Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918234078314496 |
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| author | Hays, Savannah P. Zuo, Lianrui Feng, Anqi Liu, Yihao Dewey, Blake E. Zhuo, Jiachen Mowry, Ellen M. Newsome, Scott D. Prince, Jerry L. Carass, Aaron |
| author_facet | Hays, Savannah P. Zuo, Lianrui Feng, Anqi Liu, Yihao Dewey, Blake E. Zhuo, Jiachen Mowry, Ellen M. Newsome, Scott D. Prince, Jerry L. Carass, Aaron |
| contents | Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inversion time (multi-TI) T$_1$-weighted (T$_1$-w) magnetic resonance (MR) imaging improves visualization, it is rarely acquired in clinical settings. Approach: We present SyMTIC (Synthetic Multi-TI Contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired T$_1$-w, T$_2$-weighted (T$_2$-w), and FLAIR images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time (T$_1$) and proton density (PD) maps. These maps are then used to compute multi-TI images with arbitrary inversion times. Results: SyMTIC was trained using paired MPRAGE and FGATIR images along with T$_2$-w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data.The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei. Conclusion: SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. It generalizes well to varied clinical datasets, including those with missing FLAIR images or unknown parameters, offering a practical solution for improving brain MR image visualization and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures Hays, Savannah P. Zuo, Lianrui Feng, Anqi Liu, Yihao Dewey, Blake E. Zhuo, Jiachen Mowry, Ellen M. Newsome, Scott D. Prince, Jerry L. Carass, Aaron Image and Video Processing Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inversion time (multi-TI) T$_1$-weighted (T$_1$-w) magnetic resonance (MR) imaging improves visualization, it is rarely acquired in clinical settings. Approach: We present SyMTIC (Synthetic Multi-TI Contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired T$_1$-w, T$_2$-weighted (T$_2$-w), and FLAIR images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time (T$_1$) and proton density (PD) maps. These maps are then used to compute multi-TI images with arbitrary inversion times. Results: SyMTIC was trained using paired MPRAGE and FGATIR images along with T$_2$-w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data.The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei. Conclusion: SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. It generalizes well to varied clinical datasets, including those with missing FLAIR images or unknown parameters, offering a practical solution for improving brain MR image visualization and analysis. |
| title | Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2506.04173 |