Accelerated CMR using zonal, parallel and prior knowledge driven imaging methods

Accelerated imaging is highly relevant for many CMR applications as competing constraints with respect to spatiotemporal resolution and tolerable scan times are frequently posed. Three approaches, all involving data undersampling to increase scan efficiencies, are discussed in this review. Zonal imaging can be considered a niche but nevertheless has found application in coronary imaging and CMR flow measurements. Current work on parallel-transmit systems is expected to revive the interest in zonal imaging techniques. The second and main approach to speeding up CMR sequences has been parallel imaging. A wide range of CMR applications has benefited from parallel imaging with reduction factors of two to three routinely applied for functional assessment, perfusion, viability and coronary imaging. Large coil arrays, as are becoming increasingly available, are expected to support reduction factors greater than three to four in particular in combination with 3D imaging protocols. Despite these prospects, theoretical work has indicated fundamental limits of coil encoding at clinically available magnetic field strengths. In that respect, alternative approaches exploiting prior knowledge about the object being imaged as such or jointly with parallel imaging have attracted considerable attention. Five to eight-fold scan accelerations in cine and dynamic CMR applications have been reported and image quality has been found to be favorable relative to using parallel imaging alone

Application of kt-BLAST acceleration to reduce cardiac MR imaging time in healthy and infarcted mice

OBJECT: We evaluated the use of kt-broad-use linear acquisition speed-up technique (kt-BLAST) acceleration of mouse cardiac imaging in order to reduce scan times, thereby minimising physiological variation and improving animal welfare. MATERIALS AND METHODS: Conventional cine cardiac MRI data acquired from healthy mice (n = 9) were subsampled to simulate kt-BLAST acceleration. Cardiological indices (left ventricular volume, ejection fraction and mass) were determined as a function of acceleration factor. kt-BLAST threefold undersampling was implemented on the scanner and applied to a second group of mice (n = 6 healthy plus 6 with myocardial infarct), being compared with standard cine imaging (3 signal averages) and cine imaging with one signal average. RESULTS: In the simulations, sufficient accuracy was achieved for undersampling factors up to three. Cardiological indices determined from the implemented kt-BLAST scanning showed no significant differences compared with the values determined from the standard sequence, and neither did indices derived from the cine scan with only one signal average despite its lower signal-to-noise ratio. Both techniques were applied successfully in the infarcted hearts. CONCLUSION: For cardiac imaging of mice, threefold undersampling of kt-space, or a similar reduction in the number of signal averages, are both feasible with subsequent reduction in imaging time

Analytic image concept combined to SENSE reconstruction

Two approaches of reconstructing undersampled partial k-space data, acquired with multiple coils are compared: homodyne detection combined with SENSE (HM_SENSE) and analytic image reconstruction combined with SENSE (AI_SENSE). The latter overcomes limitations of HM_ SENSE by considering aliased images as analytic thus avoiding the need for phase correction required for HM_SENSE. MATERIALS AND METHODS: In vivo imaging experiments were carried out in male Lewis rats using both gradient echo and spin echo sequences. Accelerated images obtained by using the various reconstruction algorithms were compared to fully sampled reference images both qualitatively and quantitatively. RESULTS: For the various sampling patterns evaluated, both HM_SENSE and AI_SENSE were found to yield robust image reconstruction with small deviations from the reference image. Even for high acceleration factors AI_SENSE still provided useful results and was found superior compared to HM_SENSE. CONCLUSION: Combination of partial k-space sampling and parallel image acquisition allows for further acceleration of data acquisition as compared to each method alone. Image reconstruction from undersampled data sets using the AI_SENSE algorithm was found to considerably reduce reconstruction errors and artifacts observed for HM_SENSE reconstruction caused by errors in phase estimation