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Initial Operating Room Experience with Digital Variance Angiography in Carbon Dioxide-Assisted Lower Limb Interventions: a Pilot Study
Cardiovasc Intervent Radiol https://doi.org/10.1007/s00270-020-02530-5 TECHNICAL NOTE Initial Operating Room Experience with Digital Variance Angiography in Carbon Dioxide-Assisted Lower Limb Interventions: A Pilot Study 1,3 1 2,3 3,4 Marcell Gya´no´ • Csaba Csobay-Nova´k • Ma´rton Berczeli • Istva´n Go´g • 3 3,5 3,5 1 Ja´nos P. Kiss • Krisztia´n Szigeti • Szabolcs Osva´th • Bala´zs Nemes Received: 4 January 2020 / Accepted: 15 May 2020 Ó The Author(s) 2020 Abstract Raters preferred DVA images in terms of diagnostic value Purpose In retrospective clinical studies digital variance and usefulness for therapeutic decisions in 85.2% and angiography (DVA) provided higher contrast-to-noise ratio 83.9% of all comparisons, respectively. These benefits and better image quality than digital subtraction angiog- were achieved at lower frame rates (1–3 FPS) than usually raphy (DSA). Our aim was to verify the clinical usefulness recommended for CO2 angiography (4–6 FPS). No adverse and benefits of DVA in carbon dioxide (CO2)-assisted events were recorded during or after the procedures. lower limb interventions. Conclusions Our initial experience shows that DVA might Materials and Methods A workstation running the DVA facilitate the correct diagnostic and therapeutic decisions, software was integrated into a Siemens Artis Zee with Pure and potentially help to reduce radiation exposure in lower angiography system, and this new image processing tech- limb CO2 angiography. Although the dose management nology was used in four patients (3 male, 1 female, age: capabilities of DVA have to be validated in further clinical 76.2 ± 4.2 years) with peripheral artery disease (PAD, studies, this technology might be a useful new tool in the Rutherford 2–3) and impaired renal function (average operating room and contributes to the safety and efficacy of 2 eGFR 25.5 ± 11.2 ml/min/1.73 m ). -
Significant Contrast Dose-Reduction with Digital Variance Angiography in Carotid and Cerebral X-Ray Angiography
Significant contrast dose-reduction with Digital Variance Angiography in carotid and cerebral X-ray angiography Viktor Orias MD Bács-Kiskun County Hospital, Kecskemét, Hungary Heart and Vascular Center of Semmelweis University, Budapest, Hungary Disclosure Speaker name: Viktor Orias MD ................................................................................. I have the following potential conflicts of interest to report: Employment in industry: Kinepict Health Ltd. Kinetic imaging (or DVA) • New, patented x-ray image processing method • Digital Variance Angiography (DVA) instead of subtraction (DSA) • Significant image quality advantage over current reference- standard DSA in lower extremity arteriography with iodinated contrast media (1) and carbon-dioxide (2) • First aim: DVA feasibility in carotid angio setting 1: Gyano M, Gog I, Orias VI, et al. Kinetic Imaging in Lower Extremity Arteriography: Comparison to Digital Subtraction Angiography. Radiology. 2019;290(1):246-53 2: Orias VI, Gyano M, Szollosi D, et al. Digital Variance Angiography as a Paradigm Shift in Carbon-Dioxide Angiography, Investigative Radiology (forthcoming 2019) Carotid and cerebral X-ray angiography • Contrast medium • Nephrotoxicity • „Selective injections of hyperosmolar contrast material into the common and internal carotid arteries may... • Cause pain, resulting in patient movement • Decreased image quality • Increased patient discomfort • “May produce transient disruption of the blood-brain barrier with associated neurologic deficit or seizure” (3) -
DVA White Paper SCI V4.0 BAZ
Digital Variance Angiography, a technological breakthrough in X-ray angiography (white paper) Summary: Digital Variance Angiography (DVA) is a proprietary innovative parametric imaging method that outperforms Digital Subtraction Angiography (DSA) in vascular imaging in the following three aspects: enhanced image quality, X-ray dose reduction, and decrease of contrast agent use. DVA technology is implemented in the CE IIa marked and FDA approved Kinepict Medical Imaging Tool, available for use in the operating room in Europe and the USA. Colour-coded DVA image of a hepatic tumour (courtesy of Prof Thomas Vogl, Uniklinik Frankfurt) Cardiovascular disorders are the leading cause