Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.
| Title: | Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images. |
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| Authors: | Kageyama, Hajime, Yoshida, Nobukiyo, Kondo, Keisuke, Akai, Hiroyuki |
| Source: | Radiological Physics & Technology; Mar2025, Vol. 18 Issue 1, p172-185, 14p |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kageyama%2C+Hajime%22">Kageyama, Hajime</searchLink><br /><searchLink fieldCode="AR" term="%22Yoshida%2C+Nobukiyo%22">Yoshida, Nobukiyo</searchLink><br /><searchLink fieldCode="AR" term="%22Kondo%2C+Keisuke%22">Kondo, Keisuke</searchLink><br /><searchLink fieldCode="AR" term="%22Akai%2C+Hiroyuki%22">Akai, Hiroyuki</searchLink> – Name: TitleSource Label: Source Group: Src Data: Radiological Physics & Technology; Mar2025, Vol. 18 Issue 1, p172-185, 14p – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Radiological Physics & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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