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A Semi-Automated Workflow Solution for Multimodal Neuroimaging

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A Semi-Automated Workflow Solution for Multimodal Neuroimaging Brain Informatics (2016) 3:1–15 DOI 10.1007/s40708-015-0026-y A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury Koon-Pong Wong . Marvin Bergsneider . Thomas C. Glenn . Vladimir Kepe . Jorge R. Barrio . David A. Hovda . Paul M. Vespa . Sung-Cheng Huang Received: 2 November 2015 / Accepted: 16 November 2015 / Published online: 1 December 2015 Ó The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Traumatic brain injury (TBI) is a major cause 15O-water (0–10 min) and 18F-FDDNP (0–6 min) for of mortality and morbidity, placing a significant financial measuring cerebral blood flow in patients with TBI. burden on the healthcare system worldwide. Non-invasive neuroimaging technologies have been playing a pivotal Keywords Traumatic brain injury (TBI) Á Cerebral blood role in the study of TBI, providing important information flow (CBF) Á Magnetic resonance imaging (MRI) Á for surgical planning and patient management. Advances in 15O-water Á 18F-FDDNP Á Positron emission tomography understanding the basic mechanisms and pathophysiology (PET) Á Spatial normalization of the brain following TBI are hindered by a lack of reli- able image analysis methods for accurate quantitative assessment of TBI-induced structural and pathophysiolog- 1 Introduction ical changes seen on anatomical and functional images obtained from multiple imaging modalities. Conventional Traumatic brain injury (TBI) is an important public health region-of-interest (ROI) analysis based on manual labeling and socio-economic problem throughout the world. It is of brain regions is time-consuming and the results could be one of the most common causes of death and long-term inconsistent within and among investigators. In this study, disability in adolescents, young adults, and the elderly. In we propose a workflow solution framework that combined the United States, it was estimated that 1.7 million people the use of non-linear spatial normalization of structural sustain a TBI annually [1]. Of these people, approximately brain images and template-based anatomical labeling to 81 % were treated in and released from emergency automate the ROI analysis process. The proposed workflow departments, about 16 % were hospitalized and discharged, solution is applied to dynamic PET scanning with and approximately 3 % died [1]. However, these numbers underestimate the real prevalence of TBI as they do not K.-P. Wong (&) Á V. Kepe Á J. R. Barrio Á account for those people who did not seek for medical care, D. A. Hovda Á S.-C. Huang had non-fatal (mild or moderate) TBI and presented in Department of Molecular and Medical Pharmacology, David outpatient settings such as physician’s offices, or those who Geffen School of Medicine at UCLA, Los Angeles, CA, USA received medical care from federal, military, or Veterans e-mail: [email protected] Affairs hospitals [1]. M. Bergsneider Á T. C. Glenn Á D. A. Hovda Á P. M. Vespa Non-invasive neuroimaging technologies have been Department of Neurosurgery, David Geffen School of Medicine playing a pivotal role in the study of TBI, providing at UCLA, Los Angeles, CA, USA important information for anatomic localization, surgical P. M. Vespa planning, staging and monitoring the therapeutic responses, Department of Neurology, David Geffen School of Medicine at and predicting the recovery outcomes that could improve UCLA, Los Angeles, CA, USA the survival and change management in patients under acute and chronic conditions. Survivors of TBI typically S.-C. Huang Department of Biomathematics, David Geffen School of live with varying degrees of physical disability and suffer Medicine at UCLA, Los Angeles, CA, USA from significant cognitive deficits (e.g., impaired attention 123 2 K.-P. Wong et al. and poor executive function) and psychological health curves and surfaces as well as the boundaries and neigh- issues (e.g., depression and elevated impulsivity), all of borhoods between structures while allowing a large degree which require long-term or lifelong medical care and of transformation. Examples of this class of algorithms support. Advances in understanding the basic mechanisms include Demons [14], LDDMM [18], DARTEL [19] and pathophysiology of the brain following TBI are available in the SPM software (http://www.fil.ion.ucl.ac. somewhat limited due to lack of reliable image analysis uk/spm/), FNIRT [20] implemented in the FSL software methods that allow accurate quantitative assessment of (http://www.fmrib.ox.ac.uk/fsl/), and symmetric image TBI-induced structural and pathophysiological changes normalization (SyN) [21, 22] implemented in an open seen on anatomical and functional images obtained from source