Publications by Year: 2017

Anna Custo, Dimitri Van De Ville, William M Wells, Miralena I Tomescu, Denis Brunet, and Christoph M Michel. 12/2017. “Electroencephalographic Resting-State Networks: Source Localization of Microstates.” Brain Connect, 7, 10, Pp. 671-82.Abstract
Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.
Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E Reddick, John O Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, Omar Ocegueda J González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal MW Tax, Fenghua Guo, Hamed Y Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, Ertan H Cetingul, Benjamin L Odry, Boris Mailhe, Mariappan S Nadar, Fabrizio Pizzagalli, Gautam Prasad, Julio E Villalon-Reina, Justin Galvis, Paul M Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell'Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B Dyrby, Tim Holland-Letz, Claus C Hilgetag, Bram Stieltjes, and Maxime Descoteaux. 11/2017. “The Challenge of Mapping the Human Connectome Based on Diffusion Tractography.” Nat Commun, 8, 1, Pp. 1349.Abstract
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Lena KL Oestreich, Amanda E Lyall, Ofer Pasternak, Zora Kikinis, Dominick T Newell, Peter Savadjiev, Sylvain Bouix, Martha E Shenton, Marek Kubicki, Marek Kubicki, Thomas J Whitford, and Simon McCarthy-Jones. 11/2017. “Characterizing White Matter Changes in Chronic Schizophrenia: A Free-water Imaging Multi-site Study.” Schizophr Res, 189, Pp. 153-61.Abstract
Diffusion tensor imaging (DTI) studies in chronic schizophrenia have found widespread but often inconsistent patterns of white matter abnormalities. These studies have typically used the conventional measure of fractional anisotropy, which can be contaminated by extracellular free-water. A recent free-water imaging study reported reduced free-water corrected fractional anisotropy (FAT) in chronic schizophrenia across several brain regions, but limited changes in the extracellular volume. The present study set out to validate these findings in a substantially larger sample. Tract-based spatial statistics (TBSS) was performed in 188 healthy controls and 281 chronic schizophrenia patients. Forty-two regions of interest (ROIs), as well as average whole-brain FAT and FW were extracted from free-water corrected diffusion tensor maps. Compared to healthy controls, reduced FAT was found in the chronic schizophrenia group in the anterior limb of the internal capsule bilaterally, the posterior thalamic radiation bilaterally, as well as the genu and body of the corpus callosum. While a significant main effect of group was observed for FW, none of the follow-up contrasts survived correction for multiple comparisons. The observed FAT reductions in the absence of extracellular FW changes, in a large, multi-site sample of chronic schizophrenia patients, validate the pattern of findings reported by a previous, smaller free-water imaging study of a similar sample. The limited number of regions in which FAT was reduced in the schizophrenia group suggests that actual white matter tissue degeneration in chronic schizophrenia, independent of extracellular FW, might be more localized than suggested previously.
Joost JM van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina GH Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, and Hugo JWL Aerts. 11/2017. “Computational Radiomics System to Decode the Radiographic Phenotype.” Cancer Res, 77, 21, Pp. e104-e107.Abstract
Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. .
Christian Herz, Jean-Christophe Fillion-Robin, Michael Onken, Jörg Riesmeier, Andras Lasso, Csaba Pinter, Gabor Fichtinger, Steve Pieper, David Clunie, Ron Kikinis, and Andriy Fedorov. 11/2017. “DCMQI: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results using DICOM.” Cancer Research, 77, 21, Pp. e87-e90.Abstract
Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided on Harvard DASH.
Sarah Parisot, Ben Glocker, Sofia Ira Ktena, Salim Arslan, Markus D Schirmer, and Daniel Rueckert. 11/2017. “A Flexible Graphical Model for Multi-modal Parcellation of the Cortex .” Neuroimage, 162, Pp. 226-48.Abstract
Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
Isaiah Norton, Walid Ibn Essayed, Fan Zhang, Sonia Pujol, Alex Yarmarkovich, Alexandra J Golby, Gordon Kindlmann, Demian Wasserman, Raul San Jose Estepar, Yogesh Rathi, Steve Pieper, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, and Lauren J O'Donnell. 11/2017. “SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research.” Cancer Res, 77, 21, Pp. e101-e103.Abstract
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer ( More information, videos, tutorials, and sample data are available at Cancer Res; 77(21); e101-3. ©2017 AACR.
