在高分辨率扩散MRI扫描上估计神经取向分布场
Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
AI总结：
针对传统隐式神经表征方法难以适配超高分辨率扩散MRI、学习精细结构效率低的问题，本研究提出基于网格哈希编码的ODF场估计方法HashEnc，在图像质量提升10%的同时将计算资源需求降至现有方法的1/3。
摘要：
取向分布函数（Orientation Distribution Function，ODF）可表征关键的大脑微结构特性，在理解大脑结构连接性方面发挥着重要作用。近期研究引入了基于隐式神经表征（Implicit Neural Representation，INR）的方法，以生成具有空间感知能力的ODF场连续估计值，并且与传统离散方法相比，在相关关键任务中展现出了可观的效果。然而，传统INR方法在扩展至大规模图像（如现代超高分辨率MRI扫描）时存在困难，既难以学习精细结构，训练和推理速度也存在效率低下的问题。在本研究中，我们提出HashEnc——一种基于网格哈希编码的ODF场估计方法，并验证了其在保留结构和纹理特征方面的有效性。实验表明，HashEnc的图像质量提升了10%，同时所需计算资源仅为现有方法的三分之一。我们的代码可在https://github.com/MunzerDw/NODF-HashEnc获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
