碲锌镉SPECT数字化脉冲信息获取关键技术研究

批准号:
62001471
项目类别:
青年科学基金项目
资助金额:
24.0 万元
负责人:
吴中毅
依托单位:
学科分类:
医学信息检测与处理
结题年份:
2023
批准年份:
2020
项目状态:
已结题
项目参与者:
吴中毅
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中文摘要
碲锌镉(CZT)半导体具有较高的探测效率及能量分辨率,可以有效解决目前临床单光子发射计算机断层成像(SPECT)系统结构庞大,成像时间长,系统空间分辨能力不足等临床使用限制。但目前模数混合的CZT读出电路结构复杂,单通道计数率受限,正日益成为SPECT系统进一步发展的瓶颈。基于此,本项目将多电压阈值采样法(MVT)引入到SPECT电子学设计中。针对基于CZT的SPECT读出电路通道数量庞大,采集能量分辨率要求高,能量信息提取困难等核心问题,本项目拟在对CZT脉冲特性进行深入研究的基础上,建立CZT脉冲的形状及噪声模型;并针对该模型进行算法优化,实现CZT脉冲能量信息的准确获取,进而实现一个电子学结构简单,高计数率的全数字化SPECT读出电路,为后期我国研制具有自主知识产权的全数字化SPECT读出芯片进行原理验证,并为我国开发新一代磁兼容SPECT分子影像设备提供一种思路及解决方案。
英文摘要
With high detection efficiency and energy resolution, CdZnTe (CZT) semiconductor can effectively resolve current restrictions of the clinical use of large-scale single photon emission computed tomography (SPECT) like large structure, long imaging time and insufficient resolution of system space. However, the further development of SPECT system is getting tough owing to the complicated structure of the current analog-to-digital CZT readout circuit and the limited single-channel count rate. Based on this, the project introduced Multi-Voltage Threshold (MVT) into SPECT electronics design. Focusing on the core problems including the large amount of channels, high standard of acquisition energy resolution and energy information extraction difficulties of CZT-based SPECT readout circuit, this project intend to establish the shape and noise model of CZT pulse based on the in-depth study of CZT pulse characteristics. It will also optimize the MVT sampling algorithm according to this model to correct the pulse energy that will achieve accurate acquisition of CZT pulse energy information, thereby implementing a full-digital SPECT readout circuit with a simple electronics structure and high count rate, which will provide a proof of principle for the development of a full-digital SPECT readout chip with independent intellectual property rights of our country and a solution for the development of a new generation of magnetic compatibility SPECT molecular imaging equipment for our country.
期刊论文列表
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专利列表
DOI:10.1002/mp.15952
发表时间:2023
期刊:Medical Physics
影响因子:--
作者:Jiping Wang;Yufei Tang;Zhongyi Wu;Benjamin M. W. Tsui;Wei Chen;Xiaodong Yang;Jian Zheng;Ming Li
通讯作者:Ming Li
DOI:10.1016/j.compbiomed.2022.106419
发表时间:2022-12
期刊:Computers in biology and medicine
影响因子:7.7
作者:Qiang Du;Yufei Tang;Jiping Wang;Xiaowen Hou;Zhongyi Wu;Ming Li;Xiaodong Yang;Jian Zheng
通讯作者:Qiang Du;Yufei Tang;Jiping Wang;Xiaowen Hou;Zhongyi Wu;Ming Li;Xiaodong Yang;Jian Zheng
A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on (18)F-FDG PET/CT images.
基于 18F-FDG PET/CT 图像区分 PDAC 和 AIP 的放射组学和深度学习的多域融合模型
DOI:10.1007/s11604-022-01363-1
发表时间:2023-04
期刊:JAPANESE JOURNAL OF RADIOLOGY
影响因子:2.1
作者:Wei, Wenting;Jia, Guorong;Wu, Zhongyi;Wang, Tao;Wang, Heng;Wei, Kezhen;Cheng, Chao;Liu, Zhaobang;Zuo, Changjing
通讯作者:Zuo, Changjing
DOI:10.1109/tmi.2023.3261822
发表时间:2023-09-01
期刊:IEEE TRANSACTIONS ON MEDICAL IMAGING
影响因子:10.6
作者:Li, Ming;Wang, Jiping;Tsui, Benjamin M. W.
通讯作者:Tsui, Benjamin M. W.
DOI:10.1016/j.compbiomed.2022.105759
发表时间:2022-06
期刊:Computers in biology and medicine
影响因子:7.7
作者:Yufei Tang;Qiang Du;Jiping Wang;Zhongyi Wu;Yunxiang Li;Ming Li;Xiaodong Yang;Jian Zheng
通讯作者:Yufei Tang;Qiang Du;Jiping Wang;Zhongyi Wu;Yunxiang Li;Ming Li;Xiaodong Yang;Jian Zheng
国内基金
海外基金
