Tammy Riklin Raviv (Ben-Gurion University)
Tuesday, 15.6.2021, 11:30
A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing operational costs, faster scans are essential for time-sensitive imaging, such as fetal, cardiac, or functional MRI, where temporal resolution is important and target movement is unavoidable, yet must be reduced. Current MRI acquisition methods speed up scan time at the expense of lower spatial resolution and costlier hardware. We introduce a practical, software-only framework, based on deep learning, for accelerating MRI acquisition, while maintaining anatomically meaningful imaging. This is accomplished by MRI subsampling followed by estimating the missing k-space samples via generative adversarial neural networks. A generator-discriminator interplay enables the introduction of an adversarial cost in addition to fidelity and image-quality losses used for optimizing the reconstruction.
Promising reconstruction results are obtained from feasible sampling patterns of up to a fivefold acceleration of diverse brain MRIs, from a large publicly available dataset of healthy adult scans as well as multimodal acquisitions of multiple sclerosis patients and dynamic contrast-enhanced MRI (DCE-MRI) sequences of stroke and tumor patients. Clinical usability of the reconstructed MRI scans is assessed by performing either lesion or healthy tissue segmentation and comparing the results to those obtained by using the original, fully sampled images. Reconstruction quality and usability of the DCE-MRIsequences is demonstrated by calculating the pharmacokinetic (PK) parameters. The proposed MRI reconstruction approach is shown to outperform state-of-the-art methods for all datasets tested in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), as well as either the mean squared error (MSE) with respect to the PK parameters, calculated for the fully sampled DCE-MRI sequences, or the segmentation compatibility, measured in terms of Dice scores and Hausdorff distance. The code is available on GitHub.
Dr. Tammy Riklin Raviv is a faculty member at the School of Electrical and Computer Engineering of Ben-Gurion University (BGU) since 2012. In the recent years her research group focuses on the development of deep learning algorithms for addressing a variety of Biomedical Imaging analysis problems. She holds a B.Sc. in Physics (since 1993) and an M.Sc. in Computer Science (both magna cum laude) from the Hebrew University of Jerusalem. In 2008, she received a Ph.D. from the School of Electrical Engineering of Tel-Aviv University (2008). In the years 2008-2012 she was a post-doctorate associate and a research fellow at the Computer Science and Artificial Intelligence Laboratory (CSAIL) of MIT, Harvard Medical School and the Broad Institute of MIT and Harvard. Dr. Riklin Raviv serves as an Associate Editor at the IEEE Transaction on Medical Imaging (IEEE TMI) journal and as a handling editor in NeuroImage. She is also a Technical Committee (TC) member at the IEEE Bio Imaging and Signal Processing (BISP) Committee.