Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps
Objective Compared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques aim to enhance these low-resolution images; howeve...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605251330578 |
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| _version_ | 1849325068950175744 |
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| author | Erin B Bjørkeli Jonn T Geitung Morteza Esmaeili |
| author_facet | Erin B Bjørkeli Jonn T Geitung Morteza Esmaeili |
| author_sort | Erin B Bjørkeli |
| collection | DOAJ |
| description | Objective Compared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques aim to enhance these low-resolution images; however, they frequently struggle with issues such as edge preservation, blurring, and input quality limitations. This study explores an artificial intelligence–driven approach to improve the quality of synthetically generated metabolite maps. Methods Using an open-access database of 450 participants, we trained and tested a model on 350 participants, evaluating its performance against spline and nearest-neighbor interpolation methods. Metrics such as structural similarity index, peak signal-to-noise ratio, and learned perceptual image patch similarity were used for comparison. Results Our model not only increased spatial resolution but also preserved critical image details, outperforming traditional interpolation methods in both image fidelity and edge preservation. Conclusions This artificial intelligence–powered super-resolution technique could substantially enhance magnetic resonance spectroscopic imaging quality, aiding in more accurate neurological assessments. |
| format | Article |
| id | doaj-art-cc9e45bfd4194263b7e3cc2ee9a56faf |
| institution | Kabale University |
| issn | 1473-2300 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj-art-cc9e45bfd4194263b7e3cc2ee9a56faf2025-08-20T03:48:31ZengSAGE PublishingJournal of International Medical Research1473-23002025-04-015310.1177/03000605251330578Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite mapsErin B BjørkeliJonn T GeitungMorteza EsmaeiliObjective Compared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques aim to enhance these low-resolution images; however, they frequently struggle with issues such as edge preservation, blurring, and input quality limitations. This study explores an artificial intelligence–driven approach to improve the quality of synthetically generated metabolite maps. Methods Using an open-access database of 450 participants, we trained and tested a model on 350 participants, evaluating its performance against spline and nearest-neighbor interpolation methods. Metrics such as structural similarity index, peak signal-to-noise ratio, and learned perceptual image patch similarity were used for comparison. Results Our model not only increased spatial resolution but also preserved critical image details, outperforming traditional interpolation methods in both image fidelity and edge preservation. Conclusions This artificial intelligence–powered super-resolution technique could substantially enhance magnetic resonance spectroscopic imaging quality, aiding in more accurate neurological assessments.https://doi.org/10.1177/03000605251330578 |
| spellingShingle | Erin B Bjørkeli Jonn T Geitung Morteza Esmaeili Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps Journal of International Medical Research |
| title | Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps |
| title_full | Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps |
| title_fullStr | Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps |
| title_full_unstemmed | Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps |
| title_short | Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps |
| title_sort | artificial intelligence powered four fold upscaling of human brain synthetic metabolite maps |
| url | https://doi.org/10.1177/03000605251330578 |
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