Publications

2016
Zhenrui Chen, Yanmei Tie, Olutayo Olubiyi, Fan Zhang, Alireza Mehrtash, Laura Rigolo, Pegah Kahali, Isaiah Norton, Ofer Pasternak, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 2016. “Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers using Two-Tensor Unscented Kalman Filter Tractography.” Int J Comput Assist Radiol Surg, 11, 8, Pp. 1475-86.Abstract

PURPOSE: The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers. METHODS: Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts. RESULTS: Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]). CONCLUSION: Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

C. Wang, F Ji, Z Hong, JS Poh, R. Krishnan, J. Lee, G Rekhi, RSE Keefe, RA Adcock, SJ Wood, Alex Fornito, Ofer Pasternak, MW Chee, and J Zhou. 2016. “Disrupted Salience Network Functional Connectivity and White-Matter Microstructure in Persons at Risk For Psychosis: Findings from the LYRIKS Study.” Psychol Med, 46, 13, Pp. 2771-83.Abstract

BACKGROUND: Salience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression. METHOD: We examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis. RESULTS: ARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years. CONCLUSIONS: Our findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.

Zhang Fan, Savadjev Peter, Weidong Cai, Yang Song, Ragini Verma, Westin Carl-Fredrik, and Lauren J O'Donnell. 2016. “Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging.” IEEE Int Symp Biomed Imaging.Abstract
Autism Spectrum Disorder (ASD) has been suggested to associate with alterations in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation strategy to observe white matter connectivity alterations in ASD. Compared to another popular parcellation-based approach for tractography segmentation based on cortical regions, we hypothesized that the clustering-based method could provide a more anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.
 
Zhang ISBI 2016
Georg Langs, Danhong Wang, Polina Golland, Sophia Mueller, Ruiqi Pan, Mert R Sabuncu, Wei Sun, Kuncheng Li, and Hesheng Liu. 2016. “Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.” Cereb Cortex, 26, 10, Pp. 4004-14.Abstract
The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.
Ofer Pasternak, Marek Kubicki, and Martha E Shenton. 2016. “In vivo Imaging of Neuroinflammation in Schizophrenia.” Schizophr Res, 173, 3, Pp. 200-12.Abstract

In recent years evidence has accumulated to suggest that neuroinflammation might be an early pathology of schizophrenia that later leads to neurodegeneration, yet the exact role in the etiology, as well as the source of neuroinflammation, are still not known. The hypothesis of neuroinflammation involvement in schizophrenia is quickly gaining popularity, and thus it is imperative that we have reliable and reproducible tools and measures that are both sensitive, and, most importantly, specific to neuroinflammation. The development and use of appropriate human in vivo imaging methods can help in our understanding of the location and extent of neuroinflammation in different stages of the disorder, its natural time-course, and its relation to neurodegeneration. Thus far, there is little in vivo evidence derived from neuroimaging methods. This is likely the case because the methods that are specific and sensitive to neuroinflammation are relatively new or only just being developed. This paper provides a methodological review of both existing and emerging positron emission tomography and magnetic resonance imaging techniques that identify and characterize neuroinflammation. We describe \how these methods have been used in schizophrenia research. We also outline the shortcomings of existing methods, and we highlight promising future techniques that will likely improve state-of-the-art neuroimaging as a more refined approach for investigating neuroinflammation in schizophrenia.

Qiuyun Fan, Thomas Witzel, Aapo Nummenmaa, Koene RA Van Dijk, John D Van Horn, Michelle K Drews, Leah H Somerville, Margaret A Sheridan, Rosario M Santillana, Jenna Snyder, Trey Hedden, Emily E Shaw, Marisa O Hollinshead, Ville Renvall, Roberta Zanzonico, Boris Keil, Stephen Cauley, Jonathan R Polimeni, Dylan Tisdall, Randy L Buckner, Van J Wedeen, Lawrence L Wald, Arthur W Toga, and Bruce R Rosen. 2016. “MGH-USC Human Connectome Project Datasets with Ultra-high b-value Diffusion MRI.” Neuroimage, 124, Pt B, Pp. 1108-14.Abstract
The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.
Daniel S Margulies, Satrajit S Ghosh, Alexandros Goulas, Marcel Falkiewicz, Julia M Huntenburg, Georg Langs, Gleb Bezgin, Simon B Eickhoff, Xavier F Castellanos, Michael Petrides, Elizabeth Jefferies, and Jonathan Smallwood. 2016. “Situating the Default-mode Network along a Principal Gradient of Macroscale Cortical Organization.” Proc Natl Acad Sci U S A, 113, 44, Pp. 12574-9.Abstract

