مؤتمر
Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging
| العنوان: | Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging |
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| المؤلفون: | Endt, Sebastian, Pirkl, Carolin M, Verdun, Claudio M, Menze, Bjoern H, Menzel, Marion I |
| المصدر: | Endt, Sebastian; Pirkl, Carolin M; Verdun, Claudio M; Menze, Bjoern H; Menzel, Marion I (2021). Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging. In: Seventeenth International Symposium on Medical Information Processing and Analysis, Campinas, Brazil, 17 November 2021 - 19 November 2021. SPIE, 120880P. |
| بيانات النشر: | SPIE |
| سنة النشر: | 2021 |
| المجموعة: | University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
| مصطلحات موضوعية: | Department of Quantitative Biomedicine, 610 Medicine & health |
| الوصف: | Magnetic resonance imaging is a versatile diagnostic tool with numerous clinical applications. However, despite advances towards higher resolutions, it cannot resolve images on a cellular level. To nevertheless probe tissue microstructure, multidimensional correlation imaging emerges as a promising method. It takes advantage of the fact that each tissue compartment has a unique signal. Usually, these multi-compartmental characteristics are averaged over a macroscopic voxel. In contrast, correlation imaging aims to probe the true, heterogeneous nature of tissue. Based on image series acquired with varying inversion time T I and echo time T E, multiparametric spectra of T1 and T2 relaxation times in every voxel can be reconstructed, revealing sub-voxel tissue classes. However, even with impractically long acquisition times spent on dense sampling of the image (3D) and T IT E-space (2D), the inverse problem of retrieving these components from measured signal curves remains highly ill-conditioned and requires expensive regularized approaches. We formulate multiparametric correlation imaging as a classification problem and propose a flexible physics informed deep learning framework comprising a multilayer perceptron. This way, we efficiently reconstruct voxel-wise T1-T2-spectra with increased robustness to noise and undersampling in the T I-T E-space compared to state-of-the-art regression. Our results show feasibility of further acceleration of the acquisition by a factor of 4. After training on synthetic data that is not constraint by pre-defined tissue classes and independent of annotated data, we test our method on in-vivo brain data, revealing sub-voxel compartments in white and gray matter. This allows us to quantify tissue microstructure and will potentially lead to novel biomarkers. |
| نوع الوثيقة: | conference object |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://www.zora.uzh.ch/id/eprint/219072/1/Magnetic.pdfTest |
| DOI: | 10.5167/uzh-219072 |
| DOI: | 10.1117/12.2604737 |
| الإتاحة: | https://doi.org/10.5167/uzh-219072Test https://doi.org/10.1117/12.2604737Test https://www.zora.uzh.ch/id/eprint/219072Test/ https://www.zora.uzh.ch/id/eprint/219072/1/Magnetic.pdfTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.9D6A576B |
| قاعدة البيانات: | BASE |
| DOI: | 10.5167/uzh-219072 |
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