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

Teresa Correia*, Torben Schneider, Amedeo Chiribiri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Number of pages9
ISBN (Print)9783030322441
Publication statusPublished - 1 Jan 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11765 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019


  • Model-based reconstruction
  • Quantitative perfusion cardiac MRI
  • Tracer-kinetic parameter mapping


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