_MRI- exams. 3 “What Is the Organ Distribution of MRI Studies?,” Magnetic Resonance, 2016, http://www.magnetic- resonance.org/ch/21-01.html. 4 State of Tennessee, “Magnetic Resonance Imaging” (Health Services and Development Agency, 2014). 5 Hancock et al., “Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain.” 6 “Scheduling an MRI - ASAP,” (Malcom Baldrige National Quality Award Recipient, 2011), https://www.rsna.org/uploadedFiles/RSNA/Content/Science/Quality/Storyboards/2013/Wessman- QSE1056TUA.pdf. 7 D. Carlow et al., “{PMD139} - Using Real-World Hospital Purchasing And Consumption Data To Improve Healthcare Systems Efficiencies,” Value in Health 18, no. 7 (2015): A369 – , doi:http://dx.doi.org/10.1016/j.jval.2015.09.741. 8 State of Vermont Green Mountain Care Board, “Statement of Decision and Order,” July 27, 2015. 9 Charles Rangel, “Text - H.R.3590 - 111th Congress (2009-2010): Patient Protection and Affordable Care Act,” legislation, (March 23, 2010), https://www.congress.gov/bill/111th-congress/house-bill/3590/text. 10 Ibid. 11 OECD Data, “Health Equipment - Magnetic Resonance Imaging (MRI) Units,” accessed March 5, 2016, https://data.oecd.org/healtheqt/magnetic-resonance-imaging-mri-units.htm. 12 “DICOM Grid Competitors and Products in the Healthcare IT Marketplace,” accessed March 6, 2016, http://www.health-care-it.com/company/622598/dicom-grid. 13 “Imaging-Center Growth Hits the Wall in 2013; Volumes Plummeted in 2011 | Radiology Business,” accessed March 8, 2016, http://www.radiologybusiness.com/topics/business/imaging-center-growth-hits-wall- 2013-volumes-plummeted-2011. 14 American Hospital Association, “Fast Facts on US Hospitals,” AHA, January 2016, http://www.aha.org/research/rc/stat-studies/fast-facts.shtml. 15 Peggy Lehr, Medical Magnetic Resonance Imaging (MRI): Technologies and Global Markets (BCC Research, 2013). 16 Simone Peters, Research Storage, Transfer and Analysis of Medical Images: Global Markets (BCC Research, 2015). 17 “History,” MR Innovations, April 10, 2001, https://web.archive.org/web/20010410205602/http://mrinnovations.com/. 18 E. Mark Haacke et al., “Susceptibility Weighted Imaging (SWI): A New Means to Enhance Image Contrast,” in Proceedings of the 10th Annual Meeting of ISMRM, Honolulu, Hawaii, 2002, 297, http://cds.ismrm.org/ismrm- 2002/PDF1/0297.PDF. 19 Magnetic Resonance Imaging Institute for Biomedical Research, “Susceptibility Weighted Imaging (SWI) Brochure,” 2006. 20 E. Mark Haacke, Susceptibility weighted imaging, US6658280 B1, filed May 10, 2002, and issued December 2, 2003, http://www.google.com/patents/US6658280. 21 Yi Zhong et al., “Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR,” International Journal of Biomedical Imaging 2014 (2014), doi:10.1155/2014/239123. 22 “What We Do « Image Analysis,” accessed March 13, 2016, http://www.imageanalysis.org.uk/what-we-do. 23 E. Mark Haacke, “Characterizing Iron Deposition in Multiple Sclerosis Lesions Using Susceptibility Weighted Imaging,” PubMed, NCBI, 2009, http://www.ncbi.nlm.nih.gov/pubmed/19243035. 24 Adolf Pfefferbaum et al., “MRI Estimates of Brain Iron Concentration in Normal Aging: Comparison of Field-Dependent (FDRI) and Phase (SWI) Methods,” NeuroImage 47, no. 2 (August 15, 2009): 493–500, doi:10.1016/j.neuroimage.2009.05.006. 25 Sean K. Sethi et al., “Jugular Venous Flow Abnormalities in Multiple Sclerosis Patients Compared to Normal Controls,” Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 25, no. 4 (August 2015): 600–607, doi:10.1111/jon.12183.

