State-of-the-Art of Myocardial Perfusion by CMR: A Practical View

Area of Science:

• Cardiovascular Imaging
• Medical Diagnostics
• Radiology

Background:

• Ischemic heart disease (IHD) poses a significant public health challenge, necessitating accurate diagnosis and prognostic stratification.
• While coronary angiography is established, assessing myocardial necrosis, function, and inducible ischemia is crucial for comprehensive patient management.
• Cardiovascular Magnetic Resonance (CMR) is a multiparametric imaging technique vital for evaluating these critical aspects of IHD.

Purpose of the Study:

• To review the application of myocardial perfusion CMR sequences for detecting and grading inducible ischemia.
• To highlight the advancements in quantitative perfusion (QP) CMR, particularly its automated dual-sequence strategy.
• To discuss the integration of automated QP CMR into diagnostic workflows for IHD patients.

Main Methods:

• Utilizing a dual-sequence strategy for signal intensity conversion to contrast agent concentration.
• Implementing automated processes for myocardial blood flow (MBF) calculation, pixel-wise flow mapping, and myocardial segmentation using machine learning.
• Acquiring perfusion data at both vasodilator stress and rest to determine stress/rest MBF and myocardial perfusion reserve (MPR).

Main Results:

• Quantitative perfusion (QP) provides automated calculation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR).
• Dual-sequence QP has demonstrated successful validation against reference methods and significant prognostic value in large studies.
• The automated nature of QP eliminates operator dependency, enabling seamless integration into automated diagnostic workflows.

Conclusions:

• Automated quantitative perfusion CMR is a significant advancement for assessing inducible ischemia in ischemic heart disease.
• This technique offers reliable and validated measurements of myocardial blood flow and perfusion reserve.
• The automation facilitates integration into future fully automated diagnostic workflows for IHD management.