of death worldwide. Their diagnosis and treatment are key objectives of the global healthcare systems. Currently, Digital Subtraction Angiography (DSA) is the imaging method used for interventional radiology to visualise blood vessels in cardiovascular disorders – however, the image quality is not always appropriate (CO2 angiography) and the iodinated contrast media (ICM) and radiation applied during the procedure might cause complications for patients. A solution might be Digital Variance Angiography (DVA), a new image processing technology developed recently by Kinepict Health Ltd. The method is based on the principles of kinetic imaging [Szigeti et al, 2014], thereby it can obtain more information from medical examinations using penetrating radiation than the currently used image processing algorithms. In 2D X-ray angiography, the gold standard is DSA, which records a contrast-enhanced image series and subtracts one of these images (the mask) from the other frames. These subtracted images compose the DSA video and their appropriate integration yields the DSA image. In contrast, DVA uses all frames of an unsubtracted series and calculates the standard deviation of intensity for each pixel. -
Kinepict Health, Ltd. March 5, 2020 Ms. Lilla Strobel Quality And
Kinepict Health, Ltd. March 5, 2020 ℅ Ms. Lilla Strobel Quality and Regulatory Manager Kelta k z Budakeszi, H-2092 HUNGARY Re: K190993 Trade/Device Name: Kinepict Medical Imaging Tool version v2.2 Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: January 29, 2020 Received: January 29, 2020 Dear Ms. Strobel: We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading. -
A Novel Adaptive Probabilistic Nonlinear Denoising Approach for Enhancing PET Data Sinogram
Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2013, Article ID 732178, 14 pages http://dx.doi.org/10.1155/2013/732178 Research Article A Novel Adaptive Probabilistic Nonlinear Denoising Approach for Enhancing PET Data Sinogram Musa Alrefaya1,2 and Hichem Sahli1,3 1 Department of Electronics and Informatics (ETRO-IRIS), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium 2 Faculty of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, Palestine 3 Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium Correspondence should be addressed to Musa Alrefaya; [email protected] Received 23 March 2013; Revised 4 May 2013; Accepted 4 May 2013 Academic Editor: Hang Joon Jo Copyright © 2013 M. Alrefaya and H. Sahli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose filtering the PET sinograms with a constraint curvature motion diffusion. The edge-stopping function is computed in terms of edge probability under the assumption of contamination by Poisson noise. We show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram noise is uncorrelated and follows a Poisson distribution, we then propose an adaptive probabilistic diffusivity function where the edge probability is computed at each pixel. The filter is applied on the 2D sinogram prereconstruction. The PET images are reconstructed using the Ordered Subset Expectation Maximization (OSEM). We demonstrate through simulations with images contaminated by Poisson noise that the performance of the proposed method substantially surpasses that of recently published methods, both visually and in terms of statistical measures. -
Advanced Kinetic Modelling Strategies: Towards Adoption in Clinical PET Imaging
This is a repository copy of Advanced kinetic modelling strategies: Towards adoption in clinical PET imaging. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/93618/ Version: Accepted Version Article: Kotasidis, FA, Tsoumpas, C and Rahmim, A (2014) Advanced kinetic modelling strategies: Towards adoption in clinical PET imaging. Clinical and Translational Imaging, 2 (3). pp. 219-237. ISSN 2281-5872 https://doi.org/10.1007/s40336-014-0069-8 Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Advanced kinetic modeling strategies: towards adoption in clinical PET imaging Fotis A. Kotasidis1,2†, Charalampos Tsoumpas3, Arman Rahmim4 1Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland 2 Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester, UK 3 Division of Medical Physics, University of Leeds, Leeds, UK 4 Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA Abstract Positron emission tomography (PET) is a highly quantitative imaging modality and can probe a number of functional and biologic processes depending on the radio-labeled tracer. -
