Wide Range MRI Artifact Removal with Transformers

• URL: http://arxiv.org/abs/2210.07976v2
• Date: Mon, 17 Oct 2022 13:06:21 GMT
• Title: Wide Range MRI Artifact Removal with Transformers
• Authors: Lennart Alexander Van der Goten, Kevin Smith
• Abstract summary: Artifacts on magnetic resonance scans are a serious challenge for radiologists and computer-aided diagnosis systems. We propose a method capable of retrospectively removing eight common artifacts found in native volumetric MR imagery. Our method is realized through the design of a novel transformer-based neural network that generalizes a emph windowcentered approach by the Swin transformer.
• Score: 1.1305386767685186
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially induce misdiagnoses if left unnoticed. For instance, an artifact may masquerade as a tumor or other abnormality. Retrospective artifact correction (RAC) is concerned with removing artifacts after the scan has already been taken. In this work, we propose a method capable of retrospectively removing eight common artifacts found in native-resolution MR imagery. Knowledge of the presence or location of a specific artifact is not assumed and the system is, by design, capable of undoing interactions of multiple artifacts. Our method is realized through the design of a novel volumetric transformer-based neural network that generalizes a \emph{window-centered} approach popularized by the Swin transformer. Unlike Swin, our method is (i) natively volumetric, (ii) geared towards dense prediction tasks instead of classification, and (iii), uses a novel and more global mechanism to enable information exchange between windows. Our experiments show that our reconstructions are considerably better than those attained by ResNet, V-Net, MobileNet-v2, DenseNet, CycleGAN and BicycleGAN. Moreover, we show that the reconstructed images from our model improves the accuracy of FSL BET, a standard skull-stripping method typically applied in diagnostic workflows.

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