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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.0K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.0K

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

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis.

IEEE transactions on medical imaging·2026
Same author

Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.

Abdominal radiology (New York)·2026
Same author

A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.

Cancers·2026
Same author

Diverse image generation with diffusion models and cross class label learning for polyp classification.

Scientific reports·2026
Same author

Evaluating the Predictive Value of Post-Treatment Superb Microvascular Imaging for Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Cancer.

Bioengineering (Basel, Switzerland)·2026
Same author

Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil array.

Radiology advances·2026
Same journal

Externally Tested AI for Lung Nodule Classification: A Realistic Benchmark for an Emerging Screening Era.

Radiology. Artificial intelligence·2026
Same journal

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Radiology. Artificial intelligence·2026
Same journal

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Radiology. Artificial intelligence·2026
Same journal

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026
Same journal

When One Sequence Is Enough-And When It Isn't.

Radiology. Artificial intelligence·2026
Same journal

Cracking the Registration Conundrum in Breast MRI: Preserving the Tumor Signal to Reveal True Treatment Change.

Radiology. Artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

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

989

基于物理的自编码器,用于用混合多维MRI进行前列腺组织微结构剖析.

Batuhan Gundogdu1,2, Aritrick Chatterjee1,2, Milica Medved1,2

  • 1Department of Radiology, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637.

Radiology. Artificial intelligence
|February 5, 2025
PubMed
概括
此摘要是机器生成的。

物理信息自编码器 (PIA) 是一种深度学习模型,可以准确地从MRI中测量前列腺癌生物标志物,在速度和抗噪声方面表现优于传统方法. 这种AI方法为PCa检测提供了更快,更强大的工具.

关键词:
MR扩散权重成像 扩散权重成像前列腺前列腺前列腺堆叠的自动编码器组织特征鉴定

更多相关视频

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

146
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.8K

相关实验视频

Last Updated: Jun 14, 2025

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

989
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

146
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.8K

科学领域:

  • 放射学和医学成像学 医学成像学
  • 人工智能在医学中的应用
  • 生物标志物发现发现

背景情况:

  • 前列腺癌 (PCa) 诊断依赖于精确的组织生物标志物测量.
  • 像非线性最小正方形 (NLLS) 这样的传统方法可能很慢,对噪声敏感.
  • 混合多维MRI为组织特征提供了丰富的数据.

研究的目的:

  • 评估物理信息自编码器 (PIA) 的性能,这是一个自我监督的深度学习模型,用于使用混合多维MRI测量前列腺组织生物标志物.
  • 为了比较PIA的准确性,对噪声的强度和计算效率与传统的NLLS算法.
  • 评估PIA作为人工智能工具的潜力,用于非侵入性PCa检测.

主要方法:

  • 通过将三分区扩散-放松模型与深度神经网络集成,开发了PIA.
  • 经过培训的PIA从MRI数据中预测PCa的组织特异性生物标志物.
  • 使用不同信号噪声比率 (SNR) 的 in silico 实验和来自 21 名 PCa 患者的 in vivo 数据进行验证的 PIA,与病原学和NLLS进行比较.

主要成果:

  • 在预测参考标准组织参数方面,PIA显示出高准确度,超过NLLS,特别是在杂的条件下 (SNR 20:1上的上皮体积:r=0.80对0.65).
  • 在体内,PIA的非侵入性体积分数估计与定量组织学有很强的相关性 (ICC:0.94为上皮质,0.85为肌层,0.92为光层).
  • PIA测量与PCa攻击性相关 (r = 0.75),并且明显比NLLS更快 (每张图像0.18秒vs40分钟).

结论:

  • 与NLLS相比,PIA提供了MRI精确的前列腺组织生物标志物测量,与噪声和计算效率相比,具有更高的稳定性.
  • PIA显示出作为PCa检测准确,非侵入性和可解释的人工智能方法的潜力.
  • 这种深度学习方法推动了人工智能在定量MRI中用于癌症诊断的应用.