用深度学习降低脑桥小脑角MRI造影剂用量至10%-30%仍可清晰成像。
A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
- 用深度学习从低剂量造影图像重建标准质量图像。
- 10%剂量下重建图像使病灶分割准确率提升，距离误差减少。
- 适合需减少造影剂风险的患者或重复扫描场景。
目的：评估深度学习（DL）模型在脑桥小脑角（CPA）池增强T1加权（T1ce）MRI中降低造影剂剂量的效果。方法：本多中心回顾性研究使用前庭神经鞘瘤（VS）患者的T1和T1ce影像，模拟不同剂量降低的低剂量T1ce。训练DL模型从低剂量输入恢复标准剂量T1ce。评估重建图像的图像质量和分割性能，并由头颈放射科医生从多个维度评分，包括图像质量与诊断特征。结果：共纳入72名患者（平均年龄58.51±14.73岁，39名男性），203例MRI研究。随着输入剂量增加，重建图像的结构相似性指数从0.639±0.113升至0.993±0.009，峰值信噪比从21.6±3.73 dB升至41.4±4.84 dB。在10%输入剂量下，使用重建图像进行分割，Dice系数从0.673升至0.734，95%豪斯多夫距离从2.38 mm降至2.07 mm，平均表面距离从1.00 mm降至0.59 mm。10%和30%剂量的重建图像均显示优秀，后者更富信息量。结论：该DL模型显著提升低剂量CPA MRI图像质量，使病变检测与诊断可在仅10%-30%标准剂量下实现。
原文摘要 · Abstract (English)
Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials and methods: In this multi-center retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization. Results: 203 MRI studies from 72 VS patients (mean age, 58.51 \pm 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 \pm 0.113 to 0.993 \pm 0.009, and the peak signal-to-noise ratio increased from 21.6 \pm 3.73 dB to 41.4 \pm 4.84 dB. At 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent images, with the latter being considered more informative. Conclusion: The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10% - 30% of the standard dose.
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