用深度学习自动识别乳腺DWI图像中的伪影，提升高b值影像诊断可靠性。
Slice-wise quality assessment of high b-value breast DWI via deep learning-based artifact detection
- 基于CNN的分片级伪影检测，区分高亮与低亮异常信号。
- DenseNet121模型对两类伪影的AUROC分别达0.92和0.94。
- 结果可辅助放射科医生定位伪影区域，适合临床影像质控场景。
扩散加权成像（DWI）有助于乳腺MRI中的病灶检测与特征分析，但高b值（b=1500 s/mm²）采集易出现强度伪影，影响诊断评估。本研究利用深度学习，在单中心回顾性数据集（n=11,806张切片，2022至2023年3T乳腺MRI）上，采用二分类（伪影存在）或多分类（伪影强度）方法检测高b值DWI中的高亮与低亮伪影。比较了DenseNet121、ResNet18和SEResNet50三种卷积神经网络（CNN）架构。最佳模型DenseNet121在独立测试集上实现超亮伪影0.92、低亮伪影0.94的受试者工作特征曲线下面积（AUROC），多分类加权AUROC分别为0.85和0.88。通过Grad-CAM热图生成预测边界框，放射科医生在200张切片上评估其定位精度，平均评分分别为3.33±1.04（高亮）和2.62±0.81（低亮）。结果显示，基于CNN的分片级伪影检测在高b值乳腺DWI中具有潜力，需进一步验证。
原文摘要 · Abstract (English)
Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast magnetic resonance imaging (MRI), however especially high b-value diffusion-weighted acquisitions can be prone to intensity artifacts that can affect diagnostic image assessment. This study aims to detect both hyper- and hypointense artifacts on high b-value diffusion-weighted images (b=1500 s/mm2) using deep learning, employing either a binary classification (artifact presence) or a multiclass classification (artifact intensity) approach on a slice-wise dataset.This IRB-approved retrospective study used the single-center dataset comprising n=11806 slices from routine 3T breast MRI examinations performed between 2022 and mid-2023. Three convolutional neural network (CNN) architectures (DenseNet121, ResNet18, and SEResNet50) were trained for binary classification of hyper- and hypointense artifacts. The best performing model (DenseNet121) was applied to an independent holdout test set and was further trained separately for multiclass classification. Evaluation included area under receiver operating characteristic curve (AUROC), area under precision recall curve (AUPRC), precision, and recall, as well as analysis of predicted bounding box positions, derived from the network Grad-CAM heatmaps. DenseNet121 achieved AUROCs of 0.92 and 0.94 for hyper- and hypointense artifact detection, respectively, and weighted AUROCs of 0.85 and 0.88 for multiclass classification on single-slice high b-value diffusion-weighted images. A radiologist evaluated bounding box precision on a 1-5 Likert-like scale across 200 slices, achieving mean scores of 3.33+-1.04 for hyperintense artifacts and 2.62+-0.81 for hypointense artifacts. Hyper- and hypointense artifact detection in slice-wise breast DWI MRI dataset (b=1500 s/mm2) using CNNs particularly DenseNet121, seems promising and requires further validation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。