Quantitative myocardial perfusion imaging using different autocalibrated parallel acquisition techniques

PURPOSE: To compare three different autocalibrated parallel acquisition techniques (PAT) for quantitative and semiquantitative myocardial perfusion imaging. MATERIALS AND METHODS: Seven healthy volunteers underwent myocardial first-pass perfusion imaging at rest using an SR-TrueFISP pulse sequence without PAT and while using GRAPPA, mSENSE, and TSENSE. signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), normalized upslopes (NUS), and myocardial blood flow (MBF) were calculated. Artifacts, image noise, and overall image quality were qualitatively assessed. Furthermore, the relation between signal intensity (SI) and contrast medium (CM) concentration was determined in phantoms. RESULTS: Using PAT the linear range of the SR-TrueFISP sequence was increased about 40%. All three PAT methods introduced significant loss in SNR and CNR. GRAPPA yielded slightly better values then mSENSE and TSENSE. Both SENSE techniques introduced significantly residual aliasing artifacts. Image noise was increased with all three PAT methods. However, overall image quality was reduced only with mSENSE. Even though GRAPPA yielded smaller NUS values than non-PAT, mSENSE, and TSENSE, no differences were found in MBF between all applied techniques. CONCLUSION: Quantitative and semiquantitative myocardial perfusion imaging can benefit from PAT due to shorter acquisition times and increased linearity of the pulse sequence. GRAPPA and TSENSE turned out to be well suited for quantitative myocardial perfusion imaging.