High-resolution and High-fidelity DTI of Cervical Cord using 3D Reduced-FOV Multiplexed Sensitivity Encoding (3D-rFOV-MUSE)
Motivation: Diffusion tensor imaging (DTI) of cervical spinal cord (csc-DTI) based on 2D-ss-EPI often encounters several challenges, such as low attainable spatial resolution, severe geometric distortion, and Nyquist ghost. Goal(s): To develop a 3D high-spatial-resolution and high-fidelity csc-DTI technique for addressing the current challenges existing in 2D csc-DTI. Approach: A 3D-rFOV-MUSE technique was developed by combining 3D-MUSE with two reduced-FOV techniques, for acquiring 3D csc-DTI data with a single sagittal thin slab. Results: Compared with routine 2D technique, our technique can mitigate through-plane partial volume effects and facilitate multi-planar data reformation for csc-DTI, with effective reductions of distortions and substantial improvements in SNR. Impact: The proposed 3D-rFOV-MUSE technique can produce high-fidelity csc-DTI at 1.0 mm-isotropic resolution, which can precisely assess the microstructural integrity of the cervical spinal cord. This may provide further pathophysiological insights to aid differential diagnosis for different cervical spinal cord diseases.
Authors
- Hing‐Chiu Chang (ORCID: https://orcid.org/0000-0001-8634-328X)
- Hailin Xiong (ORCID: https://orcid.org/0000-0001-7002-476X)
- Nan‐kuei Chen (ORCID: https://orcid.org/0000-0001-6564-4219)
- Liyuan Liang (ORCID: https://orcid.org/0000-0003-2096-028X)
- Xiaorui Xu
- Tian Liu (ORCID: https://orcid.org/0000-0002-2906-5110)
- Yi Li (ORCID: https://orcid.org/0000-0002-7148-0019)
- Shihui Chen (ORCID: https://orcid.org/0000-0002-7181-1295)
- Chenglang Yuan (ORCID: https://orcid.org/0000-0003-3459-5586)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/1616
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00