利用改进的视觉变换器循环广域网合成胃癌的高 b 值扩散加权成像

IF 3.8 3区 医学 Q2 ENGINEERING, BIOMEDICAL Bioengineering Pub Date : 2024-08-08 DOI:10.3390/bioengineering11080805
Can Hu, Congchao Bian, Ning Cao, Han Zhou, Bin Guo
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Objectives: This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. Methods: The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. Results: The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87–0.90; s-ADC 0.88–0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85–0.86, s-ADC 0.85–0.93; Reader2 ICC: b-ADC 0.86–0.87, s-ADC 0.89–0.92, respectively). 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引用次数: 0

摘要

背景:扩散加权成像(DWI)是多参数磁共振成像(mpMRI)的重要组成部分,在胃癌的检测、诊断和评估中发挥着关键作用。尽管 DWI 具有很大的潜力,但它经常受到严重的解剖学扭曲和灵敏度伪影的影响,这可能会妨碍它的实际应用。目前,要提高 DWI 的图像质量,必须依靠最先进的硬件和延长扫描时间。开发一种快速技术,在缩短采集时间和提高图像质量之间取得最佳平衡,将具有重大的临床意义。研究目标本研究旨在构建并评估一种名为注意力双对比度视觉转换器循环(ADCVCGAN)的无监督学习框架,以提高胃部 DWI 的图像质量并缩短扫描时间。方法:本研究提出的 ADCVCGAN 框架采用高 b 值 DWI(b = 1200 s/mm2)作为参考,从获取的低 b 值 DWI(a-DWI,b = 800 s/mm2)生成合成 b 值 DWI(s-DWI)。具体来说,ADCVCGAN 将注意力机制 CBAM 模块纳入 CycleGAN 生成器,以增强输入 a-DWI 在通道和空间维度上的特征提取。随后,引入基于 U-net 框架的视觉转换器模块,以完善细节特征,从而生成图像质量与 b-DWI 相当的 s-DWI。最后,将源域的图像作为负样本添加到判别器中,鼓励判别器引导生成器在潜空间中合成远离源域的图像,从而生成更逼真的 s-DWI。s-DWI 的图像质量通过峰值信噪比 (PSNR)、结构相似性指数 (SSIM)、特征相似性指数 (FSIM)、均方误差 (MSE)、加权峰值信噪比 (WPSNR) 和加权均方误差 (WMSE) 等指标进行量化评估。不同 DWI 图像的主观评价采用 Wilcoxon 符号秩检验。根据 b-DWI 和 s-DWI 分别计算出的 b-ADC 和 s-ADC 的再现性和一致性采用类内相关系数(ICC)进行评估。统计学显著性水平为 p < 0.05。结果无监督学习框架 ADCVCGAN 生成的 s-DWI 在 PSNR、SSIM、FSIM、MSE、WPSNR 和 WMSE 等定量指标上的得分明显高于 a-DWI,且具有统计学意义(p < 0.001)。这一性能与最新合成算法达到的最佳水平相当。与 a-DWI 相比,s-DWI 和 b-DWI 在病灶可见度、图像解剖细节、图像失真和整体图像质量方面的主观评分明显更高(p < 0.001)。同时,s-DWI 和 b-DWI 的评分没有明显差异(p > 0.05)。不同读者对 b-ADC 和 s-ADC 读数的一致性相当(ICC:b-ADC 分别为 0.87-0.90;s-ADC 为 0.88-0.89)。同一阅读器读取的 b-ADC 和 s-ADC 读数的重复性也相当(阅读器 1 ICC:b-ADC 0.85-0.86,s-ADC 0.85-0.93;阅读器 2 ICC:b-ADC 0.86-0.87,s-ADC 0.89-0.92)。结论ADCVCGAN 在生成胃癌 DWI 图像方面显示出良好的前景。它能有效缩短扫描时间,提高图像质量,确保 s-DWI 图像及其 s-ADC 值的真实性,从而为辅助临床决策提供依据。
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Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN
Background: Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI’s image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. Objectives: This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. Methods: The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. Results: The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87–0.90; s-ADC 0.88–0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85–0.86, s-ADC 0.85–0.93; Reader2 ICC: b-ADC 0.86–0.87, s-ADC 0.89–0.92, respectively). Conclusions: ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making.
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来源期刊
Bioengineering
Bioengineering Chemical Engineering-Bioengineering
CiteScore
4.00
自引率
8.70%
发文量
661
期刊介绍: Aims Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal: ● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings. ● Manuscripts regarding research proposals and research ideas will be particularly welcomed. ● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. ● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds. Scope ● Bionics and biological cybernetics: implantology; bio–abio interfaces ● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices ● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc. ● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology ● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering ● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation ● Translational bioengineering
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