用3D高斯点阵重采样脊柱MRI，让新视角更贴合解剖结构以提升诊断准确率。
Rendering Novel Views of MRI Using 3D Gaussian Splatting
- 用3D高斯点阵从非对齐MRI构建体数据，生成对齐解剖结构的新视图
- 在所有狭窄病变条件下，新视图的分级准确率均优于原始扫描及传统重采样方法
- 适合需要精准影像评估的临床放射科医生和医学影像算法研究者
本文旨在通过重新采样脊柱MRI，使新视图平面更优地对齐目标解剖结构，从而提升放射科评分的准确性。为此，我们采用3D高斯点阵技术，从非对齐的MRI出发构建体数据表示，生成与临床评估相关解剖结构对齐的成像平面。新视图平面对于诊断性放射学分级最为理想，而原始MRI则不然。利用重采样后的图像，我们预测局部椎管狭窄的有序严重程度等级。实验对比了三种方法：(1)体素插值（基于反距离加权最近邻强度平均）；(2)三次B样条重采样（使用源点构建平滑分段立方函数估计目标点）。结果表明，在所有狭窄病变条件下，基于高斯点阵的重采样图像所生成的狭窄分级精度，显著优于未包含完整平面解剖结构的原始扫描，以及体素插值或三次B样条重采样得到的图像。
原文摘要 · Abstract (English)
The objective of this paper is to improve radiological gradings measured on MRIs of spines, by resampling scans so that the new view planes are better aligned with the target anatomy than the original sparse images. To this end, we adapt 3D Gaussian Splatting to form a volumetric reconstruction starting from non-aligned MRIs to render imaging planes aligned with the anatomy relevant for clinical evaluation. The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not. The resampled scans are then used to predict ordinal severity grades of localised stenosis conditions in spinal MRIs. We compare our method against (1) Voxel Interpolation, which takes the average of inverse-distance weighted nearest neighbour intensities for each target coordinate, and (2) Cubic B-spline resampling, which estimates target points using a smooth piecewise-cubic function built from the source points. Experiments show that across all stenosis conditions, resampled scans using Gaussian Splatting produce more accurate stenosis gradings compared to the raw scans which do not include the complete anatomy in-plane, as well as images resampled using Voxel Interpolation or Cubic B-spline resampling.
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