Model-Based Reconstruction for Highly Accelerated First-Pass Perfusion Cardiac MRI

TY - CHAP

T1 - Model-Based Reconstruction for Highly Accelerated First-Pass Perfusion Cardiac MRI

AU - Correia, Teresa

AU - Schneider, Torben

AU - Chiribiri, Amedeo

PY - 2019/1/1

Y1 - 2019/1/1

N2 - First-pass perfusion cardiac magnetic resonance (FPP-CMR) allows the assessment of coronary heart disease. However, conventional FPP-CMR suffers from low spatial resolution, insufficient heart coverage and requires long breath-holds. At present, perfusion abnormalities are usually identified visually by highly trained physicians. Recently, quantitative analysis of FPP-CMR has emerged as a more reliable and operator-independent approach for identifying perfusion defects. Typically, quantitative FPP-CMR first reconstructs individual dynamic images, which are then converted to contrast agent concentration, and finally, tracer-kinetic modeling is used to generate quantitative myocardial perfusion maps. Here, we propose a model-based FPP-CMR reconstruction approach, which combines image reconstruction and tracer-kinetic modeling, to better exploit the redundancies in the FPP-CMR data. We show that such synergistic approach enables very high undersampling rates at each time frame, and thus allows for much higher spatial resolution and coverage than the traditional method. Furthermore, our proposed method can be combined with respiratory motion correction and k-t undersampling to improve myocardial perfusion quantification, while substantially increasing patient comfort.

AB - First-pass perfusion cardiac magnetic resonance (FPP-CMR) allows the assessment of coronary heart disease. However, conventional FPP-CMR suffers from low spatial resolution, insufficient heart coverage and requires long breath-holds. At present, perfusion abnormalities are usually identified visually by highly trained physicians. Recently, quantitative analysis of FPP-CMR has emerged as a more reliable and operator-independent approach for identifying perfusion defects. Typically, quantitative FPP-CMR first reconstructs individual dynamic images, which are then converted to contrast agent concentration, and finally, tracer-kinetic modeling is used to generate quantitative myocardial perfusion maps. Here, we propose a model-based FPP-CMR reconstruction approach, which combines image reconstruction and tracer-kinetic modeling, to better exploit the redundancies in the FPP-CMR data. We show that such synergistic approach enables very high undersampling rates at each time frame, and thus allows for much higher spatial resolution and coverage than the traditional method. Furthermore, our proposed method can be combined with respiratory motion correction and k-t undersampling to improve myocardial perfusion quantification, while substantially increasing patient comfort.

KW - Model-based reconstruction

KW - Quantitative perfusion cardiac MRI

KW - Tracer-kinetic parameter mapping

UR - http://www.scopus.com/inward/record.url?scp=85075652450&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32245-8_57

DO - 10.1007/978-3-030-32245-8_57

M3 - Conference paper

AN - SCOPUS:85075652450

SN - 9783030322441

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 514

EP - 522

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - SPRINGER

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -