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

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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

Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI.

European radiology experimental·2026
Same author

Assessment of breast cancer and feasibility of subtyping of breast cancer using thoracoabdominal staging photon-counting detector computed tomography.

Scientific reports·2026
Same author

Clinical evaluation of an automated Alberta Stroke Program Early Computed Tomography Score (ASPECTS)-scoring system.

Quantitative imaging in medicine and surgery·2026
Same author

The degenerome-a novel streamline-wise approach for white matter integrity in neurodegeneration.

NPJ Parkinson's disease·2026
Same author

Characterization of meningeal diverticula in patients with cerebrospinal fluid-venous fistulas.

Journal of neurointerventional surgery·2026
Same author

Speech-Like Vibrotactile Stimulation Is Associated With Enhanced Cortical Activations in Single-Sided Deafness: An fMRI Study.

The European journal of neuroscience·2026
Same journal

Vessel Wall Imaging in 1.5 T MRI Using Deep Learning Reconstruction: Prospective Evaluation of Interchangeability With Standard 3 T MRI.

Investigative radiology·2026
Same journal

Accelerated Deep-Learning-Based Image Reconstruction for 3D T2 Dark-Fluid in Imaging of Multiple Sclerosis.

Investigative radiology·2026
Same journal

3D Freehand Ultrasound Imaging of Optic Nerve Sheath.

Investigative radiology·2026
Same journal

Iodinated Contrast Media Hypersensitivity in 115,966 Patients: Risk Factors, Severity Profiles, and the Impact of Iodine Concentration on Reaction Risk.

Investigative radiology·2026
Same journal

Improvement of Lung Nodule Volumetric Accuracy with Photon-counting Computed Tomography Over Energy-integrating Computed Tomography in Low-dose Screening: A Phantom Study.

Investigative radiology·2026
Same journal

Photon-counting CT in Anterior Cervical Discectomy and Fusion: Improved Metal Artifact Reduction and Impact on Bone Fusion Assessment.

Investigative radiology·2026
查看所有相关文章

相关实验视频

Updated: Jun 18, 2026

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

26.3K

在3T乳腺MRI中使用深度学习重建算法与超分辨率处理的加速扩散加权成像:前性比较研究

Caroline Wilpert1, Claudia Neubauer, Alexander Rau

  • 1From the Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (C.W., C.N., A.R., H.S., M.B., JW, F.B., M.W.-B., J.N.); MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany (T.B., E.W.); EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany (R.S.); Medical Physics, Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (M.R.); and Department of Stereotactic and Functional Neurosurgery, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (M.R.).

Investigative radiology
|July 10, 2023
PubMed
概括
此摘要是机器生成的。

在乳腺MRI中,用于扩散权重成像 (DWI DL) 的深度学习重建显著减少了近一半的扫描时间. 这种先进的技术还可以提高病变的显着性,同时保持整体图像质量,以改善乳腺癌检测.

更多相关视频

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

546

相关实验视频

Last Updated: Jun 18, 2026

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

26.3K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

546

科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 多参数乳腺MRI利用扩散加权成像 (DWI) 来提高诊断特异性.
  • 标准DWI序列通常涉及较长的获取时间,可能会影响患者的舒适性和工作流程效率.
  • 深度学习 (DL) 重建为加速DWI获取和提高图像分辨率提供了一个潜在的解决方案.

研究的目的:

  • 评估DL加速DWI序列与超分辨率处理 (DWI DL) 的采集时间和图像质量.
  • 在侵袭性乳腺癌 (IBC),良性病变 (BEs) 和囊的病变明显性和对比性方面,将DWI DL与标准DWI (DWI STD) 进行比较.
  • 评估定量和定性图像质量指标,包括信号与噪声比 (SNR),明显扩散系数 (ADC),对比与噪声比 (CNR) 以及放射科医生的主观评估.

主要方法:

  • 一项前性,单心研究,涉及65名参与者接受3T乳腺MRI.
  • 标准单射回声平面DWI (DWI STD) 与DL加速DWI序列 (DWI DL) 的比较,使用类似的获取参数但降低了平均值.
  • 对SNR,ADC,CNR和对比度进行定量分析. 由两个独立的放射科医生对图像质量,文物和病变明显性的定性评估.

主要成果:

  • DWI DL显著减少了近50%的平均获取时间 (2:44分钟对 5:02分钟,P < 0.001).
  • 伤害对比度与DWI DL显著更高 (P < 0.001),而SNR和CNR在序列之间没有显著差异.
  • DWI DL表现出改善的损伤明显性 (P < 0.001),并保持了高的主观图像质量,尽管得分文物略有增加.

结论:

  • DL加速的DWI (DWI DL) 大大缩短了乳房MRI扫描时间.
  • DWI DL提高了病变的明显性和对比性,有助于检测入侵性乳腺癌,良性病变和囊.
  • 这项技术为高效和有效的多参数乳腺MRI提供了有前途的进步.