{"title":"A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image","authors":"","doi":"10.1016/j.cmpb.2024.108426","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016926072400419X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Background and objective
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.
Methods
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.
Results
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.
背景和目的:本研究旨在提高直肠癌磁共振(MR)成像扫描的轴向分辨率,以提高视觉解读和定量分析的准确性。磁共振成像是直肠癌诊断和治疗规划的关键技术。然而,获取高分辨率磁共振图像既费时又费钱。因此,许多医院只存储有限数量的切片,往往导致 MR 图像分辨率较低,尤其是在轴向平面。鉴于图像分辨率对准确评估的重要性,这些低分辨率图像往往缺乏必要的细节,给人类专家和计算机辅助诊断系统带来了巨大挑战。图像超分辨率(SR)是为提高图像分辨率而开发的一种技术,最初应用于自然图像。它的成功使其被应用于各种其他任务,尤其是低分辨率磁共振图像的重建。然而,现有的大多数 SR 方法在重建过程中未能考虑到所有解剖平面,导致应用于直肠癌 MR 图像时效果不理想:本文提出了一种基于 GAN 的三轴相互监督超分辨率重建方法,适合低分辨率直肠癌 MR 图像。我们的方法包括沿轴向对矢状和冠状面进行一维(1D)片内 SR 重建,以及基于轴向切片合成的片间 SR 重建。为了进一步提高超分辨率重建的准确性,我们引入了不同轴重建结果的一致性监督机制,促进各轴之间的相互学习。我们方法的一个关键创新点是引入了深度-反向网络(Depth-GAN)来合成轴向的中间切片,将深度信息和生成式反向网络(GAN)结合在一起。此外,我们在整个过程中结合使用了监督和非监督交互式学习技术,从而提高了中间切片合成的准确性:我们进行了广泛的消融研究以及与现有方法的对比分析,以验证我们方法的有效性。在山西省肿瘤医院的测试集上,我们的方法达到了 34.62 的峰值信噪比(PSNR)和 96.34 % 的结构相似性指数(SSIM)。这些可喜的结果证明了我们方法的优越性。
期刊介绍:
To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.
Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.