3 papers
cs.CV2026
Training deep learning based dynamic MR image reconstruction using synthetic fractals
Anirudh Raman, Olivier Jaubert, Mark Wrobel +8
Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, li…
eess.IV2025
High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
Mark Wrobel, Michele Pascale, Tina Yao +5
Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine…
cs.CV2025
MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
Tina Yao, Nicole St. Clair, Madeline Gong +5
We present a deep learning framework with two models for automated segmentation and large-scale flow phenotyping in a registry of single-ventricle patients. MultiFlowSeg simultaneo…