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

Computed Tomography01:10

Computed Tomography

6.2K
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...
6.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

50
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...
50
Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
193
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

2.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
2.1K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

441
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...
441

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

The effectiveness of the "Early clinical exposure" course based on narrative medicine in cultivating the professional qualities of undergraduates in clinical medicine: a mixed-methods study.

BMC medical education·2026
Same author

Synergistic phototherapy and Ca<sup>2+</sup> consumption for combating biofilms in diabetic wounds <i>via</i> ion interference, physical disruption, and biological regulation.

Bioactive materials·2025
Same author

Dual-comb mid-infrared spectromicroscopy with photothermal fluorescence detection.

Optics express·2025
Same author

XCal: model-based approach to X-ray CT spectral calibration.

Optics express·2025
Same author

Macroporous hydrogel loaded with AIE-photosensitizer for enhanced antibacterial and wounds healing.

International journal of biological macromolecules·2025
Same author

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography.

Scientific reports·2025
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Sep 10, 2025

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
07:28

Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

Published on: November 19, 2012

15.3K

图形图形稀疏视图选择使用视图共变率损失

Jingsong Lin, Amirkoushyar Ziabari, Singanallur V Venkatakrishnan

    IEEE transactions on pattern analysis and machine intelligence
    |August 19, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新方法,用于在计算机断层扫描 (CT) 重建中选择最佳视图. 视图共变率损失选择 (VCLS) 算法可以从稀疏视图CT数据中提高图像质量和准确性.

    更多相关视频

    Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
    08:39

    Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

    Published on: June 24, 2025

    169
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K

    相关实验视频

    Last Updated: Sep 10, 2025

    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
    07:28

    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

    Published on: November 19, 2012

    15.3K
    Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
    08:39

    Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

    Published on: June 24, 2025

    169
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K

    科学领域:

    • 医疗成像医学成像
    • 计算成像技术的成像
    • 非破坏性的评价

    背景情况:

    • 像FBP和FDK这样的标准计算机断层扫描 (CT) 重建算法需要多个视图,增加获取时间和成本.
    • 从有限的视图开发高质量的CT重建对于高效的非破坏性评估 (NDE) 至关重要.
    • 对稀疏视图CT重建的最佳视图选择仍然是一个未解决的挑战.

    研究的目的:

    • 引入一种新的视图共变率损失 (VCL) 函数,用于测量视图子集的联合信息内容.
    • 开发用于计算VCL的快速算法以及用于选择最佳视图的相关算法.
    • 评估在稀疏视图CT重建中提议的视图共变差损失选择 (VCLS) 算法的有效性.

    主要方法:

    • 开发一种新的视图共变率损失 (VCL) 函数,以近似正常化平均平方误差 (NMSE).
    • 实现用于VCL计算的快速算法.
    • 设计一个贪的算法来选择最小化VCL的视图子集.

    主要成果:

    • 与现有方法相比,VCLS算法在模拟和测量数据中表现出更高的性能.
    • 使用VCLS进行的重建显示出较低的正常化根平均平方误差 (NRMSE).
    • VCLS导致了更少的文物,并在稀疏视图CT重建中提高了准确性.

    结论:

    • 拟议的VCLS算法为在稀疏视图CT重建中选择最佳视图提供了有效的解决方案.
    • 这种方法显著提高了重建质量和准确性,解决了NDE应用中的一个关键限制.
    • VCLS提供了一种有价值的工具,可以减少CT成像中的扫描时间和成本,同时保持高图像保真度.