A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (allp< 0.001), improved tumor conspicuity and image sharpness (allp< 0.001, respectively), and less image noise (allp< 0.001), while maintaining diagnostic confidence (allp> 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p= 0.963), enhancing target lesions (p= 0.993), and enhancing non-target lesions (p= 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.
一套完整的诊断性胶质瘤磁共振成像方案是监测治疗效果的关键，但该方案耗时较长，对危重及不配合患者尤其具有挑战性。人工智能技术已展现出在缩短扫描时间的同时提升图像质量的潜力。本研究旨在探讨深度学习优化脑胶质瘤成像方案的诊断效能、对采集加速的影响及其图像质量表现。33例经组织学确诊的胶质母细胞瘤患者按照胶质瘤共识推荐方案，在3特斯拉磁共振扫描仪上接受了标准化脑肿瘤成像。研究获取了传统重建与深度学习重建的液体衰减反转恢复序列、T2加权及T1加权对比增强快速自旋回波序列图像，后者通过超分辨率技术实现了平面内分辨率的提升。两位资深神经放射科医师独立评估了图像数据集的主观图像质量、诊断置信度、肿瘤显著性、噪声水平、伪影及锐利度。此外，根据神经肿瘤反应评估标准2.0版测量了图像数据集中的肿瘤体积，比较了两种成像技术的差异，并确定了多项临床病理学参数。深度学习重建序列平均为每个磁共振序列节省30%的采集时间。与传统图像相比，深度学习重建序列在保持诊断置信度的同时，展现出更优的整体图像质量、更高的肿瘤显著性及图像锐利度，且图像噪声更低。根据神经肿瘤反应评估标准2.0版，非强化非靶病灶体积、强化靶病灶体积及强化非靶病灶体积在两种重建方式间均无统计学差异。本研究证实了深度学习优化胶质瘤成像方案的可行性，该方案平均减少30%采集时间并提升平面分辨率。评估显示深度学习重建序列在提升主观图像质量的同时，保持了基于神经肿瘤反应评估标准2.0版的肿瘤检测与分类诊断准确性。
肿瘤(癌症)患者之家