26 Curtis R Carlson and William W Wilmot, Innovation the Five Disciplines for Creating What Customers Want (New York: Crown Business, 2013), http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=723806. 27 SRI International, “SRI’s ‘NABC’ Approach,” 2006, http://protomo.fi/sites/protomo.fi/files/public/attachments/events/nabc.pdf. 28 Emily Rappleye, “Average Cost per Inpatient Day across 50 States,” Becker’s Hospital Review, accessed March 6, 2016, http://www.beckershospitalreview.com/finance/average-cost-per-inpatient-day-across-50-states.html. 29 Hancock et al., “Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain.” 30 “AAMC Organization Directory,” AAMC, 2016, https://members.aamc.org/eweb/DynamicPage.aspx?site=AAMC&webcode=AAMCOrgSearchResult&orgty pe=Medical%20School. 31 “Imaging-Center Growth Hits the Wall in 2013; Volumes Plummeted in 2011 | Radiology Business.” 32 “Medical Device User Fee Amendments (MDUFA) > FY2016 MDUFA User Fees,” accessed March 4, 2016, http://www.fda.gov/ForIndustry/UserFees/MedicalDeviceUserFee/ucm452519.htm. 33 P. Hartmann, “Normal Weight of the Brain in Adults in Relation to Age, Sex, Body Height and Weight,” PubMed, NCBI, June 15, 1994, http://www.ncbi.nlm.nih.gov/pubmed/8072950. 34 Iron Disorders Institute, “Iron We Consume,” 2009, http://www.irondisorders.org/iron-we-consume/. 35 E. Mark Haacke, “Imaging Iron Stores in the Brain Using Magnetic Resonance Imaging,” PubMed, NCBI, 2005, http://www.ncbi.nlm.nih.gov/pubmed/15733784. 36 Haacke, “Characterizing Iron Deposition in Multiple Sclerosis Lesions Using Susceptibility Weighted Imaging.” 37 National Library of Medicine and Armit M. Shelat, “White Matter,” NIH, February 3, 2015, https://www.nlm.nih.gov/medlineplus/ency/article/002344.htm. 38 Zhong et al., “Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR.” 39 University of Cambridge, “Cerebrospinal Fluid Circulation in Brain,” Division of Neurosurgery, 2011. 40 Ibid. 41 National Library of Medicine and Shelat, “White Matter.” 42 Robert Brown et al., Magnetic Resonance Imaging: Physical Principles and Sequence Design, 2nd ed. (John Wiley & Sons, Inc, 2014). 43 aicadmin, “Advanced Imaging Centers Blog » MRI vs. CT: What’s The Difference?,” January 6, 2016, http://www.aicenters.com/blog/?p=760. 44 Vijay Laxmi, “Medical Devices Technologies and Global Markets” (BCC Research, September 2014). 45 NEMA, “About DICOM,” DICOM, accessed February 17, 2016, http://dicom.nema.org/Dicom/about- DICOM.html. 46 Ehsan Samei et al., “AAPM/RSNA Tutorial on Equipment Selection: PACS Equipment Overview,” RadioGraphics 24, no. 1 (January 1, 2004): 313–34, doi:10.1148/rg.241035137. 47 “3 Reasons to Eliminate Hierarchy in Your Company,” Inc, November 21, 2013, http://www.inc.com/will- yakowicz/the-world-isnt-flat-but-companies-should-be.html.2

