MR-guided radiotherapy (MRgRT) systems provide excellent soft tissue imaging immediately prior to and in real time during radiation delivery for cancer treatment. However, 2D cine MRI often has limited spatial resolution due to high temporal resolution. This work applies a super resolution machine learning framework to 3.5 mm pixel edge length, low resolution (LR), sagittal 2D cine MRI images acquired on a MRgRT system to generate 0.9 mm pixel edge length, super resolution (SR), images originally acquired at 4 frames per second (FPS). LR images were collected from 50 pancreatic cancer patients treated on a ViewRay MR-LINAC. SR images were evaluated using three methods. 1) The first method utilized intrinsic image quality metrics for evaluation. 2) The second used relative metrics including edge detection and structural similarity index (SSIM). 3) Finally, automatically generated tumor contours were created on both low resolution and super resolution images to evaluate target delineation and compared with DICE and SSIM. Intrinsic image quality metrics all had statistically significant improvements for SR images versus LR images, with mean (1 SD) BRISQUE scores of 29.652.98 and 42.480.98 for SR and LR, respectively. SR images showed good agreement with LR images in SSIM evaluation, indicating there was not significant distortion of the images. Comparison of LR and SR images with paired high resolution (HR) 3D images showed that SR images had a mean (1 SD) SSIM value of 0.6330.063 and LR a value of 0.5870.067 (p ≪ 0.05). Contours generated on SR images were also more robust to noise addition than those generated on LR images. This study shows that super resolution with a machine learning framework can generate high spatial resolution images from 4fps low spatial resolution cine MRI acquired on the ViewRay MR-LINAC while maintaining tumor contour quality and without significant acquisition or post processing delay.

MR 引导放射治疗 (MRgRT) 系统在放射治疗癌症治疗之前和期间实时提供出色的软组织成像。然而，由于高时间分辨率，2D 电影 MRI 通常具有有限的空间分辨率。这项工作将超分辨率机器学习框架应用于在 MRgRT 系统上获取的 3.5 mm 像素边缘长度、低分辨率 (LR)、矢状 2D 电影 MRI 图像，以生成 0.9 mm 像素边缘长度、超分辨率 (SR)、最初在每秒 4 帧 (FPS)。从使用 ViewRay MR-LINAC 治疗的 50 名胰腺癌患者收集 LR 图像。使用三种方法评估 SR 图像。1) 第一种方法利用内在图像质量指标进行评估。2）第二种使用相对度量，包括边缘检测和结构相似性指数（SSIM）。3) 最后，在低分辨率和超分辨率图像上创建自动生成的肿瘤轮廓，以评估目标轮廓，并与 DICE 和 SSIM 进行比较。与 LR 图像相比，SR 图像的内在图像质量指标都具有统计学上的显着改善，SR 和 LR 的平均 (1 SD) BRISQUE 得分分别为 29.652.98 和 42.480.98。SR 图像在 SSIM 评估中与 LR 图像具有良好的一致性，表明图像没有明显的失真。LR 和 SR 图像与配对高分辨率 (HR) 3D 图像的比较表明，SR 图像的平均 (1 SD) SSIM 值为 0.6330.063，LR 值为 0.5870.067 (p < 0.05)。在 SR 图像上生成的轮廓对噪声添加也比在 LR 图像上生成的轮廓更稳健。