WO2021139835A3 - 一种spect成像预测模型创建方法、装置、设备及存储介质 - Google Patents
一种spect成像预测模型创建方法、装置、设备及存储介质 Download PDFInfo
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- 238000013527 convolutional neural network Methods 0.000 abstract 1
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Abstract
一种SPECT成像预测模型创建方法、装置、设备及存储介质。该方法包括:获取含有多个扫描图像组的训练集;其中,每个扫描图像组中包括相互对应的标准采集时长SPECT图像和短采集时长SPECT图像;以深度卷积神经网络为基础进行网络构建,得到待训练网络;将所述训练集中的短采集时长SPECT图像作为输入侧训练数据,将所述训练集中的标准采集时长SPECT图像作为输出侧训练数据,对所述待训练网络进行训练,得到SPECT成像预测模型,以便利用所述SPECT成像预测模型预测得到短采集时长SPECT图像在标准采集时长下的SPECT预测图像。能在保留医学影像的成像质量的情况下大幅度降低SPECT成像时间。
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Publication number | Priority date | Publication date | Assignee | Title |
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CN113436183A (zh) * | 2021-07-09 | 2021-09-24 | 浙江大学 | 一种图像的相关性分析装置 |
CN114155340B (zh) * | 2021-10-20 | 2024-05-24 | 清华大学 | 扫描光场数据的重建方法、装置、电子设备及存储介质 |
CN114494251B (zh) * | 2022-04-06 | 2022-07-15 | 南昌睿度医疗科技有限公司 | Spect图像处理方法以及相关设备 |
CN117909722A (zh) * | 2022-10-09 | 2024-04-19 | 深圳先进技术研究院 | 模型训练方法、光子检测方法、终端设备以及存储介质 |
CN117593610B (zh) * | 2024-01-17 | 2024-04-26 | 上海秋葵扩视仪器有限公司 | 图像识别网络训练及部署、识别方法、装置、设备及介质 |
Citations (5)
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CN107638188A (zh) * | 2017-09-28 | 2018-01-30 | 江苏赛诺格兰医疗科技有限公司 | 图像衰减校正方法及装置 |
CN108961237A (zh) * | 2018-06-28 | 2018-12-07 | 安徽工程大学 | 一种基于卷积神经网络的低剂量ct图像分解方法 |
CN109215014A (zh) * | 2018-07-02 | 2019-01-15 | 中国科学院深圳先进技术研究院 | Ct图像预测模型的训练方法、装置、设备及存储介质 |
CN109961435A (zh) * | 2019-04-02 | 2019-07-02 | 上海联影医疗科技有限公司 | 脑图像获取方法、装置、设备及存储介质 |
CN112381741A (zh) * | 2020-11-24 | 2021-02-19 | 佛山读图科技有限公司 | 基于spect数据采样与噪声特性的断层图像重建方法 |
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Patent Citations (5)
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CN107638188A (zh) * | 2017-09-28 | 2018-01-30 | 江苏赛诺格兰医疗科技有限公司 | 图像衰减校正方法及装置 |
CN108961237A (zh) * | 2018-06-28 | 2018-12-07 | 安徽工程大学 | 一种基于卷积神经网络的低剂量ct图像分解方法 |
CN109215014A (zh) * | 2018-07-02 | 2019-01-15 | 中国科学院深圳先进技术研究院 | Ct图像预测模型的训练方法、装置、设备及存储介质 |
CN109961435A (zh) * | 2019-04-02 | 2019-07-02 | 上海联影医疗科技有限公司 | 脑图像获取方法、装置、设备及存储介质 |
CN112381741A (zh) * | 2020-11-24 | 2021-02-19 | 佛山读图科技有限公司 | 基于spect数据采样与噪声特性的断层图像重建方法 |
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US20240177377A1 (en) | 2024-05-30 |
CN113012252A (zh) | 2021-06-22 |
WO2021139835A2 (zh) | 2021-07-15 |
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