用天体物理方法提升CT图像低对比度病灶检测与超分辨率重建
Low contrast detection and super-resolution in CT images: evaluation of a novel approach based on Centroidal Voronoi Tessellation
- 基于重心沃罗诺伊剖分结合机器学习，增强低对比度信号识别
- 单切片检测成功率超60%，所有插入物轴向均正确识别
- 适用于肝脏等均匀器官中微小病灶的早期发现，适合临床影像分析
本文将天体物理学中用于探测低对比度信号的图像分析技术应用于计算机断层扫描（CT）图像处理，结合重心沃罗诺伊剖分（CVT）与机器学习方法。通过含不同直径圆柱形插入物的模体，在不同辐射剂量（CTDIvol）和不同层厚条件下获取图像，并以已知角度倾斜模拟临床病变的随机取向。单切片信号检测成功率始终高于60%，所有插入物轴向均被准确识别。随后沿该轴投影各切片生成二维超分辨率图像，整体信噪比（CNR）显著提升。研究表明，CVT在医学影像中具有潜力，可用于肝等均质器官中低对比度病灶的识别。
原文摘要 · Abstract (English)
In this work, image analysis techniques used in astrophysics to detect low-contrast signals have been adapted in the processing of Computed Tomography (CT) images, combining Centroidal Voronoi Tessellation (CVT) and machine learning techniques. Several CT acquisitions were performed using a phantom containing cylindrical inserts of different diameters producing objects with different contrasts respect to background. The images of the phantom, tilted by a known angle with respect to the tomograph axis (to mimic the casual orientation of a clinical lesion), were acquired at various radiation doses (CTDIvol) and at different slice's thicknesses. The success in detecting the signal in the single image (slice) was always greater than 60%. The axis of each insert has always been correctly identified. A super-resolution 2D image was then generated by projecting the individual slices of the scan along this axis, thus increasing the CNR of the object scanned as a whole. CVT holds great promise for future use in medical imaging, for the identification of low-contrast lesions in homogeneous organs, such as the liver.
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