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Quantitative Assessment of Coronary Plaques by Motion-Compensated PET-MRI

PIs: Schäffter, Brenner, Makowski/Flöel

Application Area: Cardiovascular

Modality: PET-MRI

Background: Coronary plaques are the main cause of impaired tissue perfusion and subsequent myocardial infarction. Early detection of plaques likely to rupture could enable initiation of preventive treatment before stroke or infarction occurs. PET imaging using ¹⁸F-sodium fluoride (¹⁸F-NaF) has been shown to allow detection and assessment of coronary plaques. ¹⁸F-NaF accumulates in micro calcifications of plaques in the coronary arteries and identifies these high-risk plaques with high specificity. However, the diagnostic quality of ¹⁸F-NaF PET can be strongly impaired by physiological patient motion. Respiratory and cardiac motion lead to very large displacements of the coronary arteries compared to their diameter. Therefore, the PET signal of these small structures gets strongly blurred and cannot be distinguished from the background tissue anymore. PET-MR systems possess the ability to truly simultaneously acquire data of the two modalities. MRI offers a high spatial resolution in combination with an excellent soft tissue contrast which allows for extracting information on patient motion during the PET scan. This establishes the basis to use the complementary information provided by the MRI to improve the quality of the PET data.

Aim: Development of a more accurate and more reproducible quantification of risks posed by coronary plaques using PET-MR.

Methods: During this project, a novel high-resolution 3D MRI acquisition technique will be developed to obtain accurate cardiac and respiratory motion information. Image registration algorithms will be assessed to estimate the cardiac and respiratory motion of the heart. These will be incorporated into MR and PET image reconstruction using patient specific motion models. In particular, the project will focus on reconstruction methods making use of the entirety of the combined PET and MR data to mutually improve the image quality. The developed methods will be evaluated in simulations and studies with healthy volunteers and patients.