Comparison of iterative parametric and indirect deep learning-based reconstruction methods in highly undersampled DCE-MR Imaging of the breast.

Journal: Medical physics

Published Date: Sep 6, 2020

Abstract

PURPOSE: To compare the performance of iterative direct and indirect parametric reconstruction methods with indirect deep learning-based reconstruction methods in estimating tracer-kinetic parameters from highly undersampled DCE-MR Imaging breast data and provide a systematic comparison of the same.

Authors

Aditya Rastogi

Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, India.
Phaneendra K Yalavarthy

Indian Institute of Science, Department of Computational and Data Sciences, Bangalore, Karnataka, India.

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Kinetics Magnetic Resonance Imaging Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (32780871)

Comparison of iterative parametric and indirect deep learning-based reconstruction methods in highly undersampled DCE-MR Imaging of the breast.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals