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

10.1K
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...
10.1K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

319
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
319

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

SSMSNet: Scribble-Supervised Myocardial Scar Segmentation in Late Gadolinium Enhancement Images.

Diagnostics (Basel, Switzerland)·2026
Same author

A reversible small-molecule-switchable self-amplifying RNA expression platform.

International journal of biological macromolecules·2026
Same author

Cross-Modality Whole-Heart MRI Reconstruction with Deep Motion Correction and Super-Resolution.

Sensors (Basel, Switzerland)·2026
Same author

Self-amplifying mRNA nanovaccine encoding GM-CSF-fused HPV16 E7 enhances immunogenicity and therapeutic efficacy against cervical cancer.

International journal of biological macromolecules·2025
Same author

Non-invasive urine flow dynamics characterization of pediatric hydronephrosis based on deep learning and computational fluid dynamics.

Computer methods and programs in biomedicine·2025
Same author

Investigating the genetic effects of metformin-related targets on IgA nephropathy using Mendelian randomization.

Renal failure·2025
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Feb 28, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

以知识为导向的框架,用于合成来自多序非对比MRI的对比依赖数据.

Jinwei Dong1, Yihua Chen2, Nuoxi Li2

  • 1College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

KGSynth从非对比扫描中合成了诊断质量的对比增强型MRI,在没有加多基对比剂 (GBCAs) 的情况下保留了关键的病变细节. 这种以知识为导向的深度学习方法为有禁忌的患者提供了安全的替代方案.

关键词:
核磁共振成像合成损伤意识的一代.多式联络融合多式联络融合perfusion 输出映射 输出映射 输出映射 输出映射

更多相关视频

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

27.1K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K

相关实验视频

Last Updated: Feb 28, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

27.1K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K

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:

  • 增强对比度的MRI (磁共振成像),包括晚期加多增强 (LGE) 和脑血量 (CBV) 地图,对于诊断心肌痕和脑瘤至关重要.
  • 基于加多的对比剂 (GBCA) 是必要的,但在某些患者中禁用.
  • 目前用于合成对比增强MRI的深度学习方法往往无法保持病理细节.

研究的目的:

  • 开发KGSynth,一种以知识为导向的框架,用于从非对比序列中合成对比增强的MRI.
  • 为了改善病变细节的保存和合成医疗图像中的病理准确性.
  • 为特定患者群体提供GBCA的可行替代品.

主要方法:

  • KGSynth使用知识估计器来提取关键的病变和解剖特征.
  • 一个风格映射网络捕获对比特异的特定视觉特征.
  • 该框架明确模拟这些组件,以增强生成图像中的病态真实性.

主要成果:

  • 在心脏和大脑MRI数据集上,KGSynth表现出比现有方法更好的性能.
  • 实现了高结构相似度指数 (SSIM) 和LGE和CBV地图合成的峰值信号噪声比 (PSNR).
  • 与基线模型相比,在划分心肌梗塞和脑瘤区域方面显示出更好的准确性.

结论:

  • 将知识指导整合到生成模型中,可以在没有GBCA的情况下产生诊断质量的MRI.
  • KGSynth有效地保持了病理学准确性,使虚拟对比度增强成为可能.
  • 这项技术在临床应用方面显示出显著的前景,特别是在GBCA禁忌的患者中.