Purpose: To evaluate the quality of deep learning reconstruction for prospectively accelerated intraoperative magnetic resonance imaging (iMRI) during resective brain tumor surgery. Materials and Methods: Accelerated iMRI was performed during brain surgery using dual surface coils positioned around the area of resection. A deep learning (DL) model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. Evaluation was performed on imaging material from 40 patients imaged between 01.11.2021 - 01.06.2023 that underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two working neuro-radiologists and a working neurosurgeon on a 1 to 5 Likert scale (1=non diagnostic, 2=poor, 3=acceptable, 4=good, 5=excellent), and the favored reconstruction variant. Results: The DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for reader 1, 2, and 3, respectively. Two of three readers consistently assigned higher ratings for the DL reconstructions, and the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for reader 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal. Conclusion: DL shows promise to allow for high-quality reconstructions of intraoperative MRI with equal to or improved perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to compressed sense.

翻译：摘要：目的：评估深度学习重建在脑肿瘤切除手术中前瞻性加速的术中磁共振成像（iMRI）中的图像质量。材料与方法：在脑部手术过程中，使用置于切除区域周围的双表面线圈进行加速iMRI。训练了一个深度学习（DL）模型，该模型基于fastMRI神经数据集进行训练，以模拟iMRI协议的数据。评估对象为2021年11月1日至2023年6月1日期间接受肿瘤切除手术并进行iMRI的40名患者的影像资料。在传统的压缩感知（CS）方法与训练的DL重建方法之间进行了对比分析。两位在职神经放射科医生和一位在职神经外科医生采用1到5分的李克特量表（1=无法诊断，2=差，3=可接受，4=良好，5=优秀）进行盲法评估多个图像质量指标，并选择偏好的重建变体。结果：对于读者1、2和3，DL重建在33/40、39/40和8/40的病例中分别被强烈偏好或偏好于CS重建。两位（共三位）读者一致认为DL重建的评分更高，且对于读者1、2和3，DL重建在72%、72%和14%的病例中得分高于相应的CS重建。尽管如此，DL重建仍存在条带伪影和信号减弱等缺陷。结论：DL在实现术中MRI高质量重建方面展现出潜力，与压缩感知相比，其感知空间分辨率、信噪比、诊断置信度、诊断清晰度和空间分辨率均达到或有所改善。