Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909316490985472 |
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| author | Dwedari, Mohammed Munzer Consagra, William Müller, Philip Turgut, Özgün Rueckert, Daniel Rathi, Yogesh |
| author_facet | Dwedari, Mohammed Munzer Consagra, William Müller, Philip Turgut, Özgün Rueckert, Daniel Rathi, Yogesh |
| contents | The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches to form a spatially aware continuous estimate of the ODF field and demonstrated promising results in key tasks of interest when compared to conventional discrete approaches. However, traditional INR methods face difficulties when scaling to large-scale images, such as modern ultra-high-resolution MRI scans, posing challenges in learning fine structures as well as inefficiencies in training and inference speed. In this work, we propose HashEnc, a grid-hash-encoding-based estimation of the ODF field and demonstrate its effectiveness in retaining structural and textural features. We show that HashEnc achieves a 10% enhancement in image quality while requiring 3x less computational resources than current methods. Our code can be found at https://github.com/MunzerDw/NODF-HashEnc. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_09387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans Dwedari, Mohammed Munzer Consagra, William Müller, Philip Turgut, Özgün Rueckert, Daniel Rathi, Yogesh Image and Video Processing Computer Vision and Pattern Recognition The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches to form a spatially aware continuous estimate of the ODF field and demonstrated promising results in key tasks of interest when compared to conventional discrete approaches. However, traditional INR methods face difficulties when scaling to large-scale images, such as modern ultra-high-resolution MRI scans, posing challenges in learning fine structures as well as inefficiencies in training and inference speed. In this work, we propose HashEnc, a grid-hash-encoding-based estimation of the ODF field and demonstrate its effectiveness in retaining structural and textural features. We show that HashEnc achieves a 10% enhancement in image quality while requiring 3x less computational resources than current methods. Our code can be found at https://github.com/MunzerDw/NODF-HashEnc. |
| title | Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.09387 |