本研究旨在增强直肠癌磁共振（MR）成像扫描的轴向分辨率，以提高视觉判读和定量分析的准确性。 MR 成像是直肠癌诊断和治疗计划的关键技术。然而，获得高分辨率 MR 图像既耗时又昂贵。因此，许多医院仅存储有限数量的切片，通常导致 MR 图像分辨率较低，尤其是在轴向平面上。鉴于图像分辨率在准确评估中的重要性，这些低分辨率图像经常缺乏必要的细节，这给人类专家和计算机辅助诊断系统带来了巨大的挑战。图像超分辨率（SR）是一种为增强图像分辨率而开发的技术，最初应用于自然图像。它的成功导致了它在各种其他任务中的应用，特别是在低分辨率 MR 图像的重建方面。然而，大多数现有的SR方法在重建过程中无法考虑所有解剖平面，导致应用于直肠癌MR图像时结果不令人满意。在本文中，我们提出了一种基于GAN的三轴互监督超分辨率重建方法，专门针对直肠癌MR图像而设计。低分辨率直肠癌 MR 图像。我们的方法包括沿轴向方向对矢状面和冠状面执行一维 (1D) 切片内 SR 重建，并结合基于轴向方向切片合成的切片间 SR 重建。为了进一步提高超分辨率重建的准确性，我们在不同轴的重建结果之间引入一致性监督机制，促进各轴之间的相互学习。我们方法的一个关键创新是引入 Depth-GAN 来合成轴向平面中的中间切片，结合深度信息并利用生成对抗网络 (GAN) 来实现此目的。此外，我们通过在整个过程中结合使用有监督和无监督的交互式学习技术来提高中间切片合成的准确性。我们进行了广泛的消融研究并与现有方法进行比较分析，以验证我们方法的有效性。在山西省肿瘤医院的测试集上，我们的方法实现了 34.62 的峰值信噪比（PSNR）和 96.34％的结构相似性指数（SSIM）。这些有希望的结果证明了我们方法的优越性。版权所有 © 2024 Elsevier B.V. 保留所有权利。

This study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images.In this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process.We conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.Copyright © 2024 Elsevier B.V. All rights reserved.