bmistry12 / fastmri-reconstruction

University Of Birmingham, Final Year Neural Computation Assignment

FastMRI - Neural Computation Group Project

Long acquisition times in fully-sampled MRI lead to low patient throughput, problems with patient comfort and compliance, artefacts from patient motion, and high examination costs. Reducing acquisition time using under-sampling acceleration rates (4 fold and 8 fold masks), helps to mitigate these issues, however, this comes at the const of reconstructed image quality.

The project aims to improve the viability of the more efficient under-sampling strategy by designing a system that maximises the quality of under-sampled reconstructions. High-quality reconstructions are images that closely align with the ground truth, the fully-sampled image. SSIM (Structural Similarity Index Measure) is used to evaluate the success of the model's output against the ground truth image.

The model implemented was a U-Net, with RMSprop as an optimizer. The majority of this project consisted of experimenting with various hyperparameters and features, to gain a better understanding of the fundamental components and concepts of a neural network.

fastmri-reconstruction's Issues

Dataset

Hello,
I apologize for the inconvenience. I am a master student in Artificial Intelligence and Robotics. I saw your project and I found it very interesting! I’m doing a project for a course that is based on MRI reconstruction and I don’t have a dataset and I’m searching for it. In Github is not available, can you share it with me please?
Thanks in advance.
Antonio