Energy enhanced tissue texture in spectral computed tomography for lesion classification  

作　　者：Yongfeng Gao Yongyi Shi Weiguo Cao Shu Zhang Zhengrong Liang 

机构地区：[1]Department of Radiology,Stony Brook University,Stony Brook,NY 11794,USA [2]Institute of Image Processing and Pattern Recognition,Xi’an Jiaotong University,Xi’an 710049,Shanxi,China [3]Departments of Radiology,Biomedical Engineering,Computer Science,and Electrical Engineering,Stony Brook University,Stony Brook,NY 11794,USA

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2019年第1期138-149,共12页工医艺的可视计算（英文）

基　　金：This work was partially supported by the NIH/NCI,No.CA206171.

摘　　要：Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical tasks.Spectral computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies.Therefore,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion classification.For issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory.Reconstruction results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise.For issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the CMs.Studies were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration detectors.Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data only.The CM-and HF-inputs can achieve the best AUC of 0.934 and 0.927.This texture themed study shows the insight that incorporating clinical important prior information,e.g.,tissue texture in this paper,into the medical imagin

关 键 词：Tissue texture Spectral computed tomography Lesion classification Machine learning Bayesian reconstruction 

分 类 号：TG1[金属学及工艺—金属学]