References

“3 Reasons to Eliminate Hierarchy in Your Company.” Inc, November 21, 2013. http://www.inc.com/will-yakowicz/the-world-isnt-flat-but-companies-should- be.html. “AAMC Organization Directory.” AAMC, 2016. https://members.aamc.org/eweb/DynamicPage.aspx?site=AAMC&webcode=AAM COrgSearchResult&orgtype=Medical%20School. aicadmin. “Advanced Imaging Centers Blog » MRI vs. CT: What’s The Difference?,” January 6, 2016. http://www.aicenters.com/blog/?p=760. American Hospital Association. “Fast Facts on US Hospitals.” AHA, January 2016. http://www.aha.org/research/rc/stat-studies/fast-facts.shtml. Brown, Robert, Yu-Chung Cheng, E. Mark Haacke, Michael R. Thompson, and Ramesh Venkatesan. Magnetic Resonance Imaging: Physical Principles and Sequence Design. 2nd ed. John Wiley & Sons, Inc, 2014. Carlow, D., D. Dua, G. Erickson, and S. Garfield. “{PMD139} - Using Real-World Hospital Purchasing And Consumption Data To Improve Healthcare Systems Efficiencies.” Value in Health 18, no. 7 (2015): A369 – . doi:http://dx.doi.org/10.1016/j.jval.2015.09.741. Carlson, Curtis R, and William W Wilmot. Innovation the Five Disciplines for Creating What Customers Want. New York: Crown Business, 2013. http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=n labk&AN=723806. “DICOM Grid Competitors and Products in the Healthcare IT Marketplace.” Accessed March 6, 2016. http://www.health-care-it.com/company/622598/dicom-grid. Haacke, E. Mark. “Characterizing Iron Deposition in Multiple Sclerosis Lesions Using Susceptibility Weighted Imaging.” PubMed, NCBI, 2009. http://www.ncbi.nlm.nih.gov/pubmed/19243035. ———. “Imaging Iron Stores in the Brain Using Magnetic Resonance Imaging.” PubMed, NCBI, 2005. http://www.ncbi.nlm.nih.gov/pubmed/15733784. ———. Susceptibility weighted imaging. US6658280 B1, filed May 10, 2002, and issued December 2, 2003. http://www.google.com/patents/US6658280. Haacke, E. Mark, R. Ogg, J. R. Reichenbach, K. Gurleyik, Y. Xu, and G. Herigault. “Susceptibility Weighted Imaging (SWI): A New Means to Enhance Image Contrast.” In Proceedings of the 10th Annual Meeting of ISMRM, Honolulu, Hawaii, 297, 2002. http://cds.ismrm.org/ismrm-2002/PDF1/0297.PDF. Hancock, Christopher, Byron Bernal, Camila Medina, and Santiago Medina. “Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain.” Open Journal of Radiology 04, no. 03 (2014): 260–69. doi:10.4236/ojrad.2014.43034. Hartmann, P. “Normal Weight of the Brain in Adults in Relation to Age, Sex, Body Height and Weight.” PubMed, NCBI, June 15, 1994. http://www.ncbi.nlm.nih.gov/pubmed/8072950. “History.” MR Innovations, April 10, 2001. https://web.archive.org/web/20010410205602/http://mrinnovations.com/. “Imaging-Center Growth Hits the Wall in 2013; Volumes Plummeted in 2011 | Radiology Business.” Accessed March 8, 2016.

