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

Obesity01:24

Obesity

552
The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
552

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

Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data.

Medical image analysis·2026
Same author

Recent progress in the patterning of perovskite films for photodetector applications.

Light, science & applications·2025
Same author

Clinical features and prognosis analysis of patients with follicular lymphoma: a real-world study in China.

Annals of hematology·2025
Same author

Thorough Physiological Assessment in Non-Culprit Vessels of Patients with Acute Myocardial Infarction: Is It a Required Action?

Cardiovascular drugs and therapy·2025
Same author

Chrysin Attenuates Myocardial Cell Apoptosis in Mice.

Cardiovascular toxicology·2025
Same author

Motion Management in Positron Emission Tomography/Computed Tomography and Positron Emission Tomography/Magnetic Resonance.

PET clinics·2025

相关实验视频

Updated: Jul 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

对于极度肥胖的患者,使用深度学习方法进行PET图像排斥.

Hui Liu1, Hamed Yousefi2, Niloufar Mirian2

  • 1Department of Engineering Physics, Tsinghua University, and Key Laboratory of Particle & Radiation Imaging, Ministry of Education (Tsinghua University), Beijing, China, on leave from the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA.

IEEE transactions on radiation and plasma medical sciences
|June 7, 2023
PubMed
概括

使用U-Net的深度学习降噪可以改善极度肥胖患者的临床PET扫描图像质量. 这种方法将噪声水平与瘦身主体相匹配,保留精细结构,以实现一致的成像.

关键词:
德国联邦财政总局PET深度学习是一种深度学习.极度肥胖的患者患者非常肥胖.降低噪音 减少噪音

更多相关视频

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: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

相关实验视频

Last Updated: Jul 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
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: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 临床正子发射断层扫描 (PET) 图像质量受到极度肥胖患者的高噪音的影响.
  • 这种噪声降低会影响诊断的准确性和一致性.
  • 标准的降噪技术可能会影响图像分辨率.

研究的目的:

  • 为了将极度肥胖的受试者的PET图像中的噪音降低到与瘦身受试者相当的水平.
  • 为了确保在不同患者身体类型中保持一致的成像质量.
  • 评估基于深度学习的降噪方法,用于临床PET扫描.

主要方法:

  • 完全基于3D补丁的U-Net深度学习模型被用于降低噪音.
  • 两个U-Nets (A和B) 被训练使用来自瘦体受试者的PET数据,分别为40%和10%的计数水平.
  • 训练有素的U-Nets被应用于10名极度肥胖患者的PET图像上,通过肝脏规范标准偏差 (NSTD) 评估噪声.

主要成果:

  • 在40%计数数据上训练的U-Net A有效地减少了肥胖患者的PET图像中的噪音 (肝脏NSTD从0.13±0.04到0.08±0.03,p=0.01).
  • 消音后,肥胖者的噪音水平与瘦人 (0.08±0.03对比0.08±0.02,p=0.74) 相匹配,同时保留了细结构.
  • U-Net B (10%计数数据) 导致过度平滑和模糊的图像细节.

结论:

  • 在精简的受试者数据和匹配的计数水平上训练的U-Net在临床PET扫描中为极度肥胖的患者提供有效的降噪.
  • 该方法成功地保持了图像分辨率和一致性.
  • 建议进行进一步的临床验证,以确认这种深度学习方法的实用性.