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

2015
Jens Sjölund, Filip Szczepankiewicz, Markus Nilsson, Daniel Topgaard, Carl-Fredrik Westin, and Hans Knutsson. 12/2015. “Constrained Optimization of Gradient Waveforms for Generalized Diffusion Encoding.” J Magn Reson, 261, Pp. 157-68.Abstract

Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences.

Yifei Lou and Allen Tannenbaum. 10/2015. “Inter-modality Deformable Registration.” In Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press.Abstract
Deformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Yifei Lou and Allen Tannenbaum. 10/2015. “Inter-modality Deformable Registration.” In Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press.Abstract
Deformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Frank King, Jagadeesan Jayender, Steve Pieper, Tina Kapur, Andras Lasso, and Gabor Fichtinger. 10/2015. “An Immersive Virtual Reality Environment for Diagnostic Imaging.” Int Conf Med Image Comput Comput Assist Interv. 18(WS). King MICCAI WS 2015
Mukund Balasubramanian, Robert V. Mulkern, William M Wells III, Padmavathi Sundaram, and Darren B Orbach. 10/2015. “Magnetic Resonance Imaging of Ionic Currents in Solution: The Effect of Magnetohydrodynamic Flow.” Magn Reson Med, 74, 4, Pp. 1145-55.Abstract

PURPOSE: Reliably detecting MRI signals in the brain that are more tightly coupled to neural activity than blood-oxygen-level-dependent fMRI signals could not only prove valuable for basic scientific research but could also enhance clinical applications such as epilepsy presurgical mapping. This endeavor will likely benefit from an improved understanding of the behavior of ionic currents, the mediators of neural activity, in the presence of the strong magnetic fields that are typical of modern-day MRI scanners. THEORY: Of the various mechanisms that have been proposed to explain the behavior of ionic volume currents in a magnetic field, only one-magnetohydrodynamic flow-predicts a slow evolution of signals, on the order of a minute for normal saline in a typical MRI scanner. METHODS: This prediction was tested by scanning a volume-current phantom containing normal saline with gradient-echo-planar imaging at 3 T. RESULTS: Greater signal changes were observed in the phase of the images than in the magnitude, with the changes evolving on the order of a minute. CONCLUSION: These results provide experimental support for the MHD flow hypothesis. Furthermore, MHD-driven cerebrospinal fluid flow could provide a novel fMRI contrast mechanism.

Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, and et al. 10/2015. “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).” IEEE Trans Med Imaging, 34, 10, Pp. 1993-2024.Abstract

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Adrian V Dalca, Ramesh Sridharan, Mert R Sabuncu, and Polina Golland. 10/2015. “Predictive Modeling of Anatomy with Genetic and Clinical Data.” Med Image Comput Comput Assist Interv, 9351, Pp. 519-26.Abstract

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory.

Tassilo Klein and William M Wells III. 10/2015. “RF Ultrasound Distribution-Based Confidence Maps.” Int Conf Med Image Comput Comput Assist Interv. 18 (Pt2), Pp. 595-602.Abstract
Ultrasound is becoming an ever increasingly important modality in medical care. However, underlying physical acquisition principles are prone to image artifacts and result in overall quality variation. Therefore processing medical ultrasound data remains a challenging task. We propose a novel distribution-based measure of assessing the confidence in the signal, which emphasizes uncertainty in attenuated as well as shadow regions. In contrast to the similar recently proposed method that relies on image intensities, the new approach makes use of the enveloped-detected radio-frequency data, facilitating the use of Nakagami speckle statistics. Employing J-divergence as distance measure for the random-walk based algorithm, provides a natural measure of similarity, yielding a more reliable estimate of confidence. For evaluation of the model’s performance, tests are conducted on the application of shadow detection. Additionally, computed maps are presented for different organs such as neck, liver and prostate, showcasing the properties of the model. The probabilistic approach is shown to have beneficial features for image processing tasks.
 