software package ANTs (Advanced Normalization multiple imaging modalities. The use of multimodal neu- Tools, [23]), which was built on an Insight Segmentation roimaging technologies has the advantages to overcome the and Registration ToolKit (ITK) framework (http://www. limitations of any individual imaging modality and to itk.org/). We used SyN in this study for non-linear brain aggregate clinical characteristics and features obtained warping as it can work with different similarity metrics and from different imaging techniques for knowledge mining regularization kernels [23] and has been extensively eval- and for guiding medical diagnosis and decision making [2]. uated with 8 different performance measures using 80 Some state-of-the-art methods of multimodal imaging and manually labeled MR brain images in a recent large-scale their uses in brain research can be found in the following comparative image registration study and was ranked the review articles [3, 4]. While conventional region-of-inter- overall best among 14 non-linear brain warping algorithms est (ROI) analysis enables quantitation of regional changes being assessed [24]. and serves as the basis for comparing data between indi- To address the technical difficulties in analyzing TBI viduals both within and across imaging modalities [5], imaging data, we propose and develop a workflow solution delineation of brain regions through manual ROI drawing framework that combined the use of non-linear brain is labor-intensive and time-consuming, and is also prone to warping of structural MR images and anatomical labeling reproducibility errors [2, 6, 7]. The complexity level of to automatically derive the regional cerebral blood flow ROI analysis increases tremendously by the complicated (CBF) parameters from multi-tracer PET studies. CBF is an nature of TBI that generally involves a combination of important physiological parameter for assessment of brain focal and diffuse injury mechanisms. Depending on the function in normal and pathological conditions. Brain tis- cause and severity of the brain injury, variability of indi- sues depend on CBF for delivery of nutrients and for vidual TBI brains is increased, particularly in the presence removal of metabolic products. Since Kety and Schmidt of focal lesions (e.g., contusion and hemorrhage) and large developed a theory of inert-gas exchange in 1940s [25], deformations within the brain (e.g., swelling and enlarge- many methods have become available for measuring CBF ment/shrinkage of ventricular space). This adds significant in human. Currently, PET imaging with 15O-water is con- difficulties to conduct group-level analysis (such as statis- sidered the gold standard for non-invasive quantification of tical parametric mapping, SPM [8]) and poses technical CBF [26, 27]. Using a hydrophobic tracer, 2-(1-{6-[(2- challenges to perform atlas-based anatomical labeling and [18F]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)- ROI analysis [9, 10] with minimal or no human interven- malononitrile (18F-FDDNP), the initial uptake (0–6 min) of tion. The gist of the problem lies in the use of spatial which is perfusion-limited, it has been shown that regional normalization that integrates brain images obtained from perfusion can be inferred from the relative-delivery different modalities for the same individuals to establish a parameter derived by reference-tissue modeling and from one-to-one correspondence mapping between voxels of the early-summed image, and thus represent surrogate individual brains and a standard brain template in a com- indices of CBF [28]. The use of the proposed workflow mon stereotaxic space. solution is illustrated with neuroimaging data obtained A number of non-linear image registration methods have from T1-weighted magnetic resonance (MR) imaging and been proposed for spatial normalization. Some of these dynamic PET scanning of dual tracers (15O-water and methods use a linear combination of trigonometric func- 18F-FDDNP) on six TBI patients under acute condition. tions [11] or polynomials [12] as the transformation model. Because of an implicit assumption of small deformations, this class of methods would fail to normalize images with 2 Materials and methods large deformation, resulting in folding, shearing, and tearing of neighboring structures in the original image upon 2.1 Subjects and study protocol non-linear transformation. More recent research has been geared toward the development of a large deformation The study was approved by the UCLA Institutional Review framework [13–18] which preserves the continuity of Board and was conducted under the auspices of the UCLA 123 A semi-automated workflow solution for multimodal neuroimaging: application to patients 3 Brain Injury Research Center. Six patients with acute TBI 18F-FDDNP PET studies are
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