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. How ever, 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) acqui sition 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 ac curacy 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.

Respiratory motion-resolved, self-gated 4D-MRI using rotating cartesian k-space (ROCK)

“…Odille et al first proposed to simultaneously estimate the images and the motion model by solve two coupled inverse problems. Several recent works combined this approach with compressed sensing and non‐Cartesian sampling, and were able to reconstruct images based on up to 16X undersampled k‐space.…”

Self-navigated 4D cartesian imaging of periodic motion in the body trunk using partial k-space compressed sensing

“…The Cartesian acquisition uniformly subsamples the high‐frequency components (in contrast to, e.g., radial sampling) and allows performing a partial Fourier–like compression (ESPReSSo) by which the high‐frequency components are sampled denser. This yields a high tissue boundary integrity that is desired for accurate motion detection . Furthermore, a regridding in the reconstruction, which can result in undesirable streaking artifacts, can be avoided.…”

Low‐rank reconstruction for simultaneous double half‐echo 23Na and undersampled 23Na multi‐quantum coherences MRI

“…DHE provided higher‐resolution sodium images, whereas MQC focused on tissue sodium characterization at a lower spatial resolution. To accelerate the 23 Na MQC acquisition and improve its spatial resolution, the phase‐encoding lines of the 23 Na MQC data were retro‐ and prospectively undersampled with Cartesian 3D ky‐kz undersampling patterns following a Poisson distribution that were varied for each phase‐cycling step 28 . The acquisition scheme leveraging the three‐pulse MQC sequence and the low‐rank reconstruction workflows are shown in Figure 1.…”