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Collimatorless coincidence imaging. Item Type text; Dissertation-Reproduction (electronic) Authors Saffer, Janet Susan Reddin. Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 25/09/2021 15:59:28 Link to Item http://hdl.handle.net/10150/186442 INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedtbrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely. event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. -
Supplementary Information for Reconstruction of Transcriptional Dynamics from Gene Reporter Data Using Differential Equations
Supplementary information for Reconstruction of transcriptional dynamics from gene reporter data using differential equations. B. Finkenst¨adt,E. A. Heron, M. Komorowski, K. Edwards, S. Tang, C. V. Harper, J. R. E. Davis, M. R. H. White, A. J. Millar and D. A. Rand Abstract This is supplementary information for the main paper Reconstruction of transcriptional dynamics from gene reporter data using differential equations. The main paper is referred to as MP. Scaled Model Suppose that we measure M; P indirectly through variables M~ (t) = sM M(t) for the mRNA and P~(t) = sP P (t) for the reporter protein. Re-formulating model (1) in MP gives a scaled model dM=dt~ =τ ~(t; θτ ) − δM M~ (t) dP~ =dt =α ~M~ (t) − δP P~(t); (1) sP where the transcription functionτ ~(t; θτ ) = sM τ(t; θτ ) and the translation rateα ~ = α are now functions sM of the unknown scaling coefficients sM ; sP . Obviously, the functional forms of (1) and model (1) in MP are identical. If we set M = M;P~ = P~ ; τ =τ ~ and α =α ~ then (1) in MP denotes the scaled model. Diffusion Approximation Let pt(M; P ) denote the probability that at time t system is in the state (M; P ). The evolution of the joint probability is described by the chemical master equation of the form (see (Thattai and van Oudenaarden 2001) for derivation) dp (M; P; t) t = τ(t)(p (M − 1;P ) − p (M; P )) + αM(p (M; P − 1) − p (M; P )) (2) dt t t t t +δM (pt(M + 1;P )(M + 1) − pt(M; P )M) + δP (pt(M; P + 1)(P + 1) − pt(M; P )P ): In order to obtain a diffusion approximation of (2) we replace increments on the right hand of the above with their second order Taylor expansions. -
Predicting Cancer Patient Survival Using Dynamic Contrast Enhanced MRI
Predicting Cancer Patient Survival Using Dynamic Contrast Enhanced MRI A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Medical and Human Sciences 2016 Ben Dickie School of Medicine Contents 1 Introduction 18 1.1 Locally advanced cancers . 18 1.1.1 The need for personalised treatments . 18 1.1.2 TNM stage . 24 1.1.3 Tumour microvasculature and hypoxia . 26 1.1.4 Existing methods for measuring microvascular function . 28 1.1.5 Existing methods for measuring tumour hypoxia . 29 1.2 Magnetic resonance imaging . 32 1.2.1 Nuclear magnetic resonance . 32 1.2.2 The bulk longitudinal magnetisation . 34 1.2.3 Nuclear excitation and relaxation . 36 1.2.4 Spatial localisation of NMR signals . 40 1.2.5 Gradient and spin echoes . 45 1.2.6 Image contrast . 45 1.3 Dynamic contrast-enhanced MRI . 49 1.3.1 Quantitative DCE-MRI . 50 1.3.2 A Quantitative DCE-MRI experiment . 51 1.3.3 Defining the tumour region of interest (ROIt) . 52 1.3.4 Estimating pre-contrast T1 ..................... 55 1.3.5 Dynamic imaging: measuring the TRF and AIF . 56 1.3.6 Tracer kinetic modelling . 60 1.3.7 Prognostic value of pre-treatment microvascular function . 66 1.3.8 Prognostic value of pre-treatment intratumoural microvascular heterogeneity . 68 1.4 Predicting patient prognosis . 78 1.4.1 Endpoints and censoring . 79 1.4.2 Modelling failure time . 80 1.4.3 Estimating S(t), h(t), and H(t) . 83 1.4.4 The Kaplan-Meier and Nelson-Aalen estimators . -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Brain Imaging with Positron Emission Tomography: Quantification and Biomedical Applications in Alzheimer's Disease and Brain Tumors Permalink https://escholarship.org/uc/item/8bk5d65s Author Wardak, Mirwais Publication Date 2013 Supplemental Material https://escholarship.org/uc/item/8bk5d65s#supplemental Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Brain Imaging with Positron Emission Tomography: Quantification and Biomedical Applications in Alzheimer’s Disease and Brain Tumors A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Biomedical Physics by Mirwais Wardak 2013 © Copyright by Mirwais Wardak 2013 ABSTRACT