Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

IF 2.1 4区医学

Japanese Journal of Radiology Pub Date : 2025-01-11 DOI:10.1007/s11604-024-01728-8

Hiroshi Tagawa, Yasutaka Fushimi, Koji Fujimoto, Satoshi Nakajima, Sachi Okuchi, Akihiko Sakata, Sayo Otani, Krishna Pandu Wicaksono, Yang Wang, Satoshi Ikeda, Shuichi Ito, Masaki Umehana, Akihiro Shimotake, Akira Kuzuya, Yuji Nakamoto

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引用次数: 0

Abstract

Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.

Materials and methods: Brain MRI examinations including MPRAGE taken at a single institution for investigation of mild cognitive impairment, dementia and epilepsy between January 2020 and December 2022 were included retrospectively. Images taken in 2020 or 2021 were assigned to training and validation datasets, and images from 2022 were used for the test dataset. Using the training and validation set, we determined one model using visual evaluation by radiologists with reference to image quality metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The test dataset was evaluated by visual assessment and quality metrics. Voxel-based morphometric analysis was also performed, and we evaluated Dice score and volume differences between generated and original images of major structures were calculated as absolute symmetrized percent change.

Results: Training, validation, and test datasets comprised 340 patients (mean age, 56.1 ± 24.4 years; 195 women), 36 patients (67.3 ± 18.3 years, 20 women), and 193 patients (59.5 ± 24.4 years; 111 women), respectively. The test dataset showed: PSNR, 35.4 ± 4.91; SSIM, 0.871 ± 0.058; and LPIPS 0.045 ± 0.017. No overfitting was observed. Dice scores for the segmentation of main structures ranged from 0.788 (left amygdala) to 0.926 (left ventricle). Quadratic weighted Cohen kappa values of visual score for medial temporal lobe between original and generated images were 0.80-0.88.

Conclusion: Images generated using our DL-based model can be used for post-processing and visual evaluation of medial temporal lobe atrophy.

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使用基于深度学习的模型，从3D头部MRI定位器（AutoAlign head）图像生成类似mprage的高分辨率图像。

目的：磁化制备快速梯度回波（MPRAGE）是一种有用的三维（3D） t1加权序列，但不是常规脑部检查的优先选择。我们假设通过深度学习（DL）将3D MRI定位器（AutoAlign Head）图像转换为mprage样图像将有助于诊断和研究痴呆和神经退行性疾病。我们的目标是建立并评估一个基于dl的模型，用于从MRI定位器生成类似mprage的图像。材料和方法：回顾性纳入2020年1月至2022年12月在单一机构进行的脑MRI检查，包括MPRAGE，用于调查轻度认知障碍、痴呆和癫痫。2020年或2021年拍摄的图像被分配到训练和验证数据集，2022年的图像被用于测试数据集。利用训练和验证集，我们根据峰值信噪比（PSNR）、结构相似指数（SSIM）和学习感知图像斑块相似度（LPIPS）的图像质量指标，通过放射科医生的视觉评估确定了一个模型。测试数据集通过视觉评估和质量指标进行评估。我们还进行了基于体素的形态计量学分析，我们评估了Dice评分，并计算了主要结构生成图像和原始图像之间的体积差异，以绝对对称变化百分比计算。结果：训练、验证和测试数据集包括340例患者(平均年龄56.1±24.4岁；195例女性)、36例（67.3±18.3岁，女性20例）、193例(59.5±24.4岁；111名女性)。测试数据显示：PSNR为35.4±4.91；Ssim, 0.871±0.058；LPIPS为0.045±0.017。未观察到过拟合。主要结构分割的Dice评分从0.788（左杏仁核）到0.926（左心室）不等。原始图像与生成图像的内侧颞叶视觉评分的二次加权Cohen kappa值为0.80 ~ 0.88。结论：基于dl模型生成的图像可用于内侧颞叶萎缩的后处理和视觉评价。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Japanese Journal of Radiology Medicine-Radiology, Nuclear Medicine and Imaging

自引率

4.80%

发文量

133

期刊介绍： Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.