Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques
IntroductionA 3T MRI scanner delivers enhanced image quality and SNR, minimizing artifacts to provide superior high-resolution brain images compared to a 1.5T MRI. Thus, making it vitally important for diagnosing complex neurological conditions. However, its higher cost of acquisition and operation,...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532395/full |
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| author | Sk Rahatul Jannat Kirsten Lynch Maryam Fotouhi Steve Cen Steve Cen Jeiran Choupan Nasim Sheikh-Bahaei Nasim Sheikh-Bahaei Gaurav Pandey Bino A. Varghese |
| author_facet | Sk Rahatul Jannat Kirsten Lynch Maryam Fotouhi Steve Cen Steve Cen Jeiran Choupan Nasim Sheikh-Bahaei Nasim Sheikh-Bahaei Gaurav Pandey Bino A. Varghese |
| author_sort | Sk Rahatul Jannat |
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| description | IntroductionA 3T MRI scanner delivers enhanced image quality and SNR, minimizing artifacts to provide superior high-resolution brain images compared to a 1.5T MRI. Thus, making it vitally important for diagnosing complex neurological conditions. However, its higher cost of acquisition and operation, increased sensitivity to image distortions, greater noise levels, and limited accessibility in many healthcare settings present notable challenges. These factors impact heterogeneity in MRI neuroimaging data on account of the uneven distribution of 1.5T and 3T MRI scanners across healthcare institutions.MethodsIn our study, we investigated the efficacy of three deep learning-based super-resolution techniques to enhance 1.5T MRI images, aiming to achieve quality analogous to 3T scans. These synthetic and “upgraded” 1.5T images were compared and assessed against their 3T counterparts using a range of image quality assessment metrics. Specifically, we employed metrics such as the Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), and Intensity Differences in Pixels (IDP) to evaluate the similitude and visual quality of the enhanced images.ResultsAccording to our experimental results it has been exhibited that among the three evaluated deep learning-based super-resolution techniques, the Transformer Enhanced Generative Adversarial Network (TCGAN) significantly outperformed the others. To reduce pixel differences, enhance image sharpness, and preserve essential anatomical details TCGAN performed efficaciously.DiscussionThis approach presents TCGAN offers a cost-effective and widely accessible alternative for generating high-quality images without the need for expensive, high-field MRI scans and leads to inconsistencies and complicate data comparison and harmonization challenges across studies utilizing various scanners. |
| format | Article |
| id | doaj-art-e3007ec2b2d249659a4763ef9a470d74 |
| institution | OA Journals |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-e3007ec2b2d249659a4763ef9a470d742025-08-20T02:07:31ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-06-011910.3389/fnhum.2025.15323951532395Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniquesSk Rahatul Jannat0Kirsten Lynch1Maryam Fotouhi2Steve Cen3Steve Cen4Jeiran Choupan5Nasim Sheikh-Bahaei6Nasim Sheikh-Bahaei7Gaurav Pandey8Bino A. Varghese9Department of Radiology, University of Southern California, Los Angeles, CA, United StatesDepartment of Neurology, University of Southern California, Los Angeles, CA, United StatesDepartment of Radiology, University of Southern California, Los Angeles, CA, United StatesDepartment of Radiology, University of Southern California, Los Angeles, CA, United StatesDepartment of Neurology, University of Southern California, Los Angeles, CA, United StatesDepartment of Neurology, University of Southern California, Los Angeles, CA, United StatesDepartment of Radiology, University of Southern California, Los Angeles, CA, United StatesDepartment of Neurology, University of Southern California, Los Angeles, CA, United StatesDepartment of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United StatesDepartment of Radiology, University of Southern California, Los Angeles, CA, United StatesIntroductionA 3T MRI scanner delivers enhanced image quality and SNR, minimizing artifacts to provide superior high-resolution brain images compared to a 1.5T MRI. Thus, making it vitally important for diagnosing complex neurological conditions. However, its higher cost of acquisition and operation, increased sensitivity to image distortions, greater noise levels, and limited accessibility in many healthcare settings present notable challenges. These factors impact heterogeneity in MRI neuroimaging data on account of the uneven distribution of 1.5T and 3T MRI scanners across healthcare institutions.MethodsIn our study, we investigated the efficacy of three deep learning-based super-resolution techniques to enhance 1.5T MRI images, aiming to achieve quality analogous to 3T scans. These synthetic and “upgraded” 1.5T images were compared and assessed against their 3T counterparts using a range of image quality assessment metrics. Specifically, we employed metrics such as the Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), and Intensity Differences in Pixels (IDP) to evaluate the similitude and visual quality of the enhanced images.ResultsAccording to our experimental results it has been exhibited that among the three evaluated deep learning-based super-resolution techniques, the Transformer Enhanced Generative Adversarial Network (TCGAN) significantly outperformed the others. To reduce pixel differences, enhance image sharpness, and preserve essential anatomical details TCGAN performed efficaciously.DiscussionThis approach presents TCGAN offers a cost-effective and widely accessible alternative for generating high-quality images without the need for expensive, high-field MRI scans and leads to inconsistencies and complicate data comparison and harmonization challenges across studies utilizing various scanners.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532395/fullimage qualitysuper resolutionT1 weightedImage harmonizationtransformer enhanced GAN |
| spellingShingle | Sk Rahatul Jannat Kirsten Lynch Maryam Fotouhi Steve Cen Steve Cen Jeiran Choupan Nasim Sheikh-Bahaei Nasim Sheikh-Bahaei Gaurav Pandey Bino A. Varghese Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques Frontiers in Human Neuroscience image quality super resolution T1 weighted Image harmonization transformer enhanced GAN |
| title | Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques |
| title_full | Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques |
| title_fullStr | Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques |
| title_full_unstemmed | Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques |
| title_short | Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques |
| title_sort | advancing 1 5t mr imaging toward achieving 3t quality through deep learning super resolution techniques |
| topic | image quality super resolution T1 weighted Image harmonization transformer enhanced GAN |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532395/full |
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