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

Quality Assurance01:19

Quality Assurance

205
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
205
Quality Control01:05

Quality Control

304
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
304
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

311
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
311
Data Validation01:15

Data Validation

252
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
252
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K

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

Advancing radiation oncology care in Ukraine during the war: impact of international observerships on professional development and clinical practice.

Frontiers in oncology·2026
Same author

Adapting to the Future: Introducing the Radiation Oncology Alliance Adaptive Radiotherapy Working Group.

Journal of medical imaging and radiation oncology·2025
Same author

Pelvic dose accumulation accuracy in a CBCT based online adaptive radiotherapy system.

Journal of applied clinical medical physics·2025
Same author

Safety and Efficiency Analysis of Operational Decision-Making During Cone Beam Computed Tomography-Based Online Adaptive Radiation Therapy.

International journal of radiation oncology, biology, physics·2024
Same author

Intrafraction Motion and Margin Assessment for Ethos Online Adaptive Radiotherapy Treatments of the Prostate and Seminal Vesicles.

Advances in radiation oncology·2024
Same author

Dosimetry implications of VMAT delivery angles and intra-fraction motion for SBRT prostate treatments.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists·2023
Same journal

In radiation oncology, the best way to maintain patient safety when implementing cutting edge technologies such as AI-based and real-time adaptive techniques is through prospective hazard analysis.

Physical and engineering sciences in medicine·2026
Same journal

Compact neural network algorithm for electrocardiogram classification.

Physical and engineering sciences in medicine·2026
Same journal

Fat-suppression performance for in-stent plaque imaging after carotid artery stenting using three-dimensional T<sub>1</sub>-weighted MRI: a phantom study.

Physical and engineering sciences in medicine·2026
Same journal

Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.

Physical and engineering sciences in medicine·2026
Same journal

Design and implementation of an automated quality assurance tool for Hounsfield unit-to-relative electron density calibration in cone beam computed tomography imaging.

Physical and engineering sciences in medicine·2026
Same journal

Complementary roles of GPU-accelerated Monte Carlo and ArcCHECK in TomoTherapy quality assurance.

Physical and engineering sciences in medicine·2026
查看所有相关文章

相关实验视频

Updated: Sep 16, 2025

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K

最大质量保证效率和准确性

Dean Wallace1,2, Mikel Byrne1,3, Kelvin Hiscoke1,4

  • 1Icon Cancer Centre, South Brisbane, QLD, Australia.

Physical and engineering sciences in medicine
|July 10, 2025
PubMed
概括
此摘要是机器生成的。

综合放射治疗质量保证 (QA) 软件显著减少了c-arm线性加速器的任务时间. 这项研究发现,MaximQA提供了效率提升和与现有质量保证解决方案相比较的准确性.

关键词:
在CBCT中,CBCT是CBCT.在DMLC中,DMLC是DMLC.哈尔西昂是一个的星球.综合质量保证系统 综合质量保证系统马克西姆QAQA的最高标准是多叶合器是多叶合器.质量保证 质量保证 质量保证调节时间的时间.真正的木头是真实的温斯顿 - - 卢茨 (英语:Winston-Lutz) 是一个城市.

更多相关视频

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
08:37

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Published on: March 30, 2015

13.9K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.2K

相关实验视频

Last Updated: Sep 16, 2025

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
08:37

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Published on: March 30, 2015

13.9K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.2K

科学领域:

  • 医学物理 医学物理
  • 放射治疗技术 放射治疗技术
  • 软件工程 软件工程 软件工程

背景情况:

  • 有用于放射治疗的商业质量保证 (QA) 软件,但与c-arm线性加速器的集成是有限的.
  • 瓦里安医疗系统公司的MaximQA提供了与TrueBeam和Halcyon linacs的集成,用于自动化QA任务捕获和分析.
  • 目前的MaximQA功能包括CBCT,DMLC (TrueBeam) 和温斯顿-卢茨 (Halcyon) 测试.

研究的目的:

  • 与非集成软件相比,研究集成质量保证系统 (MaximQA) 的效率改进.
  • 通过将其分析结果与另一种商业产品DoseLab.Lab进行比较来评估MaximQA的准确性.
  • 为了评估特定质量保证任务的时间节约:CBCT,温斯顿-卢茨和DMLC.

主要方法:

  • 在MaximQA和DoseLab中进行了一项时间研究,以测量质量保证任务 (CBCT,Winston-Lutz,DMLC) 的持续时间.
  • 通过比较两个软件包中执行的相同质量保证测试的分析结果来评估准确性.
  • 该研究的重点是集成与非集成QA软件的效率和准确性.

主要成果:

  • 综合系统 (MaximQA) 大大减少了质量保证任务的持续时间.
  • 每项任务的CBCTQA分析速度快1-3分钟;每项任务的DMLC和温斯顿-卢茨测试速度快3-5分钟.
  • 准确性比较显示,大多数参数的结果相似,因不同计算方法而有小差异.

结论:

  • 综合QA计划,如MaximQA,减少了放射治疗QA任务所需的时间.
  • 使用集成质量保证系统与已建立的质量保证产品相比,可以保持高准确度.
  • 马克西姆QA在集成临床上显示了放射治疗QA显著的效率增长.