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            科研探索 Research

            • 標題: Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging
              發表期刊: Transl Cancer Res 2020;9(8):4646-4655
              作者: X Gao,IWK Tham,J Yan

              Background: 18F-FDG PET based radiomics is promising for precision oncology imaging. This work aimsto explore quantitative accuracies of radiomic features (RFs) for low-dose 18F-FDG PET imaging.

              Methods: Twenty lung cancer patients were prospectively enrolled and underwent 18F-FDG PET/CTscans. Low-dose PET situations (true counts: 20×106, 15×106, 10×106, 7.5×106, 5×106, 2×106, 1×106, 0.5×106,0.25×106) were simulated by randomly discarding counts from the acquired list-mode data. Each PET imagewas created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI)was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value(SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reducedcount level. Conventional SUV measures, features calculated from ?rst-order statistics (FOS) and texturefeatures (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrencematrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboringgray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Biaspercentage (BP) at different count levels for each RF was calculated.

              Results: Fifty-seven lesions with a volume greater than 1.5 cm3 were found (mean volume, 25.7 cm3,volume range, 1.5–245.4 cm3). In comparison with normal total counts, mean SUV (SUVmean) in thelesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count levelof 1×106 (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-runlow grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRMexhibited the worst performance with 50% of bias with 20×106 counts (equivalent to an effective dose of0.8 mSv).

              Conclusions: In terms of the lesions included in this study, SUVmean, entropy and sum entropy fromGLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitivefeatures to lowering count. 


              FDG PET; radiomics; low-dose; quantitative; lung

            • 標題: Group Similarity Constraint Functional Brain Network Estimation For Mild Cognitive Impairment Classification
              發表期刊: Fronties in Neuroscience doi:10.3389/fnins.2020.00165
              Functional brain network (FBN) provides an effective biomarker for understanding brain activation patterns and a diagnostic criterion for neurodegenerative diseases detections. Unfortunately, it remains challenges to estimate the biologically meaningful or discriminative FBNs accurately, because of the poor quality of functional magnetic resonance imaging data or our limited understanding of human brain. In this study, a novel FBN estimation model based on group similarity prior was proposed. In particular, we extended the FBN estimation model to tensor form and incorporated the tensor trace-norm regularizer to formulate the group similarity constraint. To verify the proposed method, we conducted experiments on identifying mild cognitive impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. Experimental results illustrated that our method is effective in modeling FBNs. Consequently, we achieved 91.97% classification accuracy, outperforming the state-of-the-art methods. The post hoc analysis further demonstrated that more biologically meaningful functional brain connections were obtained using our proposed method.
            • 標題: Age-associated reorganization of metabolic brain connectivity in Chinese children
              發表期刊: European Journal of Nuclear Medicine and Molecular Imaging 2019
              作者: Qi Huang1,2 & Jian Zhang3,4 & Tianhao Zhang2,5 & Hui Wang3 & Jianhua Yan6
              Purpose The human brain develops rapidly from infant to adolescent. Establishment of the brain developmental trajectory is important to understand cognition, behavior, and emotions, as well to evaluate the risk of neuropsychiatric disorders. 18F-FDG PET has been widely used to study brain glucose metabolism, but functional brain segregation and integration based on 18F-FDG PET remains largely unknown.
              Methods Two hundred one Chinese child patients with extracranial malignancy were retrospectively enrolled as surrogates to healthy children. All images were spatially normalized into MNI space using pediatric brain template, and the 18F-FDG uptakes were calculated for 90 regions using AAL atlas. The group-level metabolic brain network was constructed by measuring Pearsoncorrelation coefficients between each pair of brain regions in an inter-subject manner for infant (1 to 4 years), childhood (5 to 8 years), early adolescent (9 to 12 years), and adolescent (13 to 18 years) group, respectively. Global efficiency of each group was calculated, and the modular architectures were detected by a greedy algorithm.
              Results Both metabolic brain network connectivity and global efficiency increased with aging. Brain network was grouped into 4, 6, 4, and 4 modules from infant to adolescent, respectively. The modular architecture dramatically reorganized from childhood to early adolescent. The hubs spatiotemporally rewired. The ratio of the connector hub to the provincial hub increased from infant to early adolescent, but decreased during the adolescent period.
              Conclusions The topological properties and modular reorganization of human brain network dramatically changed with age, especially from childhood to early adolescence. These findings would help understand the Chinese developmental trajectory of human brain functional integration and segregation.
              關鍵詞: Brain development . Metabolic brain network . Modularity . 18F-FDG PET

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