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

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

Performance benchmarking of deep learning models for real-time median nerve segmentation and cross-sectional area measurement in ultrasound imaging.

Medical physics·2026
Same author

Rolling convolution filters for lightweight neural networks in medical image analysis.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

ISDU-QSMNet: Iteration Specific Denoising With Unshared Weights for Improved QSM Reconstruction.

NMR in biomedicine·2025
Same author

DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.

NMR in biomedicine·2024
Same author

Transformer-Based Automated Segmentation of the Median Nerve in Ultrasound Videos of Wrist-to-Elbow Region.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2023
Same author

Model resolution-based deconvolution for improved quantitative susceptibility mapping.

NMR in biomedicine·2023

相关实验视频

Updated: Jun 28, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

斯皮内特-QSM:基于模型的深度学习与schatten p-norm规范化,以改进定量敏感性映射.

Vaddadi Venkatesh1, Raji Susan Mathew2, Phaneendra K Yalavarthy3

  • 1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India.

Magma (New York, N.Y.)
|April 10, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习框架SpiNet-QSM通过使用灵活的Schatten标准来增强定量敏感度映射 (QSM). 这种先进的方法提高了MRI中的磁感应度估计,超过了现有的技术.

关键词:
双极逆转是双极逆转的一个方法.基于模型的深度学习斯坎特的p-规范.敏感性重建的重建

更多相关视频

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.4K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

相关实验视频

Last Updated: Jun 28, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.4K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

科学领域:

  • 医疗成像医学成像
  • 计算神经科学是一种神经科学.
  • 生物物理学的生物物理.

背景情况:

  • 定量敏感度映射 (QSM) 通过MRI阶段数据估计了组织的磁性敏感度.
  • 解决逆源效应问题对于准确的QSM重建至关重要.
  • 现有的方法通常依赖于固定的规范,限制了适应性.

研究的目的:

  • 开发一个有效的基于模型的深度学习框架来解决QSM反向问题.
  • 引入一种新的深度学习方法,以提高QSM的准确性.
  • 提高QSM重建模型的适应性.

主要方法:

  • 为QSM提出了一个基于Schatten-norm-driven模型的深度学习框架.
  • 整合了一个可学习的规范参数,用于数据适应.
  • 在可训练的调节器上强制执行任何规范,与固定的l1/l2规范不同.

主要成果:

  • 拟议的SpiNet-QSM方法与QSMnet和LPCNN进行了比较.
  • 在77个成像卷上进行了重建,涉及各种采集方案和临床条件 (例如,出血,多发性硬化症).
  • 该方法在定量指标方面表现出明显的优于最先进的方法.

结论:

  • 斯皮网-QSM持续提高高频误差规范 (HFEN) 和正常化根平均平方误差 (NRMSE) 至少5%.
  • 与其他方法相比,在有限的训练数据下实现了优异的QSM重建.
  • 灵活的标准驱动方法为QSM提供了一个强大的解决方案.