Andrew D. Scott

Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living heart, providing...

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation

Stain variation is a unique challenge associated with automated analysis of digital pathology. Numerous methods have been developed to improve the robustness of machine learning methods to stain...

Is Fitting Error a Reliable Metric for Assessing Deformable Motion Correction in Quantitative MRI?

Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction

Generating Short-Axis DENSE Images from 4D XCAT Phantoms: A Proof-of-Concept Study

The Effect of Temporal Variations in Myocardial Perfusion on Diffusion Tensor Measurements

Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI

Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions

RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement

Cardiac diffusion tensor imaging (DTI) offers unique insights into cardiomyocyte arrangements, bridging the gap between microscopic and macroscopic cardiac function. However, its clinical utility...

Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor...