دورية أكاديمية
Bayesian reconstruction of magnetic resonance images using Gaussian processes.
| العنوان: | Bayesian reconstruction of magnetic resonance images using Gaussian processes. |
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| المؤلفون: | Xu Y; Department of Physics, Boston University, Boston, MA, 02215, USA., Farris CW; Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA., Anderson SW; Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA., Zhang X; Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.; Department of Electrical & Computer Engineering, Boston University, Boston, MA, 02215, USA.; Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.; Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA., Brown KA; Department of Physics, Boston University, Boston, MA, 02215, USA. brownka@bu.edu.; Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA. brownka@bu.edu.; Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA. brownka@bu.edu. |
| المصدر: | Scientific reports [Sci Rep] 2023 Aug 02; Vol. 13 (1), pp. 12527. Date of Electronic Publication: 2023 Aug 02. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مستخلص: | A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use. (© 2023. The Author(s).) |
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| تواريخ الأحداث: | Date Created: 20230802 Latest Revision: 20230805 |
| رمز التحديث: | 20230805 |
| مُعرف محوري في PubMed: | PMC10397278 |
| DOI: | 10.1038/s41598-023-39533-4 |
| PMID: | 37532743 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-023-39533-4 |