Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Positron Emission Tomography01:29

Positron Emission Tomography

4.2K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Image optimization for low-dose <sup>18</sup>F-FDG breast PET/MRI using deep learning: a pilot study.

EJNMMI physics·2026
Same author

From Oceans to Orbit: Radiography at the Threshold of a New Human Environment.

Radiology·2026
Same author

Advances and Challenges in Pharmacokinetic Modeling for PET Imaging: Compartment Models, Input Functions, and Quantitative Techniques.

Tomography (Ann Arbor, Mich.)·2026
Same author

The Potential Expertise Paradox in AI-Assisted Radiology.

Radiology·2026
Same author

Radiomics-based Differentiation of Recurrent Brain Metastases from Treatment Effects: A Multi-Institutional Comparative Study with Advanced Imaging.

Radiology. Imaging cancer·2026
Same author

Is the Google Artificial Intelligence Overview Accurate for Current Procedural Terminology Coding in Hand Surgical Procedures?

The Journal of hand surgery·2026

相关实验视频

Updated: Jul 5, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K

使用深度学习进行乳腺PET/MR成像的减弱校正和截断完成.

Xue Li1,2, Jacob M Johnson2, Roberta M Strigel2,3,4

  • 1Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States of America.

Physics in medicine and biology
|January 22, 2024
PubMed
概括

深度学习模型通过生成合成CT图像,有效地执行乳腺PET/MR成像的减弱校正. 这种方法提高了精度,并解决了同时进行PET/MR扫描的挑战.

关键词:
减弱纠正的纠正减弱纠正.乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.磁共振成像技术的使用定子发射断层扫描 (PET).合成CTCT 合成CT截断 完成 截断 完成

更多相关视频

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

3.7K
Whole-body PET/MRI of Pediatric Patients: The Details That Matter
10:02

Whole-body PET/MRI of Pediatric Patients: The Details That Matter

Published on: December 19, 2017

14.6K

相关实验视频

Last Updated: Jul 5, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

3.7K
Whole-body PET/MRI of Pediatric Patients: The Details That Matter
10:02

Whole-body PET/MRI of Pediatric Patients: The Details That Matter

Published on: December 19, 2017

14.6K

科学领域:

  • 医疗成像医学成像
  • 放射学 放射学是一门学科.
  • 人工智能在医学中的应用

背景情况:

  • 同时使用的PET/MR扫描仪提供高灵敏度和功能性成像功能.
  • 对乳腺PET/MR成像进行精确的减弱校正 (AC) 是一个重大的技术挑战.
  • 现有的方法在PET数据中的解剖切断和缺乏骨信息方面扎.

研究的目的:

  • 开发一个强大的深度学习 (DL) 算法用于乳腺PET/MR减弱校正.
  • 为了使切断完成和骨信息生成只使用PET数据.
  • 建立可靠的AC方法,用于同时进行乳腺PET/MR成像.

主要方法:

  • 训练了三个DL模型 (U-Net变体:DLMAE,DLMSE,DL感知) 来预测合成CT (sCT) 图像从非减弱校正 (NAC) PETPET/MR图像.
  • 利用了23名患有侵袭性乳腺癌的女性受试者接受PET/CT和PET/MR治疗的数据.
  • 基于DL的sCT重建的PET图像与基于CT的重建使用SUV错误和统计测试进行了比较.

主要成果:

  • DL模型在MAE,峰值信号与噪声比率和正常化交叉相关性方面表现相似.
  • 在DLMSE/DL感知SCT和AC的参考CT之间没有观察到显著的SUV差异.
  • 所有DL方法在SUV分析中都超过了基于狄克森的方法.

结论:

  • 具有MSE或感知损失的3D U-Net适用于乳腺PET/MR AC工作流程.
  • 获得的sCT图像有助于成功完成截断和减弱校正.
  • 这种DL方法增强了同时进行乳腺PET/MR成像的诊断效用.