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7T MRI Synthesization from 3T Acquisitions
March 15, 2024, 4:46 a.m. | Qiming Cui, Duygu Tosun, Reza Abbasi-Asl
cs.CV updates on arXiv.org arxiv.org
Abstract: Supervised deep learning techniques can be used to generate synthetic 7T MRIs from 3T MRI inputs. This image enhancement process leverages the advantages of ultra-high-field MRI to improve the signal-to-noise and contrast-to-noise ratios of 3T acquisitions. In this paper, we introduce multiple novel 7T synthesization algorithms based on custom-designed variants of the V-Net convolutional neural network. We demonstrate that the V-Net based model has superior performance in enhancing both single-site and multi-site MRI datasets compared …
abstract acquisitions advantages algorithms arxiv contrast cs.cv deep learning deep learning techniques eess.iv generate image inputs mri multiple noise novel paper process signal synthetic type
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