Gohla Georg, Hauser Till-Karsten, Bombach Paula, Feucht Daniel, Estler Arne, Bornemann Antje, Zerweck Leonie, Weinbrenner Eliane, Ernemann Ulrike, Ruff Christer
Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tübingen, 72076 Tübingen, Germany.
Department of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany.
Cancers (Basel). 2024 May 10;16(10):1827. doi: 10.3390/cancers16101827.
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 (all < 0.001), improved tumor conspicuity and image sharpness (all < 0.001, respectively), and less image noise (all < 0.001), while maintaining diagnostic confidence (all > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions ( = 0.963), enhancing target lesions ( = 0.993), and enhancing non-target lesions ( = 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.
完整的诊断性MRI胶质瘤检查方案是监测治疗评估的关键，但耗时较长，对于重症和不配合的患者尤其具有挑战性。人工智能在同时减少扫描时间和提高图像质量方面显示出前景。本研究的目的是调查深度学习优化的脑胶质瘤检查方案的诊断性能、对采集加速的影响以及图像质量。33例组织学确诊的胶质母细胞瘤患者在3特斯拉MRI扫描仪上根据胶质瘤共识建议进行了标准化的脑肿瘤成像。采集了常规的以及深度学习重建（DLR）的液体衰减反转恢复序列，以及具有更高平面分辨率（即超分辨率）的T2加权和T1加权对比增强快速自旋回波图像。两名经验丰富的神经放射科医生独立评估图像数据集的主观图像质量、诊断信心、肿瘤清晰度、噪声水平、伪影和锐度。此外，根据神经肿瘤学反应评估（RANO）2.0在图像数据集中测量肿瘤体积，并在两种成像技术之间进行比较，并确定各种临床病理参数。DLR序列每个MRI序列平均节省时间30%。同时，与传统图像相比，DLR序列显示出更高的整体图像质量（均P<0.001）、更好的肿瘤清晰度和图像锐度（分别均P<0.001）以及更少的图像噪声（均P<0.001），同时保持诊断信心（均P>0.05）。关于RANO 2.0，重建类型之间非强化非靶病变（P=0.963）、强化靶病变（P=0.993）和强化非靶病变（P=0.951）的体积没有差异。深度学习优化的胶质瘤检查方案的可行性得到了证实，平均采集时间减少了30%，平面分辨率提高。评估的DLR序列改善了主观图像质量，并在根据RANO 2.0进行肿瘤检测和肿瘤分类时保持了诊断准确性。