Klein MICCAI 2015
Ion-Florin Talos, Marianna Jakab, and Ron Kikinis. 9/2015. CT-based Atlas of the Abdomen. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Publisher's VersionAbstract
The Surgical Planning Laboratory at Brigham and Women's Hospital, Harvard Medical School, developed the SPL Abdominal Atlas. The atlas was derived from a computed tomography (CT) scan, using semi-automated image segmentation and three-dimensional reconstruction techniques. The current version consists of: 1. the original CT scan; 2. a set of detailed label maps; 3. a set of three-dimensional models of the labeled anatomical structures; 4. a mrml-file that allows loading all of the data into the 3D Slicer for visualization (see the tutorial associated with the atlas); 5. several pre-defined 3D-views (“anatomy teaching files”). The SPL Abdominal Atlas provides important reference information for surgical planning, anatomy teaching, and template driven segmentation. Visualization of the data requires Slicer 3. This software package can be downloaded from here. We are pleased to make this atlas available to our colleagues for free download. Please note that the data is being distributed under the Slicer license. By downloading these data, you agree to acknowledge our contribution in any of your publications that result form the use of this atlas. 
The Slicer4 version archived in a mrb (Medical Reality Bundle) file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH's National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Matthew D'Artista, Alex Kikinis, Tobias Schmidt, Svenja van der Gaag.
This atlas maybe viewed with our Open Anatomy Browser.
Marianna Jakab and Ron Kikinis. 9/2015. CT-based Atlas of the Head and Neck. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.Abstract
This Head and Neck Atlas has been made available by the Surgical Planning Laboratory at Brigham and Women's Hospital. The data set consists of: 1. Reduced resolution (256x256) of the MANIX data set from the OSIRIX data sets. 2. A set of detailed label maps. 3. A set of three-dimensional models of the labeled anatomical structures. 4. Several pre-defined Scene Views (“anatomy teaching files”). 5. Annotation as supplementary information associated with a scene. 6. Anatomical model hierarchy. All in a mrb (Medical Reality Bundle) archive file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download. The atlas data is made available under terms of the 3D Slicer License section B.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Neha Agrawal, Matthew D'Artista, Susan Kikinis, Dashawn Richardson, Daniel Sachs.
This atlas maybe viewed with our Open Anatomy Browser.
Jens Richolt, Marianna Jakab, and Ron Kikinis. 9/2015. MRI-based Atlas of the Knee. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Publisher's VersionAbstract
The Surgical Planning Laboratory at Brigham and Women's Hospital, Harvard Medical School, developed the SPL Knee Atlas. The atlas was derived from a MRI scan, using semi-automated image segmentation and three-dimensional reconstruction techniques. The current version consists of: 1. the original MRI scan; 2. a set of detailed label maps; 3. a set of three-dimensional models of the labeled anatomical structures; 4. a mrml-file that allows loading all of the data into the 3D Slicer for visualization. 5. several pre-defined 3D views (“anatomy teaching files”). The SPL Knee Atlas provides important reference information for anatomy teaching, and template driven segmentation. Visualization of the data requires Slicer 3. This software package can be downloaded from here. We are pleased to make this atlas available to our colleagues for free download. Please note that the data is being distributed under the Slicer license. By downloading these data, you agree to acknowledge our contribution in any of your publications that result form the use of this atlas. 
The Slicer4 version archived in a mrb (Medical Reality Bundle) file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH's National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Matthew D'Artista, Alex Kikinis.
This atlas maybe viewed with our Open Anatomy Browser.
Lipeng Ning, Kawin Setsompop, Oleg Michailovich, Nikos Makris, Carl-Fredrik Westin, and Yogesh Rathi. 6/2015. “A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI.” Inf Process Med Imaging, 24, Pp. 57-68.Abstract

We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use subpixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images.

Matthew Toews, Christian Wachinger, Raul San Jose Estepar, and William M Wells III. 6/2015. “A Feature-Based Approach to Big Data Analysis of Medical Images.” Inf Process Med Imaging, 24, Pp. 339-50.Abstract

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

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