Synopsis

Magnetic resonance imaging of myocardial perfusion continues to challenge current limits for fast dynamic imaging to provide sufficient spatial and temporal resolution for accurate detection perfusion defects, and enable almost complete coverage of the left ventricle. The technical capabilities of MR cardiac perfusion imaging impact the clinical use of this technique for the detection of ischemic heart disease, and its relative importance compared to other imaging modalities and tests. Recent studies have shown that cardiac magnetic resonance perfusion imaging provides strong prognostic value for predicting adverse events.

Introduction

• In cardiac MR (CMR) imaging, contrast-enhancement “first pass” perfusion imaging continues to be the most widely applied technique for assessing myocardial perfusion.[1-3]
  
• Arterial spin labeling is feasible in the heart, and some initial validation studies have been performed.[4]
  
• Indirect measures of the coronary vasodilator response have been tested for detection of ischemia in research studies (e.g. T1 mapping, magnetization transfer). [5-7]
  
• Stress myocardial perfusion by CMR improves risk prediction. [8]

Technical Approaches for First Pass Imaging

• T1-weighting of signal by magnetization preparation - mostly saturation-recovery is used for heart rate independence of T1 enhancement.
• Image-update rate equivalent to patient’s heart rate, in particular under cardiac stress conditions
• For clinical application, multi-slice 2D perfusion imaging continues to be the standard – 3D imaging only feasible with very high imaging acceleration factors.[9]

• higher in-plane spatial resolution[10]
• more complete ventricular coverage
• reduction or mitigation of artifacts (“dark rim”) [11, 12]

• Parallel imaging acceleration, which is generally limited to acceleration factors of x 2-3 because of the relatively low contrast-to-noise in multi-slice 2D perfusion imaging [13, 14]
• Temporal-spatial acceleration schemes like T-SENSE [15, 16], k-t BLAST, k-t PCA[17], which allow acceleration factors up to x10 or even higher by undesampling in the k-t domain.
• Acceleration by compressed sensing with parallel imaging (e.g. k-t SPARSE) [18-20]

• Radial sampling also used extensively with under-sampling and iterative reconstruction using constraints like total variation (suppression of streaking artifacts from radial undersampling)[24].
• Motion during image acquisition and relaxation recovery may be additional sources of artifacts during peak enhancement in blood pool.

• Correction for in-plane breathing motion (“MOCO”) [25-28]
• Currently examination of the images is the standard for clinical interpretation but use of parametric maps may become a useful aid.
• Emerging roles of machine learning for cardiac perfusion imaging [29, 30]

• 2D multi-slice imaging during entire cardiac cycle, or 3D imaging limited to diastole?
• Use of on-gadolinium based contrast agents?

Clinical Applications

• Requires assessment during stress (generally pharmacological vasodilator stress). Rest perfusion is used as reference and is not generally thought useful for detection of myocardial ischemia.
• Clinical usage has been limited to qualitative interpretation of perfusion scans. Evidence that perfusion quantification is useful beyond the setting of research studies still not solid to warrant clinical adoption.
• Clinical use of quantitative markers is helped by automated, “in-line” pixel-wise perfusion mapping.

Acknowledgements

References

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