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

6.7K
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...
6.7K
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

135
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
135
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

52
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,...
52
Computed Tomography01:10

Computed Tomography

6.1K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.1K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

436
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
436
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

219
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
219

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

Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.

Cancers·2026
Same author

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same author

Quantitative Understanding of Advanced Novel Imaging Techniques for Fasciitis and Biosignature Yield (Quantify): Protocol for a Cross-Sectional Diagnostic Study.

JMIR research protocols·2026
Same author

Burn Injury as a Chronic Disease: Recognizing the Unseen Burden.

Journal of burn care & research : official publication of the American Burn Association·2025
Same author

Is Burn Center Admission Necessary After Home Oxygen Ignition Injury?

Journal of burn care & research : official publication of the American Burn Association·2025
Same author

MEMORY-EFFICIENT DEEP END-TO-END POSTERIOR NETWORK (DEEPEN) FOR INVERSE PROBLEMS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025

相关实验视频

Updated: Sep 9, 2025

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.2K

快速多对比MRI使用联合多尺度能量模型

Nima Yaghoobi1, Jyothi Rikhab Chand1, Yan Chen1

  • 1University of Virginia.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于CNN的新型模型,用于更快的3D多对比MRI采集. 这种方法通过对比学习来提高图像质量和细节保存,提高重建准确度.

关键词:
基于能源的模型插入即用重建工作

更多相关视频

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.7K
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

676

相关实验视频

Last Updated: Sep 9, 2025

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.2K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.7K
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

676

科学领域:

  • 医学成像
  • 人工智能
  • 计算神经科学

背景情况:

  • 在高同位空间分辨率下获取3D多对比MRI数据受到长时间扫描的阻碍.
  • 现有的方法往往难以平衡扫描时间和图像质量,特别是复杂的3D数据集.

研究的目的:

  • 开发一种高分辨率3D多对比MRI数据的有效方法.
  • 在加速MRI重建中提高图像准确性和细节保存.

主要方法:

  • 介绍了基于卷积神经网络 (CNN) 的多尺度能量模型,以学习多对比MRI图像的联合概率分布.
  • 从低样本数据中共同恢复对比度作为最大后期 (MAP) 估计问题,使用学习的能量模型作为前期.
  • 使用最大化-最小化算法来解决优化问题.

主要成果:

  • 拟议的模型有效地利用对比度冗余来提高图像保真度.
  • 与独立重建对比度的方法相比,重建显示出精细细节和对比度的优越保存.
  • 实现了更清晰的图像重建,解决了3DMRI中漫长扫描时间的挑战.

结论:

  • 基于CNN的多尺度能量模型在加速3D多对比MRI采集方面取得了重大进展.
  • 这种方法提高了图像质量和细节保存,优于传统的重建技术.
  • 该方法不仅适用于研究的特定3DMPNRAGE采集,也显示出更广泛的多对比MRI应用的潜力.