Edward Ofori, Florian Krismer, Roxana G Burciu, Ofer Pasternak, Johanna L McCracken, Mechelle M Lewis, Guangwei Du, Nikolaus R McFarland, Michael S Okun, Werner Poewe, Christoph Mueller, Elke R Gizewski, Michael Schocke, Christian Kremser, Hong Li, Xuemei Huang, Klaus Seppi, and David E Vaillancourt. 10/2017. “Free Water Improves Detection of Changes in the Substantia Nigra in Parkinsonism: A Multisite Study.” Mov Disord, 32, 10, Pp. 1457-64.Abstract
BACKGROUND: Imaging markers that are sensitive to parkinsonism across multiple sites are critically needed for clinical trials. The objective of this study was to evaluate changes in the substantia nigra using single- and bi-tensor models of diffusion magnetic resonance imaging in PD, MSA, and PSP. METHODS: The study cohort (n = 425) included 107 healthy controls and 184 PD, 63 MSA, and 71 PSP patients from 3 movement disorder centers. Bi-tensor free water, free-water-corrected fractional anisotropy, free-water-corrected mean diffusivity, single-tensor fractional anisotropy, and single-tensor mean diffusivity were computed for the anterior and posterior substantia nigra. Correlations were computed between diffusion MRI measures and clinical measures. RESULTS: In the posterior substantia nigra, free water was greater for PSP than MSA and PD patients and controls. PD and MSA both had greater free water than controls. Free-water-corrected fractional anisotropy values were greater for PSP patents than for controls and PD patients. PSP and MSA patient single-tensor mean diffusivity values were greater than controls, and single-tensor fractional anisotropy values were lower for PSP patients than for healthy controls. The parkinsonism effect size for free water was 0.145 in the posterior substantia nigra and 0.072 for single-tensor mean diffusivity. The direction of correlations between single-tensor mean diffusivity and free-water values and clinical scores was similar at each site. CONCLUSIONS: Free-water values in the posterior substantia nigra provide a consistent pattern of findings across patients with PD, MSA, and PSP in a large cohort across 3 sites. Free water in the posterior substantia nigra relates to clinical measures of motor and cognitive symptoms in a large cohort of parkinsonism. © 2017 International Parkinson and Movement Disorder Society.
Miaomiao Zhang, William M Wells, and Polina Golland. 10/2017. “Probabilistic Modeling of Anatomical Variability using a Low Dimensional Parameterization of Diffeomorphisms.” Med Image Anal, 41, Pp. 55-62.Abstract
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space.
David Black, Christian Hansen, Arya Nabavi, Ron Kikinis, and Horst Hahn. 10/2017. “A Survey of Auditory Display in Image-guided Interventions.” Int J Comput Assist Radiol Surg, 12, 10, Pp. 1665-76.Abstract
PURPOSE: This article investigates the current state of the art of the use of auditory display in image-guided medical interventions. Auditory display is a means of conveying information using sound, and we review the use of this approach to support navigated interventions. We discuss the benefits and drawbacks of published systems and outline directions for future investigation. METHODS: We undertook a review of scientific articles on the topic of auditory rendering in image-guided intervention. This includes methods for avoidance of risk structures and instrument placement and manipulation. The review did not include auditory display for status monitoring, for instance in anesthesia. RESULTS: We identified 15 publications in the course of the search. Most of the literature (60%) investigates the use of auditory display to convey distance of a tracked instrument to an object using proximity or safety margins. The remainder discuss continuous guidance for navigated instrument placement. Four of the articles present clinical evaluations, 11 present laboratory evaluations, and 3 present informal evaluation (2 present both laboratory and clinical evaluations). CONCLUSION: Auditory display is a growing field that has been largely neglected in research in image-guided intervention. Despite benefits of auditory displays reported in both the reviewed literature and non-medical fields, adoption in medicine has been slow. Future challenges include increasing interdisciplinary cooperation with auditory display investigators to develop more meaningful auditory display designs and comprehensive evaluations which target the benefits and drawbacks of auditory display in image guidance.
Marc Niethammer, Kilian M Pohl, Firdaus Janoos, and William M Wells. 9/2017. “Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models.” SIAM J. Imaging Sci., 10, 3, Pp. 1069-1103.Abstract
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating this uncertainty is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. However, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the active mean fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model, in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the tt icgbench dataset.
