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Medical Image Computing and Computer-assisted Intervention: MICCAI ...

Gang Li, Li Wang, Weili Lin, Dinggang Shen
The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the individualized cortical parcellation based on each infant's own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Dingna Duan, Shunren Xia, Yu Meng, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li
The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Zhengwang Wu, Gang Li, Yu Meng, Li Wang, Weili Lin, Dinggang Shen
The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first, we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Guillermo Sapiro, Christophe Lenglet
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Anand A Joshi, Minqi Chong, Richard M Leahy
We describe a method that allows direct comparison of resting fMRI (rfMRI) time series across subjects. For this purpose, we exploit the geometry of the rfMRI signal space to conjecture the existence of an orthogonal transformation that synchronizes fMRI time series across sessions and subjects. The method is based on the observation that rfMRI data exhibit similar connectivity patterns across subjects, as reflected in the pairwise correlations between different brain regions. The orthogonal transformation that performs the synchronization is unique, invertible, efficient to compute, and preserves the connectivity structure of the original data for all subjects...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Nicolas Honnorat, Drew Parker, Birkan Tunç, Christos Davatzikos, Ragini Verma
Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Junyan Wang, Yonggang Shi
Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Wei Sun, Lilyana Amezcua, Yonggang Shi
To achieve improved understanding of white matter (WM) lesions and their effect on brain functions, it is important to obtain a comprehensive map of their connectivity. However, changes of the cellular environment in WM lesions attenuate diffusion MRI (dMRI) signals and make the robust estimation of fiber orientation distributions (FODs) difficult. In this work, we integrate techniques from image inpainting and compartment modeling to develop a novel method for enhancing FOD estimation in WM lesions from multi-shell dMRI, which is becoming increasingly popular with the success of the Human Connectome Project (HCP)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jin Kyu Gahm, Yonggang Shi
In brain shape analysis, the striatum is typically divided into three parts: the caudate, putamen, and accumbens nuclei for its analysis. Recent connectivity and animal studies, however, indicate striatum-cortical inter-connections do not always follow such subdivisions. For the holistic mapping of striatum surfaces, conventional spherical registration techniques are not suitable due to the large metric distortions in spherical parameterization of striatal surfaces. To overcome this difficulty, we develop a novel striatal surface mapping method using the recently proposed Riemannian metric optimization techniques in the Laplace-Beltrami (LB) embedding space...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Wenfeng Xia, Sacha Noimark, Sebastien Ourselin, Simeon J West, Malcolm C Finlay, Anna L David, Adrien E Desjardins
Ultrasound imaging is widely used for guiding minimally invasive procedures, including fetal surgery. Visualisation of medical devices such as medical needles is critically important and it remains challenging in many clinical contexts. During in-plane insertions, a needle can have poor visibility at steep insertion angles and at large insertion depths. During out-of-plane insertions, the needle tip can have a similar ultrasonic appearance to the needle shaft when it intersects with the ultrasound imaging plane...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jeffrey Glaister, Aaron Carass, Dzung L Pham, John A Butman, Jerry L Prince
The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI). Multi-atlas whole brain segmentation is performed using the T1-weighted MRI and the gray matter cerebrum labels are extended into the longitudinal fissure using fast marching to find an initial estimate of the falx...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Daniel Moyer, Boris A Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M Thompson
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each stream-line curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Takanori Watanabe, Birkan Tunc, Drew Parker, Junghoon Kim, Ragini Verma
In this paper, we present a novel method for obtaining a low dimensional representation of a complex brain network that: (1) can be interpreted in a neurobiologically meaningful way, (2) emphasizes group differences by accounting for label information, and (3) captures the variation in disease subtypes/severity by respecting the intrinsic manifold structure underlying the data. Our method is a supervised variant of non-negative matrix factorization (NMF), and achieves dimensionality reduction by extracting an orthogonal set of subnetworks that are interpretable, reconstructive of the original data, and also discriminative at the group level...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xiaohuan Cao, Yaozong Gao, Jianhua Yang, Guorong Wu, Dinggang Shen
Computed tomography (CT) is widely used for dose planning in the radiotherapy of prostate cancer. However, CT has low tissue contrast, thus making manual contouring difficult. In contrast, magnetic resonance (MR) image provides high tissue contrast and is thus ideal for manual contouring. If MR image can be registered to CT image of the same patient, the contouring accuracy of CT could be substantially improved, which could eventually lead to high treatment efficacy. In this paper, we propose a learning-based approach for multimodal image registration...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xiaobo Chen, Han Zhang, Dinggang Shen
Conventional functional connectivity (FC) and corresponding networks focus on characterizing the pairwise correlation between two brain regions, while the high-order FC (HOFC) and networks can model more complex relationship between two brain region "pairs" (i.e., four regions). It is eye-catching and promising for clinical applications by its irreplaceable function of providing unique and novel information for brain disease classification. Since the number of brain region pairs is very large, clustering is often used to reduce the scale of HOFC network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jie Yang, Elsa D Angelini, Pallavi P Balte, Eric A Hoffman, Colin O Wu, Bharath A Venkatesh, R Graham Barr, Andrew F Laine
Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans, using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method, when compared with several types of thresholding, provides more reproducible emphysema segmentation on repeated cardiac scans, and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yong Zhang, Sang Hyun Park, Kilian M Pohl
To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acquisition differences based on a control cohort before performing disease specific classification. The initial harmonization often mitigates group differences negatively impacting classification accuracy. To preserve cohort separation, we propose the first model unifying linear regression for data harmonization with a logistic regression for disease classification...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Long Xie, Laura E M Wisse, Sandhitsu R Das, Hongzhi Wang, David A Wolk, Jose V Manjón, Paul A Yushkevich
Quantification of medial temporal lobe (MTL) cortices, including entorhinal cortex (ERC) and perirhinal cortex (PRC), from in vivo MRI is desirable for studying the human memory system as well as in early diagnosis and monitoring of Alzheimer's disease. However, ERC and PRC are commonly over-segmented in T1-weighted (T1w) MRI because of the adjacent meninges that have similar intensity to gray matter in T1 contrast. This introduces errors in the quantification and could potentially confound imaging studies of ERC/PRC...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Erdem Varol, Aristeidis Sotiras, Christos Davatzikos
Computer assisted imaging aims to characterize disease processes by contrasting healthy and pathological populations. The sensitivity of these analyses is hindered by the variability in the neuroanatomy of the normal population. To alleviate this shortcoming, it is necessary to define a normative range of controls. Moreover, elucidating the structure in outliers may be important in understanding diverging individuals and characterizing prodromal disease states. To address these issues, we propose a novel geometric concept called minimal convex polytope (MCP)...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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