在 T1 加权脑磁共振图像的超分辨率处理中使用多重对比图像进行数据集扩增。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-12-16 DOI:10.1007/s12194-024-00871-1
Hajime Kageyama, Nobukiyo Yoshida, Keisuke Kondo, Hiroyuki Akai
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引用次数: 0

摘要

本研究调查了利用深度学习对脑部磁共振成像(MRI)T1 加权图像(T1WI)的超分辨率处理数据集进行增强的有效性。通过纳入同一受试者的不同对比度图像,本研究试图提高网络性能,并评估其对峰值信噪比(PSNR)和结构相似性(SSIM)等图像质量指标的影响。这项回顾性研究包括 240 名接受脑部核磁共振成像的患者。研究人员创建了两种数据集:纯数据集组(包括 T1WIs)和混合数据集组(包括 T1WIs、T2 加权图像和液体衰减反转恢复图像)。在这些数据集上训练了一个基于 U-Net 的网络和一个增强型深度超分辨率网络(EDSR)。使用 PSNR 和 SSIM 进行了客观图像质量分析。为了评估结果,还进行了统计分析,包括配对 t 检验和皮尔逊相关系数。随着数据集规模的增大,用不同对比度的图像来增强数据集能显著提高训练的准确性。在混合数据集上训练的 U-Net 的 PSNR 值在 29.84-30.26 dB 之间,SSIM 值在 0.9858-0.9868 之间。同样,在混合数据集上训练的 EDSR 的 PSNR 值在 32.34-32.64 dB 之间,SSIM 值在 0.9941-0.9945 之间。在纯数据集和混合数据集上训练的模型在 PSNR 和 SSIM 上存在显著差异。皮尔逊相关系数表明,数据集大小与图像质量指标之间存在很强的正相关性。在医学图像超分辨率任务中,使用从同一研究对象获取的不同图像数据可以提高深度学习模型的性能。
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Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.

This study investigated the effectiveness of augmenting datasets for super-resolution processing of brain Magnetic Resonance Images (MRI) T1-weighted images (T1WIs) using deep learning. By incorporating images with different contrasts from the same subject, this study sought to improve network performance and assess its impact on image quality metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). This retrospective study included 240 patients who underwent brain MRI. Two types of datasets were created: the Pure-Dataset group comprising T1WIs and the Mixed-Dataset group comprising T1WIs, T2-weighted images, and fluid-attenuated inversion recovery images. A U-Net-based network and an Enhanced Deep Super-Resolution network (EDSR) were trained on these datasets. Objective image quality analysis was performed using PSNR and SSIM. Statistical analyses, including paired t test and Pearson's correlation coefficient, were conducted to evaluate the results. Augmenting datasets with images of different contrasts significantly improved training accuracy as the dataset size increased. PSNR values ranged 29.84-30.26 dB for U-Net trained on mixed datasets, and SSIM values ranged 0.9858-0.9868. Similarly, PSNR values ranged 32.34-32.64 dB for EDSR trained on mixed datasets, and SSIM values ranged 0.9941-0.9945. Significant differences in PSNR and SSIM were observed between models trained on pure and mixed datasets. Pearson's correlation coefficient indicated a strong positive correlation between dataset size and image quality metrics. Using diverse image data obtained from the same subject can improve the performance of deep-learning models in medical image super-resolution tasks.

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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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