David Black, Julian Hettig, Maria Luz, Christian Hansen, Ron Kikinis, and Horst Hahn. 9/2017. “Auditory Feedback to Support Image-Guided Medical Needle Placement.” Int J Comput Assist Radiol Surg, 12, 9, Pp. 1655-63.Abstract
PURPOSE: During medical needle placement using image-guided navigation systems, the clinician must concentrate on a screen. To reduce the clinician's visual reliance on the screen, this work proposes an auditory feedback method as a stand-alone method or to support visual feedback for placing the navigated medical instrument, in this case a needle. METHODS: An auditory synthesis model using pitch comparison and stereo panning parameter mapping was developed to augment or replace visual feedback for navigated needle placement. In contrast to existing approaches which augment but still require a visual display, this method allows view-free needle placement. An evaluation with 12 novice participants compared both auditory and combined audiovisual feedback against existing visual methods. RESULTS: Using combined audiovisual display, participants show similar task completion times and report similar subjective workload and accuracy while viewing the screen less compared to using the conventional visual method. The auditory feedback leads to higher task completion times and subjective workload compared to both combined and visual feedback. CONCLUSION: Audiovisual feedback shows promising results and establishes a basis for applying auditory feedback as a supplement to visual information to other navigated interventions, especially those for which viewing a patient is beneficial or necessary.
Ning Lipeng and Yogesh Rathi. 9/2017. “Dynamic Regression for Partial Correlation and Causality Analysis of Functional Brain Networks.” Int Conf Med Image Comput Comput Assist Interv. 20 (Pt1), Pp. 365-72.Abstract
We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the dynamic regression problem by minimizing the entropy of the associated stochastic process. We also provide the relation between the solutions and the linear dependence models of Geweke and Granger and derive novel expressions for computing partial correlation and causality using an optimal prediction filter with minimum error variance. We use the proposed dynamic framework to study the intrinsic partial correlation and causal- ity between seven different brain networks using resting state functional MRI (rsfMRI) data from the Human Connectome Project (HCP) and compare our results with those obtained from standard correlation and causality measures. The results show that our optimal prediction filter explains a significant portion of the variance in the rsfMRI data at low frequencies, unlike standard partial correlation analysis.
Yi Hong, Polina Golland, and Miaomiao Zhang. 9/2017. “Fast Geodesic Regression for Population-Based Image Analysis.” Int Conf Med Image Comput Comput Assist Interv. 20 (Pt1), Pp. 317-25.Abstract
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challenges for the current regression approaches and prohibits large scale studies. In this paper, we present a fast geodesic regression method that dramatically decreases the computational cost of the inference procedure while maintaining prediction accuracy. We employ an efficient low dimensional representation of diffeomorphic transformations derived from the image data and characterize the regressed trajectory in the space of diffeomorphisms by its initial conditions, i.e., an initial image template and an initial velocity field computed as a weighted average of pairwise diffeomorphic image registration results. This construction is achieved by using a first-order approximation of pairwise distances between images. We demonstrate the efficiency of our model on a set of 3D brain MRI scans from the OASIS dataset and show that it is dramatically faster than the state-of-the-art regression methods while producing equally good regression results on the large subject cohort.
Yongxin Chen, Tryphon T Georgiou, Lipeng Ning, and Allen Tannenbaum. 9/2017. “Matricial Wasserstein-1 Distance.” IEEE Control Syst Lett, 1, 1, Pp. 14-9.Abstract
We propose an extension of the Wasserstein 1-metric (W1) for density matrices, matrix-valued density measures, and an unbalanced interpretation of mass transport. We use duality theory and, in particular, a "dual of the dual" formulation of W1. This matrix analogue of the Earth Mover's Distance has several attractive features including ease of computation.
Yoshito Otake, Futishi Yokota, Norio Fukuda, Masaki Takao, Shu Takagi, Naoto Yamamura, Lauren O'Donnell, Westin Carl-Fredrik, Nobuhiko Sugano, and Yoshinobu Sato. 9/2017. “Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images.” Int Conf Med Image Comput Comput Assist Interv. 20 (Pt1), Pp. 656-63.Abstract
We propose an optimization method for estimating patient- specific muscle fiber arrangement from clinical CT. Our approach first computes the structure tensor field to estimate local orientation, then a geometric template representing fiber arrangement is fitted using a B- spline deformation by maximizing fitness of the local orientation using a smoothness penalty. The initialization is computed with a previously proposed algorithm that takes account of only the muscle’s surface shape. Evaluation was performed using a CT volume (1.0mm3/voxel) and high resolution optical images of a serial cryosection (0.1mm3/voxel). The mean fiber distance error at the initialization of 6.00 mm was decreased to 2.78mm after the proposed optimization for the gluteus maximus muscle, and from 5.28 mm to 3.09 mm for the gluteus medius muscle. The result from 20 patient CT images suggested that the proposed algorithm reconstructed an anatomically more plausible fiber arrangement than the previous method.
