Spectral CT: Preliminary Studies in the Liver Cirrhosis

Peijie Lv,XiaoZhu Lin,Jianbo Gao,Kemin Chen 대한영상의학회 2012 Korean Journal of Radiology Vol.13 No.4

Objective: To investigate the value of spectral CT imaging in the diagnosis and classification of liver cirrhosis during the arterial phase (AP) and portal venous phase (PVP). Materials and Methods: Thirty-eight patients with liver cirrhosis (Child-Pugh class A/B/C: n = 10/14/14), and 43 patients with healthy livers, participated in this study. The researchers used abdominal spectral CT imaging during AP and PVP. Iodine concentration, derived from the iodine-based material-decomposition image and the iodine concentration ratio (ICratio) between AP and PVP, were obtained. Statistical analyses {two-sample t test, One-factor analysis of variance, and area under the receiver operating characteristic curve (A [z])} were performed. Results: The mean normalized iodine concentration (NIC) (0.5 ± 0.12) during PVP in the control group was significantly higher than that in the study group (0.4 ± 0.10 on average, 0.4 ± 0.08 for Class A, 0.4 ± 0.15 for Class B, and 0.4 ± 0.06 for Class C) (All p < 0.05). Within the cirrhotic liver group, the mean NIC for Class C during the AP (0.1 ± 0.05) was significantly higher than NICs for Classes A (0.1 ± 0.06) and B (0.1 ± 0.03) (Both p < 0.05). The ICratio in the study group (0.4 ± 0.15), especially for Class C (0.5 ± 0.14), was higher than that in the control group (0.3 ± 0.15) (p < 0.05).The combination of NIC and ICratio showed high sensitivity and specificity for differentiating healthy liver from cirrhotic liver, especially in Class C cirrhotic liver. Conclusion: Spectral CT Provides a quantitative method with which to analyze the cirrhotic liver, and shows the potential value in the classification of liver cirrhosis. Objective: To investigate the value of spectral CT imaging in the diagnosis and classification of liver cirrhosis during the arterial phase (AP) and portal venous phase (PVP). Materials and Methods: Thirty-eight patients with liver cirrhosis (Child-Pugh class A/B/C: n = 10/14/14), and 43 patients with healthy livers, participated in this study. The researchers used abdominal spectral CT imaging during AP and PVP. Iodine concentration, derived from the iodine-based material-decomposition image and the iodine concentration ratio (ICratio) between AP and PVP, were obtained. Statistical analyses {two-sample t test, One-factor analysis of variance, and area under the receiver operating characteristic curve (A [z])} were performed. Results: The mean normalized iodine concentration (NIC) (0.5 ± 0.12) during PVP in the control group was significantly higher than that in the study group (0.4 ± 0.10 on average, 0.4 ± 0.08 for Class A, 0.4 ± 0.15 for Class B, and 0.4 ± 0.06 for Class C) (All p < 0.05). Within the cirrhotic liver group, the mean NIC for Class C during the AP (0.1 ± 0.05) was significantly higher than NICs for Classes A (0.1 ± 0.06) and B (0.1 ± 0.03) (Both p < 0.05). The ICratio in the study group (0.4 ± 0.15), especially for Class C (0.5 ± 0.14), was higher than that in the control group (0.3 ± 0.15) (p < 0.05).The combination of NIC and ICratio showed high sensitivity and specificity for differentiating healthy liver from cirrhotic liver, especially in Class C cirrhotic liver. Conclusion: Spectral CT Provides a quantitative method with which to analyze the cirrhotic liver, and shows the potential value in the classification of liver cirrhosis.

Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Serous Cystadenoma, Mucinous Cystadenoma, and a Pseudocyst with Detailed Analysis of Cystic Features on CT Scans: a Preliminary Study

Peijie Lv,Radfan Mahyoub,Xiaozhu Lin,Kemin Chen,Weimin Chai,Jing Xie 대한영상의학회 2011 Korean Journal of Radiology Vol.12 No.2

Objective: To determine whether or not detailed cystic feature analysis on CT scans can assist in the differential diagnosis of pancreatic ductal adenocarcinoma (PDAC) from serous cystadenoma (SCN), mucinous cystadenoma (MCN), and a pseudocyst. Materials and Methods: This study received Institutional Review Board approval and informed patient consent was waived. Electronic radiology and pathology databases were searched to identify patients with PDAC (n = 19), SCN (n = 26), MCN (n = 20) and a pseudocyst (n = 23) who underwent pancreatic CT imaging. The number, size, location, and contents of cysts,and the contour of the lesions were reviewed, in addition to the wall thickness, enhancement patterns, and other signs of pancreatic and peripancreatic involvement. Diagnosis was based on lesion resection (n = 82) or on a combination of cytological fi ndings, biochemical markers, and tumor markers (n = 6). Fisher’s exact test was used to analyze the results. Results: A combination of the CT findings including irregular contour, multiple cysts, mural nodes, and localized thickening, had a relatively high sensitivity (74%) and specifi city (75%) for differentiating PDAC from SCN, MCN, and pseudocysts (p < 0.05). Other CT fi ndings such as location, greatest dimension, or the presence of calcifi cation were not signifi cantly different. Conclusion: The CT fi ndings for PDAC are non-specifi c, but perhaps helpful for differentiation. PDAC should be included in the general differential diagnosis of pancreatic cystic neoplasms.

Structure and Electrical Performance of Na₂C₆O₆ under High Pressure

Wang, Xuan,Zhang, Peijie,Tang, Xingyu,Guan, Junjie,Lin, Xiaohuan,Wang, Yajie,Dong, Xiao,Yue, Binbin,Yan, Jinyuan,Li, Kuo,Zheng, Haiyan,Mao, Ho-kwang American Chemical Society 2019 The Journal of Physical Chemistry Part C Vol. No.

<P>Sodium rhodizonate (Na<SUB>2</SUB>C<SUB>6</SUB>O<SUB>6</SUB>) has very high theoretical capacity as a positive electrode material of sodium-ion batteries, but it still has problems such as low actual capacity and poor electronic/ionic conductivity. In order to improve its conductivity, we investigated its structure and electrical properties under high pressure. By performing in situ X-ray diffraction, Raman, infrared absorption, and alternating current impedance spectroscopy in the range of 0-30 GPa at room temperature, we observed a phase transition at ∼11 GPa, with the conductivity increasing by an order of magnitude. Above ∼20 GPa, Na<SUB>2</SUB>C<SUB>6</SUB>O<SUB>6</SUB> gradually amorphized. During the decompression process, the pressure regulation of the structure and properties of the material are reversible. Our study shows that applying external pressure is an effective tool to improve the conductivity of molecular battery materials. The investigation will help to obtain next-generation electrode materials.</P> [FIG OMISSION]</BR>

Investigation of the super-resolution methods for vision based structural measurement

Zhi Cong Chen,Lijun Wu,Zhouwei Cai,Chenghao Lin,Shuying Cheng,Peijie Lin 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.3

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.