| Cardiac magnetic resonance imaging perfusion | |
| Purpose: | test performed on patients with known or suspected coronary artery disease |
| Synonyms: | Stress CMR perfusion |
Cardiac magnetic resonance imaging perfusion (cardiac MRI perfusion, CMRI perfusion), also known as stress CMR perfusion,[1] is a clinical magnetic resonance imaging test performed on patients with known or suspected coronary artery disease to determine if there are perfusion defects in the myocardium of the left ventricle that are caused by narrowing of one or more of the coronary arteries.
CMR perfusion is increasingly used in cardiac imaging to test for inducible myocardial ischaemia and has been well validated against other imaging modalities such as invasive angiography[2] [3] or FFR. Several recent large-scale studies have shown non-inferiority or superiority to SPECT imaging. It is becoming increasingly established as a marker of prognosis in patients with coronary artery disease.[4] [5]
The images are stored as video files and are analysed on a dedicated workstation. The majority of clinical scans are analysed qualitatively by visually comparing the stress and rest scans in parallel. In a normal scan, the wash in (1st pass) of gadolinium into the myocardium can be seen as the myocardium turning from black to mid grey uniformly throughout the whole of the left ventricle in both the stress and rest scans. In an abnormal scan an area of the myocardium will turn grey slower than the surrounding tissue as the blood (and hence gadolinium) enters more slowly due to a narrowing of the coronary artery supplying it. This is called a perfusion defect and usually represents myocardial ischaemia. It may be seen on both the rest and stress scans in which case it is called a matched perfusion defect and is probably due to an area or scar from a previous myocardial infarction. If it is only seen on the stress scan it is called an area of inducible perfusion defect (ischaemia). The position in the left ventricle of the perfusion defects are described using the AHA 17 segment model.[6]