| 1-R01-EB029431-01A1 |
Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results |
Deshan Yang |
Washington University |
| 1-R01-EB032825-01A1 |
Artificial Intelligence powered virtual digital twins to construct and validate AI automated tools for safer MR-guided adaptive RT of abdominal cancers |
Neelam Tyagi |
Sloan-Kettering Inst Can Research |
| 1-R01-EB033773-01A1 |
Fast and Robust Deep Learning for Medical imaging: Segmentation and Registration methods invariant to contrast and resolution |
Adrian Dalca |
Massachusetts General Hospital |
| 1-R01-EB033782-01A1 |
Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis |
Tyler Bradshaw |
University of Wisconsin-Madison |
| 1-R01-EB034231-01 |
Full-stack automation for reliable and reproducible MRS of brain cancer |
Malgorzata Marjanska |
University of Minnesota |
| 1-R01-EB034261-01A1 |
Advancing three-dimensional preclinical dynamic contrast-enhanced photoacoustic computed tomography via quantitative image reconstruction |
Umberto Villa |
University of Texas at Austin |
| 1-R01-EB034517-01A1 |
Development of a new weighted ROC (WROC) analysis - Resubmission - 1 |
Yulei Jiang |
University of Chicago |
| 1-R01-EB035092-01 |
PixelPrint: a 3D printing platform for creating lifelike patient-based CT phantoms |
Peter Noël |
University of Pennsylvania |
| 1-R01-EB035103-01 |
Data Driven Background Estimation in PET |
Suleman Surti |
University of Pennsylvania |
| 1-R01-EB035160-01 |
AI-Powered MRI Quality Control and Artifact Correction for Multi-Site Studies |
Pew-Thian Yap |
Univ of North Carolina Chapel Hill |
