Magnetic resonance imaging quality control, quality assurance and quality improvement

Sreedher, Gayathri; Ho, Mai‐Lan; Smith, Mark A.; Udayasankar, Unni K.; Risacher, Seretha; Rapalino, Otto; Greer, Mary‐Louise C.; Doria, A.; Gee, Michael S.

doi:10.1007/s00247-021-05043-6

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Variable quality models are examined by Sreedher et al [7] in both competitive and single-agent frameworks. To facilitate communication among the various communities with an interest in quality management, our objectives are to (1) give the reader a perspective on the state of the art in this field; (2) identify the distinctions between the various concepts of quality; and (3) develop a research agenda for the field.…”

Section: Related Workmentioning

confidence: 99%

“…Many software and high resolution cameras are a possible cost issue Objects with reflective surfaces cause problems due to reflected background noise. There needs to be more emphasis on software comparison research which would help companies implement this type of quality inspection system and train employees [7] Work flow is well described considering different stages…”

Section: Refmentioning

confidence: 99%

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An Improved Intelligent Cloud-based Structure for Automated Product Quality Control

Shrivastava,

Udgir

2024

IJLERA

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Regardless of the products, quality is the most crucial concern for all manufacturers in any manufacturing system. Quality management is a collection of procedures and instruments that can improve the overall system performance of a manufacturing facility. The dynamic nature of today's business climate and intense rivalry in the market have made quality tools and continuous improvement (CI) essential for long-term success and overcoming obstacles. The methodology of automating quality assurance with cloud computing is also explored in this study. Given how popular cloud computing has become lately. An increasing number of manufacturers are thinking about switching to a cloud-based quality system for their quality management. Because of this, the cloud's power, agility, and cost advantages are now essential for surviving in today's dynamic and uncertain manufacturing environment. Thus, utilizing machine learning methods and cloud computing, this work has provided a service-based proposal system for visual quality assurance. The model's accuracy in detecting defects in manufactured parts and response time are assessed. The model's average accuracy was estimated to be approximately 93%, while the average reaction delay was estimated to be approximately 8 seconds.

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Section: Related Workmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

An Improved Intelligent Cloud-based Structure for Automated Product Quality Control

Shrivastava,

Udgir

2024

IJLERA

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“…Often, these protocols are only explicitly described in the context of large-scale multicenter studies. Depending on the main questions, they may focus on different aspects of the MRI data, e.g., signal-to-noise ratio (SNR), image homogeneity, differentiation between tissue classes or, especially in the context of functional MRI (fMRI) studies, temporal stability (for an overview on the set-up of QA protocols for MR scanners, see (Sreedher et al 2021); for an in-depth review on previous studies which developed QA programs, including QA phantoms, QA metrics and preprocessing pipelines, see (Lu et al 2019)).…”

Section: Introductionmentioning

confidence: 99%

The German research consortium for the study of bipolar disorder (BipoLife): A quality assurance protocol for MR neuroimaging data

Vogelbacher,

Sommer,

Bopp

et al. 2023

Preprint

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Background The German multicenter research consortium BipoLife aims to investigate the mechanisms underlying bipolar disorders. It focuses in particular on people at high risk of developing the disorder and young patients in the early stages of the disease. Functional and structural magnetic resonance imaging (MRI) data was collected in all participating centers. The collection of neuroimaging data in a longitudinal, multicenter study requires the implementation of a comprehensive quality assurance (QA) protocol. Here, we describe this protocol and its impact on the analysis of data on the patient-level. Methods The QA protocol consisted of (1) a training of participating research staff, (2) regular phantom measurements to evaluate the MR scanner performance and its temporal stability across the course of the study, and (3) the assessment of the quality of human MRI data by evaluating a variety of image metrics (e.g., signal-to-noise ratio, ghosting level). In this article, we will provide an overview on these QA procedures and show exemplarily the influence of its application on the results different analysis pipelines. Discussion The QA protocol helped to characterize the various MR scanners, record their performance over the course of the study and detect possible malfunctions at an early stage. It also assessed the quality of the human MRI data systematically to characterize its influence on various analyses. Furthermore, by setting up and publishing this protocol, we define standards that must be considered when analyzing data from the BipoLife consortium. It further promotes approaches to systematically consider aspects of data quality and to implement standardized analysis methods and subject selection criteria. In the long term, it will help to increase the chance of achieving clinically relevant results.

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“…Quality indicators serve as pivotal tools catering to the requirements of healthcare performance assessment [2][3][4]. Incorporating these indicators into continuous improvement strategies has the potential to raise the standards of ultrasound services.…”

Section: Introductionmentioning

confidence: 99%

Development of Quality Indicators for the Ultrasound Department through a Modified Delphi Method

Zeng,

Gu,

et al. 2023

Diagnostics

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This study aims to establish precise quality indicators for evaluating and enhancing ultrasound performance, employing a methodology based on a comprehensive review of the literature, expert insights, and practical application experiences. We conducted a thorough review of both the domestic and international literature on ultrasound quality control to identify potential indicators. A dedicated team was formed to oversee the complete indicator development process. Utilizing a three-round modified Delphi method, we sought expert opinions through personalized email correspondence. Subsequently, data from diverse hospital indicators were collected to validate and assess feasibility. A novel set of seven indicators was compiled initially, followed by the convening of a 36-member nationally representative expert panel. After three rounds of meticulous revisions, consensus was reached on 13 indicators across three domains. These finalized indicators underwent application in various hospital settings, demonstrating their initial validity and feasibility. The development of thirteen ultrasound quality indicators represents a significant milestone in evaluating ultrasound performance. These indicators empower hospitals to monitor changes in quality effectively, fostering efficient quality management practices.

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Magnetic resonance imaging quality control, quality assurance and quality improvement

Cited by 9 publications

References 24 publications

An Improved Intelligent Cloud-based Structure for Automated Product Quality Control

An Improved Intelligent Cloud-based Structure for Automated Product Quality Control

The German research consortium for the study of bipolar disorder (BipoLife): A quality assurance protocol for MR neuroimaging data

Development of Quality Indicators for the Ultrasound Department through a Modified Delphi Method

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