Hazen: an open-source MRI QA analysis software library

Motivation: Develop a standardised automated open-source MRI QA tool to reduce analysis time, user variability and licensing issues in quality assurance analysis. Goal(s): Develop Hazen, an open-source Python package automating MRI QA across MR vendors. Approach: Developed Hazen, an open-source Python package automating MRI QA using published methods; validated it against manual analysis and existing tools to improve efficiency and standardisation. Results: Hazen QA measurements closely matched manual and other software analyses with small percentage differences; reduced analysis time by ~75%, demonstrating improved efficiency and accuracy. Impact: Hazen enables clinicians and scientists to perform rapid, standardised MRI QA across multiple vendors, reducing analysis time by ~75%. This facilitates faster troubleshooting, improves imaging consistency, overcomes licensing barriers, and fosters collaboration - ultimately enhancing diagnostic quality and patient care.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0951
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Hazen: an open-source MRI QA analysis software library

Brianna Laureano, Blair F. Johnston, Dijana Vilić, Laurence H. Jackson et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

Hazen: an open-source MRI QA analysis software library

Brianna Laureano, Blair F. Johnston, Dijana Vilić, Laurence H. Jackson, Thomas Roberts, R. Thornley, Simon Shah, Mathias Löwe, Paul Wilson, L. P. Jenkins, A. J. Drysdale, Maria M. Buckley, Robert Johnstone, S J Culley, Laura Gabriel, John Tracey, Geoff Charles‐Edwards, Haris Shuaib, David J. Price, Rachael Franklin, Suhail Ahmad, Yassine Azma, John Ansell, H. H. Richardson, Maria Liljeroth, Lucrezia Cester, Zsofia Ratkai, Neil Heraghty, Ryan Satnarine
article en

Abstract

Motivation: Develop a standardised automated open-source MRI QA tool to reduce analysis time, user variability and licensing issues in quality assurance analysis. Goal(s): Develop Hazen, an open-source Python package automating MRI QA across MR vendors. Approach: Developed Hazen, an open-source Python package automating MRI QA using published methods; validated it against manual analysis and existing tools to improve efficiency and standardisation. Results: Hazen QA measurements closely matched manual and other software analyses with small percentage differences; reduced analysis time by ~75%, demonstrating improved efficiency and accuracy. Impact: Hazen enables clinicians and scientists to perform rapid, standardised MRI QA across multiple vendors, reducing analysis time by ~75%. This facilitates faster troubleshooting, improves imaging consistency, overcomes licensing barriers, and fosters collaboration - ultimately enhancing diagnostic quality and patient care.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Advanced MRI Techniques and Applications
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