用哈希编码提升高分辨率脑部MRI的纤维方向估计效率
Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
- 基于网格哈希编码构建连续的纤维方向场表示
- 图像质量提升10%，计算资源减少三分之二
- 适合需要高效处理大尺度MRI数据的研究者
方向分布函数（ODF）表征大脑微结构特性，在理解脑连接性中起关键作用。近期研究采用隐式神经表示（INR）方法实现对ODF场的空间感知连续估计，在多项任务中表现优异。然而，传统INR方法在处理现代超高分辨率MRI扫描时面临挑战，难以捕捉精细结构，且训练与推理效率低下。本文提出HashEnc，一种基于网格哈希编码的ODF场估计方法，有效保留了结构与纹理特征。实验表明，HashEnc在图像质量上提升10%，同时所需计算资源仅为现有方法的三分之一。
原文摘要 · Abstract (English)
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.
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