798 citations · 845 across the 9 of their papers we have counts for
9 papers
Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma +7
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging…
XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans
Lavsen Dahal, Mobina Ghojoghnejad, Dhrubajyoti Ghosh +10
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and…
Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection
Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe +16
Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a so…
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
Kaouther Mouheb, Mobina Ghojogh Nejad, Lavsen Dahal +4
Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challe…
The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Data
Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima +4
Purpose: The credibility of Artificial Intelligence (AI) models for medical imaging continues to be a challenge, affected by the diversity of models, the data used to train the mod…
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima +5
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with…
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT
Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu +5
Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in terms of quality (because labels cover only a few…
TransMorph: Transformer for unsupervised medical image registration
Junyu Chen, Eric C. Frey, Yufan He +3
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by…
Generation of annotated multimodal ground truth datasets for abdominal medical image registration
Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch +5
Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical…