A survey on deep learning in medical image reconstruction

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained...

A Dictionary Learning Approach for Joint Reconstruction and Denoising in Low Field Magnetic Resonance Imaging

Currently, many children with hydrocephalus in East Africa and other resource-constrained countries do not have access to Magnetic Resonance Imaging (MRI) scanners, the preferred imaging tool during the disease administration and treatment. Conventional MRI scanners are costly to buy and manage, which limits their utilization in low-income...

Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging

Background: Magnetic resonance imaging (MRI) is a safe non-invasive and nonionizing medical imaging modality that is used to visualize the structure of human anatomy. Conventional (high-field) MRI scanners are very expensive to purchase, operate and maintain, which limit their use in many developing countries. This study is part of a project...

A Dictionary Learning Approach for Noise-Robust Image Reconstruction in Low-Field Magnetic Resonance Imaging

Objective: Image denoising has been considered as a separate procedure from image reconstruction which could otherwise be combined with acquisition and reconstruction. This paper discusses a joint image reconstruction and denoising algorithm in low-field MRI using a dictionary learning approach. Method: Our proposed algorithm uses a two-level...