http://www.radiologybusiness.com/topics/business/imaging-center-growth-hits- wall-2013-volumes-plummeted-2011. Iron Disorders Institute. “Iron We Consume,” 2009. http://www.irondisorders.org/iron- we-consume/. Laxmi, Vijay. “Medical Devices Technologies and Global Markets.” BCC Research, September 2014. Lehr, Peggy. Medical Magnetic Resonance Imaging (MRI): Technologies and Global Markets. BCC Research, 2013. Magnetic Resonance Imaging Institute for Biomedical Research. “Susceptibility Weighted Imaging (SWI) Brochure,” 2006. “Medical Device User Fee Amendments (MDUFA) > FY2016 MDUFA User Fees.” Accessed March 4, 2016. http://www.fda.gov/ForIndustry/UserFees/MedicalDeviceUserFee/ucm452519.ht m. National Library of Medicine, and Armit M. Shelat. “White Matter.” NIH, February 3, 2015. https://www.nlm.nih.gov/medlineplus/ency/article/002344.htm. NEMA. “About DICOM.” DICOM. Accessed February 17, 2016. http://dicom.nema.org/Dicom/about-DICOM.html. OECD Data. “Health Equipment - Magnetic Resonance Imaging (MRI) Units.” Accessed March 5, 2016. https://data.oecd.org/healtheqt/magnetic-resonance-imaging-mri- units.htm. Peter G. Peterson Foundation. “The U.S. Performs a High Number of MRI Exams Compared to Other Countries.” Peter G. Peterson Foundation, August 16, 2015. http://www.pgpf.org/chart-archive/0052_MRI-exams. Peters, Simone. Research Storage, Transfer and Analysis of Medical Images: Global Markets. BCC Research, 2015. Pfefferbaum, Adolf, Elfar Adalsteinsson, Torsten Rohlfing, and Edith V. Sullivan. “MRI Estimates of Brain Iron Concentration in Normal Aging: Comparison of Field- Dependent (FDRI) and Phase (SWI) Methods.” NeuroImage 47, no. 2 (August 15, 2009): 493–500. doi:10.1016/j.neuroimage.2009.05.006. Rangel, Charles. “Text - H.R.3590 - 111th Congress (2009-2010): Patient Protection and Affordable Care Act.” Legislation, March 23, 2010. https://www.congress.gov/bill/111th-congress/house-bill/3590/text. Rappleye, Emily. “Average Cost per Inpatient Day across 50 States.” Becker’s Hospital Review. Accessed March 6, 2016. http://www.beckershospitalreview.com/finance/average- cost-per-inpatient-day-across-50-states.html. Samei, Ehsan, J. Anthony Seibert, Katherine Andriole, Aldo Badano, Jay Crawford, Bruce Reiner, Michael J. Flynn, and Paul Chang. “AAPM/RSNA Tutorial on Equipment Selection: PACS Equipment Overview.” RadioGraphics 24, no. 1 (January 1, 2004): 313–34. doi:10.1148/rg.241035137. “Scheduling an MRI - ASAP.” Malcom Baldrige National Quality Award Recipient, 2011. https://www.rsna.org/uploadedFiles/RSNA/Content/Science/Quality/Storyboards /2013/Wessman-QSE1056TUA.pdf. Sethi, Sean K., David T. Utriainen, Ana M. Daugherty, Wei Feng, J. Joseph Hewett, Naftali Raz, and E. Mark Haacke. “Jugular Venous Flow Abnormalities in Multiple Sclerosis Patients Compared to Normal Controls.” Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 25, no. 4 (August 2015): 600–607. doi:10.1111/jon.12183. 54

SRI International. “SRI’s ‘NABC’ Approach.” 2006. http://protomo.fi/sites/protomo.fi/files/public/attachments/events/nabc.pdf. State of Tennessee. “Magnetic Resonance Imaging.” Health Services and Development Agency, 2014. State of Vermont Green Mountain Care Board. “Statement of Decision and Order,” July 27, 2015. University of Cambridge. “Cerebrospinal Fluid Circulation in Brain.” Division of Neurosurgery, 2011. “What Is the Organ Distribution of MRI Studies?” Magnetic Resonance, 2016. http://www.magnetic-resonance.org/ch/21-01.html. “What We Do « Image Analysis.” Accessed March 13, 2016. http://www.imageanalysis.org.uk/what-we-do. Zhong, Yi, David Utriainen, Ying Wang, Yan Kang, and E. Mark Haacke. “Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR.” International Journal of Biomedical Imaging 2014 (2014). doi:10.1155/2014/239123.