Meeting Banner
Abstract #1779

MRI super-resolution reconstruction: A patient-specific and dataset-free deep learning approach

Yao Sui1,2, Onur Afacan1,2, Ali Gholipour1,2, and Simon K Warfield1,2
1Harvard Medical School, Boston, MA, United States, 2Boston Children's Hospital, Boston, MA, United States

Spatial resolution is critically important in MRI. Unfortunately, direct high-resolution acquisition is time-consuming and suffers from reduced signal-to-noise ratio. Deep learning-based super-resolution has emerged to improve MRI resolution. However, current methods require large-scale training datasets of high-resolution images, which are difficult to obtain at suitable quality. We developed a deep learning technique that trains the model on the patient-specific low-resolution data, and achieved high-quality MRI at a resolution of 0.125 cubic mm with six minutes of imaging time. Experiments demonstrate our approach achieved superior results to state-of-the-art super-resolution methods, while reduced scan time as delivered with direct high-resolution acquisitions.

This abstract and the presentation materials are available to members only; a login is required.

Join Here