torchsense – a PyTorch-based Compressed Sensing reconstruction framework for dynamic MRI

Type: MA thesis

Status: finished

Date: April 15, 2021 - October 15, 2021

Supervisors: Fasil Gadjimuradov, Andreas Maier, Oliver Cossairt (Northwestern University), Florian Schiffers (Northwestern University)

In this master thesis a novel deep learning-based reconstruction method specifically tailored for cardiac radial cine MRI image sequences is investigated. Despite the many advantages presented by state-of-the-art unrolled networks, their applicability is limited due to integration of the forward operator into the scheme which poses a computational challenge within the scope of dynamic non-Cartesian MRI. The novelty of our algorithm constitutes the decoupling of regularization and data consistency enforcement into two separate steps that can be combined into an end-to-end reconstruction scheme which reduces the usage of the forward operator and, thereby, offers more flexibility. In contrast to unrolled networks, the regularization step will be achieved by a lightweight denoising CNN, in some cases leading to a closed-form solution of the data-consistency step.

Utilizing the flexibility (e.g., variable network length at test time), we will seek to increase the undersampling ratio of the k-space, thereby, allowing a higher temporal resolution using an existing acquisition scheme.