Interpretable low-dose CT enhancement via multi-Gaussian cluster variance reduction
通过多高斯簇方差降低实现可解释的低剂量CT增强
マルチガウスクラスタ分散低減による解釈可能な低線量CT造影
다중 가우시안 클러스터 분산 감소를 통한 해석 가능한 저용량 CT 조영술
Interpretación de la TC de dosis baja mediante reducción de varianza en conglomerados multi-gaussianos
Imagerie par tomodensitométrie à faible dose interprétable via la réduction de variance des clusters multi-Gaussiens
Интерпретируемое контрастное усиление низкодозной КТ с помощью уменьшения дисперсии кластеров множественных гауссовых функций
Xiaofeng Zhang ¹, Yilan Zhu ¹, Yongsheng Huang ¹, Jielong Yang ², Zhili Wang ³, Kai Zhang ⁴, Si Chen ⁵, Linbo Liu ⁶, Xin Ge ¹
¹ School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
中国 深圳 中山大学深圳校区理学院
² College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
中国 沈阳 东北大学信息科学与工程学院
³ School of Physics, Hefei University of Technology, Hefei 230009, China
中国 合肥 合肥工业大学物理学院
⁴ Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Beijing 100049, China
中国 北京 中国科学院高能物理研究所北京同步辐射装置
⁵ Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
⁶ Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, China
中国 广州 广州国家实验室
Computed tomography (CT) is indispensable in both clinical medicine and biological research, yet reducing radiation exposure while maintaining image quality remains a big challenge. To address this, we propose multi-Gaussian Cluster Variance Reduction (mGCVR), a method that enables low-dose CT images to approximate the quality of high-dose scans. mGCVR models the heterogeneous tissue CT intensity distribution using multiple Gaussian components, and performs denoising by shrinking the variance within each component.
In biological imaging experiments, mGCVR consistently improves image quality across the entire field of view. Compared with classical denoising algorithms, mGCVR produces images that more closely resemble high-dose clinical CT images and achieves superior performance in quantitative metrics. These results validate the effectiveness of mGCVR and highlight its potential for broad use in both medical imaging and scientific applications.
