Combining Compressed Sensing with motion correction in acquisition and reconstruction for PET/MR

Abstract

In the field of oncology, simultaneous Positron-Emission-Tomography/Magnetic Resonance (PET/MR) scanners offer a great potential for improving diagnostic accuracy. However, to achieve a high Signal-to-Noise Ratio (SNR) for an accurate lesion detection and quantification in the PET/MR images, one has to overcome the induced respiratory motion artifacts. The simultaneous acquisition allows performing a MR-based non-rigid motion correction of the PET data. It is essential to acquire a 4D (3D + time) motion model as accurate and fast as possible to minimize additional MR scan time overhead. Therefore, a Compressed Sensing (CS) acquisition by means of a variable-density Gaussian subsampling is employed to achieve high accelerations. Reformulating the sparse reconstruction as a combination of the inverse CS problem with a non-rigid motion correction improves the accuracy by alternately projecting the reconstruction results on either the motion-compensated CS reconstruction or on the motion model optimization. In-vivo patient data substantiates the diagnostic improvement.