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

4.3K
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...
4.3K

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

A Federated Benchmark for Clinical Natural Language Processing (FedDRAGON).

Studies in health technology and informatics·2026
Same author

The clinical Alzheimer's disease spectrum classified in the A/T/N framework with <sup>18</sup>F-Flutemetamol-Centiloid, <sup>18</sup>F-MK-6240-CenTauR, and <sup>18</sup>F-FDG-AD metaROI: a multicenter memory clinic observational study.

European journal of nuclear medicine and molecular imaging·2026
Same author

Reference tissue uptake of [18F]PSMA-1007 in positron emission tomography of recurrent prostate cancer.

European radiology·2026
Same author

Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.

Radiology. Imaging cancer·2026
Same author

Prospective validation of an AI software for detecting clinically significant prostate cancer on biparametric MRI.

Insights into imaging·2026
Same author

Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.

Scientific reports·2025

相关实验视频

Updated: Jul 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

在图像空间使用深度学习进行骨盆PET/MR减弱校正.

Bendik Skarre Abrahamsen1, Ingerid Skjei Knudtsen1, Live Eikenes1

  • 1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.

Frontiers in oncology
|September 11, 2023
PubMed
概括

这项研究引入了一种新的卷积神经网络方法,通过直接预测与骨相关的错误来改善正子发射断层扫描/磁共振成像 (PET/MRI) 衰减校正. 新方法显著减少了PET/MRI成像中的错误,提高了骨损伤的诊断准确性.

关键词:
在MRAC中,MRAC是MRAC.聚乙烯 (PET) /MR (MR) 是一种人工智能边界的人工智能边界减弱纠正的纠正减弱纠正深度学习是一种深度学习.前列腺癌是前列腺癌.伪CT是一种伪CT.

更多相关视频

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
07:45

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

414
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 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
07:45

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

414
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
  • 放射化学 放射化学是指辐射化学.
  • 人工智能的人工智能

背景情况:

  • 在PET/MRI中,基于狄克森的四类减弱校正 (AC) 由于骨质问题,容易出现错误.
  • 已知现有的五类模型,包括骨 atlases 也是不准确的.

研究的目的:

  • 开发一种用于骨盆PET/MRI AC的新方法,可以直接预测和纠正与骨相关的错误.
  • 通过解决当前基于狄克森的方法的局限性,提高PET/MRI减弱校正的准确性.

主要方法:

  • 训练了一个卷积神经网络,使用迪克森MR图像和四类ACμ-maps来预测AC错误图.
  • 该模型使用22名患者的PET/MRI数据进行训练和验证,并对17名接受基于PSMA的PET成像的患者进行了测试.
  • 定量分析涉及基于voxel和病变的错误指标来评估性能.

主要成果:

  • 拟议的模型将中位数根的平均平方百分比误差从12.1%和8.6%降低到6.2%.
  • 与四类和五类AC方法相比,骨病变SUVmax中位数绝对百分比误差从20.0%和7.0%提高到3.8%.
  • 该方法有效地减少了骨损伤中的基于voxel和SUVmax错误.

结论:

  • 这种基于卷积神经网络的新方法显著提高了PET/MRI减弱校正的准确性,特别是在骨损伤方面.
  • 这种方法为PET/MRI中减弱校正提供了更强大的解决方案,优于现有的基于Dixon的模型.