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

Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.

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

Objective quality assessment for precision functional MRI data.

Neuron·2026
Same author

Integrating neuroscience across species and scales.

Nature neuroscience·2026
Same author

A Sub-Microsecond Switch Enabling SWIFT <sup>23</sup>Na Imaging at 10.5 T.

Magnetic resonance in medicine·2026
Same author

A Systematic Characterization of Causal Interactions Between Human Visual Areas.

bioRxiv : the preprint server for biology·2026
Same author

Dissociating stimulus encoding and task demands in ECoG responses from human visual cortex.

bioRxiv : the preprint server for biology·2026
Same journal

Influence of gadolinium-based contrast agent (GBCA) on the diffusion weightings of breast lesions: an intra-patient analysis.

Magma (New York, N.Y.)·2026
Same journal

Evaluation of the diffusion time dependence of the IVIM effect based on realistic capillary flow simulations in mouse brain.

Magma (New York, N.Y.)·2026
Same journal

An evaluation of brain volume and cortical thickness measurement at 0.55 T.

Magma (New York, N.Y.)·2026
Same journal

Net zero emission MR imaging using a permanent 0.4 T magnet.

Magma (New York, N.Y.)·2026
Same journal

Special issue on "deuterium metabolic imaging".

Magma (New York, N.Y.)·2026
Same journal

Black-blood dynamic contrast-enhanced MRI of abdominal aortic aneurysms.

Magma (New York, N.Y.)·2026
查看所有相关文章

相关实验视频

Updated: Jun 19, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

151

使用卷积神经网络改善前列腺T2放松计量的定量参数估计.

Patrick J Bolan1,2, Sara L Saunders3, Kendrick Kay4,5

  • 1Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA. bolan@umn.edu.

Magma (New York, N.Y.)
|July 23, 2024
PubMed
概括
此摘要是机器生成的。

神经网络 (NN) 在前列腺中显示出优异的T2映射,与传统的曲线拟合相比. 在合成数据上训练的卷积神经网络 (CNN) 实现了更高的准确性和稳定性,特别是在杂的环境中.

关键词:
在T2映射中使用T2映射.磁共振成像技术 磁共振成像技术神经网络的神经网络的神经网络前列腺 前列腺前列腺放松计放松计放松计.

更多相关视频

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
05:30

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

1.3K
Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

115

相关实验视频

Last Updated: Jun 19, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

151
Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
05:30

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

1.3K
Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

115

科学领域:

  • 磁共振成像 (MRI) 是一种磁共振成像.
  • 医疗成像医学成像
  • 计算生物学 计算生物学

背景情况:

  • 在MRI中的定量参数映射通常使用曲线拟合.
  • 估计T2参数对于前列腺成像至关重要.
  • 目前的方法面临着挑战,特别是在信号对噪声低的地区.

研究的目的:

  • 为了比较传统的曲线拟合技术与神经网络 (NN) 测量前列腺T2的方法.
  • 为了评估不同NN架构和训练策略的准确性,精度和噪声强度,与已建立的曲线拟合方法相比.

主要方法:

  • 产生大型基于物理的合成数据集,模拟T2映射采集,用于NN培训和绩效比较.
  • 实施和比较四个NN组合 (架构和培训机构) 与四个曲线拟合策略.
  • 使用已知基底真相的合成数据和带有噪声增强的体内数据进行定量评估.

主要成果:

  • 在自然合成数据上训练的卷积神经网络 (CNN) 在合成数据集上表现出最高的准确性和精度.
  • 最好的CNN在体内数据上制作了低噪音的T2地图.
  • 这种CNN方法在增加输入噪声水平时表现出最少的性能降低.

结论:

  • 使用合成数据对CNN的监督训练可以比传统的曲线拟合产生更高的T2估计性能.
  • 基于NN的T2映射显示了提高前列腺低信号噪声比区域的精度的特别希望.
  • 这种方法为前列腺成像中的定量MRI提供了潜在的进步.