Norris Stanley A, Carrion Daniel, Ditchfield Michael, Gubser Manuel, Seah Jarrel, Badawy Mohamed K
Monash Imaging, Monash Health, 246 Clayton Rd, Clayton, VIC, 3168, Australia.
School of Science, RMIT University, Melbourne, Australia.
J Imaging Inform Med. 2025 Jan 3. doi: 10.1007/s10278-024-01385-3.
We extend existing techniques by using generative adversarial network (GAN) models to reduce the appearance of cast shadows in radiographs across various age groups. We retrospectively collected 11,500 adult and paediatric wrist radiographs, evenly divided between those with and without casts. The test subset consisted of 750 radiographs with cast and 750 without cast. We extended the results from a previous study that employed CycleGAN by enhancing the model using a perceptual loss function and a self-attention layer. The CycleGAN model which incorporates a self-attention layer and perceptual loss function delivered a similar quantitative performance as the original model. This model was applied to images from 20 cases where the original reports recommended CT scanning or repeat radiographs without the cast, which were then evaluated by radiologists for qualitative assessment. The results demonstrated that the generated images could improve radiologists' diagnostic confidence, in some cases leading to more decisive reports. Where available, the reports from follow-up imaging were compared with those produced by radiologists reading AI-generated images. Every report, except two, provided identical diagnoses as those associated with follow-up imaging. The ability of radiologists to perform robust reporting with downsampled AI-enhanced images is clinically meaningful and warrants further investigation. Additionally, radiologists were unable to distinguish AI-enhanced from unenhanced images. These findings suggest the cast suppression technique could be integrated as a tool to augment clinical workflows, with the potential benefits of reducing patient doses, improving operational efficiencies, reducing delays in diagnoses, and reducing the number of patient visits.
我们通过使用生成对抗网络（GAN）模型扩展现有技术，以减少不同年龄组X光片中石膏阴影的出现。我们回顾性收集了11500张成人和儿童腕部X光片，在有石膏和无石膏的片子之间平均分配。测试子集包括750张有石膏的X光片和750张无石膏的X光片。我们扩展了先前一项使用循环生成对抗网络（CycleGAN）的研究结果，通过使用感知损失函数和自注意力层增强模型。结合自注意力层和感知损失函数的CycleGAN模型表现出与原始模型相似的定量性能。该模型应用于20例原始报告建议进行CT扫描或去除石膏后重复拍摄X光片的病例图像，然后由放射科医生进行定性评估。结果表明，生成的图像可以提高放射科医生的诊断信心，在某些情况下能得出更具决定性的报告。在有后续影像报告的情况下，将其与放射科医生阅读人工智能生成图像后给出的报告进行比较。除两份报告外，每份报告提供的诊断与后续影像检查的诊断相同。放射科医生使用下采样的人工智能增强图像进行可靠报告的能力具有临床意义，值得进一步研究。此外，放射科医生无法区分人工智能增强图像和未增强图像。这些发现表明，石膏抑制技术可作为一种工具整合到临床工作流程中，具有减少患者辐射剂量、提高运营效率、减少诊断延迟和减少患者就诊次数等潜在益处。
