Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings.
Humans
Magnetic Resonance Imaging
/ methods
Artificial Intelligence
Female
Male
Adult
Middle Aged
Signal-To-Noise Ratio
Brain
/ diagnostic imaging
Artifacts
Aged
Data Compression
/ methods
Liver
/ diagnostic imaging
Algorithms
Kidney
/ diagnostic imaging
Young Adult
Spine
/ diagnostic imaging
Observer Variation
Knee
/ diagnostic imaging
Artificial intelligence assisted compressed sensing
Magnetic resonance imaging
Qualitative analysis
Quantitative analysis
Scan duration
Journal
BMC medical imaging
ISSN: 1471-2342
Titre abrégé: BMC Med Imaging
Pays: England
ID NLM: 100968553
Informations de publication
Date de publication:
21 Oct 2024
21 Oct 2024
Historique:
received:
15
08
2024
accepted:
11
10
2024
medline:
22
10
2024
pubmed:
22
10
2024
entrez:
21
10
2024
Statut:
epublish
Résumé
Conventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging. This study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen's kappa correlation coefficient (k) was employed to measure radiologists' inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS. The qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study's findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality. Integrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.
Sections du résumé
BACKGROUND
BACKGROUND
Conventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.
METHODS
METHODS
This study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen's kappa correlation coefficient (k) was employed to measure radiologists' inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.
RESULTS
RESULTS
The qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study's findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.
CONCLUSION
CONCLUSIONS
Integrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.
Identifiants
pubmed: 39434010
doi: 10.1186/s12880-024-01463-6
pii: 10.1186/s12880-024-01463-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
284Informations de copyright
© 2024. The Author(s).
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