Yongxin Chen, Tryphon Georgiou, Michele Pavon, and Allen Tannenbaum. 9/2017. “Robust Transport over Networks.” IEEE Trans Automat Contr, 62, 9, Pp. 4675-82.Abstract
We consider transportation over a strongly connected, directed graph. The scheduling amounts to selecting transition probabilities for a discrete-time Markov evolution which is designed to be consistent with initial and final marginal constraints on mass transport. We address the situation where initially the mass is concentrated on certain nodes and needs to be transported in a certain time period to another set of nodes, possibly disjoint from the first. The random evolution is selected to be closest to a prior measure on paths in the relative entropy sense-such a construction is known as a Schrödinger bridge between the two given marginals. It may be viewed as an atypical stochastic control problem where the control consists in suitably modifying the prior transition mechanism. The prior can be chosen to incorporate constraints and costs for traversing specific edges of the graph, but it can also be selected to allocate equal probability to all paths of equal length connecting any two nodes (i.e., a uniform distribution on paths). This latter choice for prior transitions relies on the so-called Ruelle-Bowen random walker and gives rise to scheduling that tends to utilize all paths as uniformly as the topology allows. Thus, this Ruelle-Bowen law (Ruelle-Bowen Law) taken as prior, leads to a transportation plan that tends to lessen congestion and ensures a level of robustness. We also show that the distribution Ruelle-Bowen Law on paths, which attains the maximum entropy rate for the random walker given by the topological entropy, can itself be obtained as the time-homogeneous solution of a maximum entropy problem for measures on paths (also a Schrödinger bridge problem, albeit with prior that is not a probability measure). Finally we show that the paradigm of Schrödinger bridges as a mechanism for scheduling transport on networks can be adapted to graphs that are not strongly connected, as well as to weighted graphs. In the latter case, our approach may be used to design a transportation plan which effectively compromises between robustness and other criteria such as cost. Indeed, we explicitly provide a robust transportation plan which assigns maximum probability to minimum cost paths and therefore compares favourably with Optimal Mass Transportation strategies.
Zhang Fan, Wu Weining, Ning Lipeng, McAnulty Gloria, Waber Deborah, Borjan Gagoski, Sarill Kiera, Hamoda Hesham M, Song Yang, Cai Weidong, Yogesh Rathi, and Lauren J O'Donnell. 9/2017. “Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis.” Int Conf Med Image Comput Comput Assist Interv. 20 (Pt1), Pp. 556-65.Abstract
This work presents a supra-threshold fiber cluster (STFC) analysis that leverages the whole brain fiber geometry to enhance sta- tistical group difference analysis. The proposed method consists of (1) a study-specific data-driven tractography parcellation to obtain white matter (WM) tract parcels according to the WM anatomy and (2) a nonparametric permutation-based STFC test to identify significant dif- ferences between study populations (e.g. disease and healthy). The basic idea of our method is that a WM parcel’s neighborhood (parcels with similar WM anatomy) can support the parcel’s statistical significance when correcting for multiple comparisons. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder (ADHD) patients and 29 healthy controls (HCs). Evaluations are conducted using both synthetic and real data. The results indicate that our STFC method gives greater sensitivity in finding group differences in WM tract parcels compared to several traditional multiple comparison correction methods.
Lena Maier-Hein, Swaroop Vedula, Stefanis Speidel, Nassir Navab, Ron Kikinis, Matthias Eisenman, Hubertus Feussner, and Germain Forestier. 9/2017. “Surgical Data Science for Next-generation Interventions.” Nature Biomedical Engineering, 1, Pp. 691-6.
Anne-Katrin Giese, Markus D Schirmer, Kathleen L Donahue, Lisa Cloonan, Robert Irie, Stefan Winzeck, Mark JRJ Bouts, Elissa C McIntosh, Steven J Mocking, Adrian V Dalca, Ramesh Sridharan, Huichun Xu, Petrea Frid, Eva Giralt-Steinhauer, Lukas Holmegaard, Jaume Roquer, Johan Wasselius, John W Cole, Patrick F McArdle, Joseph P Broderick, Jordi Jimenez-Conde, Christina Jern, Brett M Kissela, Dawn O Kleindorfer, Robin Lemmens, Arne Lindgren, James F Meschia, Tatjana Rundek, Ralph L Sacco, Reinhold Schmidt, Pankaj Sharma, Agnieszka Slowik, Vincent Thijs, Daniel Woo, Bradford B Worrall, Steven J Kittner, Braxton D Mitchell, Jonathan Rosand, Polina Golland, Ona Wu, and Natalia S Rost. 8/2017. “Design and Rationale for Examining Neuroimaging Genetics in Ischemic Stroke: The MRI-GENIE Study.” Neurol Genet, 3, 5, Pp. e180.Abstract
OBJECTIVE: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. METHODS: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. CONCLUSIONS: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.