6 citations · 12 across the 16 of their papers we have counts for
17 papers
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
Zahra Khodakarami, Yue Li, Pulkit Khandelwal +5
White matter hyperintensities (WMH), bright regions on Fluid-attenuated Inversion Recovery (FLAIR) scans are associated with cerebrovascular pathology and neurodegeneration. FLAIR…
Automated Optical Density Normalization for Myelin Quantification: Cross-Modal Validation with 7T Ex Vivo MRI
Zahra Khodakarami, Sheina Emrani, Pulkit Khandelwal +21
White matter hyperintensities (WMH) are bright regions on T2-weighted magnetic resonance imaging (MRI) scans and are associated with cerebrovascular pathology and neurodegeneration…
ICHOR: A Robust Representation Learning Approach for ASL CBF Maps with Self-Supervised Masked Autoencoders
Xavier Beltran-Urbano, Yiran Li, Xinglin Zeng +10
Arterial spin labeling (ASL) perfusion MRI allows direct quantification of regional cerebral blood flow (CBF) without exogenous contrast, enabling noninvasive measurements that can…
Achieving detailed medial temporal lobe segmentation with upsampled isotropic training from implicit neural representation
Yue Li, Pulkit Khandelwal, Rohit Jena +8
Imaging biomarkers in magnetic resonance imaging (MRI) are important tools for diagnosing, tracking and treating Alzheimer's disease (AD). Neurofibrillary tau pathology in AD is cl…
Imaging Biomarkers for Neurodegenerative Diseases from Detailed Segmentation of Medial Temporal Lobe Subregions on in vivo Brain MRI Using Upsampling Strategy Guided by High-resolution ex vivo MRI
Yue Li, Pulkit Khandelwal, Long Xie +21
The medial temporal lobe (MTL) is a region impacted extensively and non-uniformly in early stages of Alzheimer's disease (AD). Regional MTL morphometric measures extracted from mag…
Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias
Pulkit Khandelwal, Michael Tran Duong, Lisa Levorse +20
Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led t…
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI
Mengjin Dong, Long Xie, Sandhitsu R. Das +5
Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical…
Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation
Sadhana Ravikumar, Ranjit Ittyerah, Sydney Lim +29
When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphomet…
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
Pulkit Khandelwal, Michael Tran Duong, Shokufeh Sadaghiani +34
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for bra…
Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres
Pulkit Khandelwal, Shokufeh Sadaghiani, Michael Tran Duong +30
Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy. However, automated cortical segmentation methods…
NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration
Yifan Wu, Tom Z. Jiahao, Jiancong Wang +3
Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this pap…
Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline
Long Xie, Laura E. M. Wisse, Jiancong Wang +6
Deep learning (DL) is the state-of-the-art methodology in various medical image segmentation tasks. However, it requires relatively large amounts of manually labeled training data,…
DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease
Mengjin Dong, Long Xie, Sandhitsu R. Das +5
Volume change measures derived from longitudinal MRI (e.g. hippocampal atrophy) are a well-studied biomarker of disease progression in Alzheimer's Disease (AD) and are used in clin…
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging
Pulkit Khandelwal, Paul Yushkevich
Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be…
Barnes-Hut Approximation for Point SetGeodesic Shooting
Jiancong Wang, Long Xie, Paul Yushkevich +1
Geodesic shooting has been successfully applied to diffeo-morphic registration of point sets. Exact computation of the geodesicshooting between point sets, however, requiresO(N2) c…
Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool
Ludovic Venet, Sarthak Pati, Paul Yushkevich +1
Variously stained histology slices are routinely used by pathologists to assess extracted tissue samples from various anatomical sites and determine the presence or extent of a dis…
Diffeomorphic Medial Modeling
Paul A. Yushkevich, Ahmed Aly, Jiancong Wang +4
Deformable shape modeling approaches that describe objects in terms of their medial axis geometry (e.g., m-reps [Pizer et al., 2003]) yield rich geometrical features that can be us…