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

Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

763
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...
763
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

261
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...
261
Deconvolution01:20

Deconvolution

527
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
527
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

261
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
261

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

Block Matching Based Speckle Tracking Echocardiography: Clinical Applications and Research Outlook in a Deep Learning Context.

Journal of imaging informatics in medicine·2025
Same author

Enhancing Fairness in Ultrasound Imaging: Evaluating Adversarial Debiasing Across Diverse Patient Demographics.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

GEDFormer: Gradient Edge Detection in LDCT Image Denoising Transformer Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

SGAMARN: A GAN Framework for Metal Artifact Reduction in CT Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Point-of-care musculoskeletal ultrasound for hemophilic arthropathy: a scoping review of scanning protocols by the Imaging Expert Working Group of the International Prophylaxis Study Group.

Research and practice in thrombosis and haemostasis·2025
Same author

Recommendations Regarding the Appropriateness of Virtual Care: A Systematic Review.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association·2025

相关实验视频

Updated: Jan 9, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

扩散与Sinogram相遇:用于低剂量CT图像与结构和纹理先验的混合学习框架.

Farzan Niknejad Mazandarani, Paul Babyn, Javad Alirezaie

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    我们介绍了Sinogram-Aware Diffusion (SADiff),一种用于低剂量CT (LDCT) 消噪的新方法. SADiff通过将sinogram priors集成到扩散模型中来提高诊断图像质量,优于现有的技术.

    更多相关视频

    Hybrid µCT-FMT imaging and image analysis
    13:45

    Hybrid µCT-FMT imaging and image analysis

    Published on: June 4, 2015

    13.6K

    相关实验视频

    Last Updated: Jan 9, 2026

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.4K
    Hybrid µCT-FMT imaging and image analysis
    13:45

    Hybrid µCT-FMT imaging and image analysis

    Published on: June 4, 2015

    13.6K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 低剂量CT扫描 (LDCT) 对于减少辐射暴露至关重要,但会受到噪音和文物的影响,影响诊断质量.
    • 现有的基于扩散的染方法缺乏CT特定的图像形成先验,并且难以在多种不同的解剖结构中进行概括.
    • 需要先进的无雾化技术来保护诊断信息,同时提高LDCT的图像质量.

    研究的目的:

    • 为低剂量CT (LDCT) 成像开发一种基于扩散的新型消噪框架,即Sinogram-Aware Diffusion (SADiff).
    • 将sinogram priors和CT特定的条件模块集成到一个扩散模型中,以改善无声化性能和通用化.
    • 为了提高CT图像的诊断质量和真实性,这些图像是从LDCT数据重建的.

    主要方法:

    • 拟议的SADiff是一个两阶段的框架,结合了退化消除 (DR) 网络和CT条件 (CTC) 稳定扩散网络.
    • 集成的sinogram priors用于指导扩散过程,以改善功能生成和文物抑制.
    • 开发了一个CT提示符 (CTP) 模块,用于动态,CT特定的提示符生成,以指导退化过程.

    主要成果:

    • 在多个CT数据集上,SADiff表现出与现有无效化方法相比更优异的性能.
    • 在峰值信号与噪声比率 (PSNR) 中实现了高达17%的显著改进,结构相似度指数 (SSIM) 达到38%.
    • 该方法成功地从杂的LDCT扫描中恢复了高质量的,现实的CT图像.

    结论:

    • SADiff有效地解决了CT成像当前基于扩散的无色化方法的局限性.
    • 整合sinogram意识和CT特定调节显著提高了无声化性能和图像保真度.
    • 在低剂量CT应用中,SADiff提供了一种有前途的方法来提高诊断准确度.