OF THE DISSERTATION Brain Imaging with Positron Emission Tomography: Quantification and Biomedical Applications in Alzheimer’s Disease and Brain Tumors by Mirwais Wardak Doctor of Philosophy in Biomedical Physics University of California, Los Angeles, 2013 Professor Sung-Cheng Huang, Chair Positron emission tomography (PET) is a unique and powerful imaging technique that is used to visualize and quantify various biological processes in living subjects in health and disease. PET imaging can also provide biological information for the assessment of therapies. In this dissertation, we will cover three projects that utilize the quantitative capability of PET for studying two neurological disorders: Alzheimer’s disease and brain tumors. One of the goals in PET imaging is to produce an image volume that accurately describes the true distribution of the injected radioactivity. The correction factor that has the most significant impact on the quantitative aspects of a PET image is attenuation correction. -
Kinetic Imaging As a Novel Analysis of X-Ray Angiography
Kinetic imaging as a Marcell Gyano MD novel analysis of X-ray angiography CIRSE 16/09/2017 Copenhagen Disclosures I have the following potential conflicts of interest to report: I received travel support from Semmelweis University I am employed by and receiving honoraria from Kinepict Health Ltd. as a researcher First ideas Motion Based X-Ray Imaging Modality Krisztian Szigeti; Domokos Mathe; Szabolcs Osvath IEEE Transactions on Medical Imaging, 2014, Volume: 33, Issue: 10 Theoretical background of kinetic imaging Kinetic image possibly better than Deviation DSA A t Novel statistical t analysis of intensity e Mean deviation in motion n intensity image series u a t Big deviation causes i small mean intensity o change n Time DSA: mean intensity Mask - mask Does the kinetic image show the same vasculature as DSA? • Fractal Dimension - Box Counting (Minkowski-Bouligand Dimension) KIN DSA KIN-DSA Results • Fractal dimension measurements: • 10 image pairs of lower limb angiography • The fractal dimensions of the segmented images were compared with paired t-test • No significant differences were found (p = 0,3112) Which image would the physicist choose? Raw sumDSA Postprocessed sumDSA Raw kinetic picture Signal to noise ratio (SNR) Signal = intensity difference Noise = field deviation - Raw sumDSA (rsDSA): calculated from all the pictures in the angiography series - Siemens post-processed sumDSA (ssDSA): rsDSA + Pixel Shift and other image quality enhancers (Siemens Artis zee with Pure, Siemens Syngo workstation - 2016) - Kinetic image (KIN): -
Kinetic Modelling Approaches to in Vivo Imaging
REVIEWS KINETIC MODELLING APPROACHES TO IN VIVO IMAGING Robert D. Phair* and Tom Misteli‡ The ability to visualize protein dynamics and biological processes by in vivo microscopy is revolutionizing many areas of biology. These methods generate large, kinetically complex data sets, which often cannot be intuitively interpreted. The combination of dynamic imaging and computational modelling is emerging as a powerful tool for the quantitation of biophysical properties of molecules and processes. The new discipline of computational cell biology will be essential in uncovering the pathways, mechanisms and controls of biological processes and systems as they occur in vivo. 2,3 CONFOCAL MICROSCOPY Science is built up of facts just as a house is built up interactions between molecules in vivo . More rele- A microscopy method used to of stones, but a collection of facts is no more a vant to the topic of this discussion, by combining obtain a thin optical section science than a heap of stones is a house. in vivo microscopy with computational approaches, it through a specimen. H. Poincaré is now possible, for the first time, to extract quantita- MULTI-PHOTON MICROSCOPY tive information about the biophysical properties of A microscopy method that uses Ever since the first microscope was built in the seven- proteins within living cells. We discuss here some of the simultaneous absorbance of teenth century, morphological observations by the kinetic microscopy methods that provide the basis several low-energy electrons to microscopy have driven the course of biology. of quantitative in vivo imaging and we outline compu- generate an optical section Microscopes allowed the discovery of the cell as the tational approaches to extract biophysical information through a specimen.