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.
翻译:磁共振扫描中的人工制品对放射学家和计算机辅助诊断系统来说都是一项严峻的挑战。 最常见的是, 人工制品是由病人的动作引起的, 但也可能来自设备特有的异常, 如噪音模式。 不管来源如何, 人工制品不仅可以使扫描毫无用处, 而且可能会诱发错误诊断; 例如, 人工制品可能伪装成肿瘤或其他异常现象。 回溯性人工制品修正(RAC) 是在扫描已经进行后删除人工制品的问题。 在这项工作中, 我们提出了一种方法, 能够追溯性地删除在本地分辨率MMM图像中发现的8种常见的手工艺品。 对特定工艺品的存在或位置的了解不会被假定, 而这个系统通过设计可以消除多种工艺品的相互作用。 我们的方法是通过设计一个新型的量变变换网络网络网络网络(RAC), 与Swin变异模型相比, 我们的方法可以追溯性地删除8个常见的直线图 。 (i) 本地的周期性变更精确性地展示了我们不断变换的变换的系统,, 更精确地展示了我们不断变造型的变型的变型的系统,, 显示我们更精确的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型系统,, 显示了我们的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型系统,, 更的变型的变型的变型的变型的变型的变型的变型的变型的变型的变型系统, 更的变型的变型的变型的变型的变型的变型的变型的变型的变型系统的变型系统的变型系统, 更的变型的变型的变型的变式的变型系统的变式的变式的变型的变型的变型的变型的变型的变型系统的变式的变式的变式的变型的变式的变式的变式的变型系统的变型的变型的变式的变型系统,, 的变型的变型的变型的