Image Reconstruction for Multicoil Low-field MRI

Abstract

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality that is commonly used in the clinic today. However, it is an expensive technique. The high purchase, operational and maintenance costs, as well as the need for trained staff with technical expertise, put this technique out of reach for a large part of the world population. To combat this, low-cost MRI systems are being developed. The use of lower magnetic fields allows for reductions in cost and size, increasing the accessibility and portability of the device in developing countries. Nevertheless, the signal-to-noise ratio is proportional to the magnetic field, and so low-field MR images are of a significantly lower quality. As such, a reconstruction algorithm is necessary in order to denoise the image, while preserving the details. Besides the cost, MRI is a relatively slow modality, leading to decreased patient comfort and increased chance of motion artifacts. The data acquisitions can be sped up through Parallel Imaging, which requires the use of a receiver coil array rather than a single RF coil. This leads to incomplete data as well as spatial variations in the coil sensitivity profiles that must be accounted for. Furthermore, while the Field-of-View of the image is generally larger than the spatial support of the object, information on this support is not used in MR image reconstructions. In this work, a reconstruction algorithm is developed to reconstruct MR images using incomplete multicoil data and spatial support information in a low-field setting. This algorithm is based on the single coil algorithm of De Leeuw Den Bouter et al. (2021), which will be extended to the multicoil case using the Contrast Source Inversion method. Three adaptations of the algorithm have been considered. Their performance is characterized in terms of denoising ability, edge-preservation and execution time, using both simulated and real low-field data. A mask S is introduced to include information on the spatial support. The use of this mask is shown to have a positive effect on the quality of the reconstruction, improving the preservation of edges and details. Furthermore, a mask R is introduced to deal with incomplete data in case of accelerated acquisitions. In conjuction with a Parallel Imaging technique, the reconstruction algorithm can yield de-aliased and denoised reconstructions, combatting the inherent drop in SNR caused by the acceleration. Chances for improving the algorithms remain, for instance by the exploring the possibilities for a multiplicative implementation of the Total Generalized Variation regularization term, as well as the automatic detection of the spatial support and the inclusion of compressed sensing.