Automatic Analysis of ACR Phantom Images in MRI.
ACR MRI phantom
MRI image quality
Magnetic resonance imaging
image processing
medical
imaging
quality control
Journal
Current medical imaging
ISSN: 1573-4056
Titre abrégé: Curr Med Imaging
Pays: United Arab Emirates
ID NLM: 101762461
Informations de publication
Date de publication:
2020
2020
Historique:
received:
03
03
2019
revised:
13
07
2019
accepted:
17
08
2019
entrez:
16
10
2020
pubmed:
17
10
2020
medline:
28
7
2021
Statut:
ppublish
Résumé
Quality Assurance (QA) of Magnetic Resonance Imaging (MRI) system is an essential step to avoid problems in diagnosis when image quality is low. It is considered a patient safety issue. The accreditation program of the American College of Radiology (ACR) includes a standardized image quality measurement protocol. However, it has been shown that human testing by visual inspection is not objective and not reproducible. The overall goal of the present paper was to develop and implement a fully automated method for accurate image analysis to increase its objectivity. It can positively impact the QA process by decreasing the reaction time, improving repeatability, and by reducing operator dependency. The proposed QA procedures were applied to ten clinical MRI scanners. The performance of the automated procedure was assessed by comparing the test results with the decisions made by trained MRI technologists according to ACR guidelines. The p-value, correlation coefficient of the manual and automatic measurements were also computed using the Pearson test. Compared to the manual process, the use of the proposed approach can significantly reduce the time requirements while maintaining consistency with manual measurements and furthermore, decrease the subjectivity of the results. Accordingly, a strong correlation was found and the corresponding p-value was much lower than the significance level of 0.05 indicating a good agreement between the two measurements.
Sections du résumé
BACKGROUND
Quality Assurance (QA) of Magnetic Resonance Imaging (MRI) system is an essential step to avoid problems in diagnosis when image quality is low. It is considered a patient safety issue. The accreditation program of the American College of Radiology (ACR) includes a standardized image quality measurement protocol. However, it has been shown that human testing by visual inspection is not objective and not reproducible.
METHODS
The overall goal of the present paper was to develop and implement a fully automated method for accurate image analysis to increase its objectivity. It can positively impact the QA process by decreasing the reaction time, improving repeatability, and by reducing operator dependency. The proposed QA procedures were applied to ten clinical MRI scanners. The performance of the automated procedure was assessed by comparing the test results with the decisions made by trained MRI technologists according to ACR guidelines. The p-value, correlation coefficient of the manual and automatic measurements were also computed using the Pearson test.
RESULTS AND CONCLUSION
Compared to the manual process, the use of the proposed approach can significantly reduce the time requirements while maintaining consistency with manual measurements and furthermore, decrease the subjectivity of the results. Accordingly, a strong correlation was found and the corresponding p-value was much lower than the significance level of 0.05 indicating a good agreement between the two measurements.
Identifiants
pubmed: 33059559
pii: CMIR-EPUB-100611
doi: 10.2174/1573405615666190903145343
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
892-901Informations de copyright
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