Paper Info
Title
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI.
Abstract
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream approaches for enhanced scan efficiency are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. In reconstruction, performance decreases towards higher acceleration factors with diminished sampling density particularly at high-spatial-frequencies. In synthesis, the absence of data samples from the target contrast can lead to artefactual sensitivity or insensitivity to image features. Here we propose a new approach for synergistic reconstruction-synthesis of multi-contrast MRI based on conditional generative adversarial networks. The proposed method preserves high-frequency details of the target contrast by relying on the shared high-frequency information available from the source contrast, and prevents feature leakage or loss by relying on the undersampled acquisitions of the target contrast. Demonstrations on brain MRI datasets from healthy subjects and patients indicate the superior performance of the proposed method compared to previous state-of-the-art. The proposed method can help improve the quality and scan efficiency of multi-contrast MRI exams.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Sampling density,Brain mri,Pattern recognition,Computer science,Feature (computer vision),Contrast (statistics),Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1805.10704
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Salman Ul Hassan Dar131.11
Mahmut Yurt210.69
Mohammad Shahdloo310.35
Muhammed Emrullah Ildiz410.35
Tolga Çukur5368.84