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Abstract #0951

Hazen: an open-source MRI QA analysis software library

Molly Buckley1, Zsofia Ratkai1, Thomas Roberts1, Ryan Satnarine1, Jane Ansell1, Liz Gabriel1, Yassine Azma2, Rebecca Thornley1, Lewis Jenkins1, Beatriz Laureano1, Melissa Lowe1, Simon Shah1, Rachael Franklin1, Robert Johnstone1, Sadeed Ahmad3, Maria Liljeroth3, Laurence Jackson1, Lucrezia Cester1, Dika Vilic1, Siân Culley1, John Tracey4, Hamish Richardson5, Blair Johnston6, Alex Drysdale7, Paul Wilson8, Neil Heraghty9, David Price1, Haris Shuaib1, and Geoff Charles-Edwards1
1Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom, 2Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Royal Surrey County Hospital NHS Foundation Trust, London, United Kingdom, 4NHS Highland, Inverness, United Kingdom, 5NHS Lothian, Edinburgh, United Kingdom, 6NHS Greater Glasgow and Clyde, Glasgow, United Kingdom, 7Swansea Bay University Health Board, Swansea, United Kingdom, 8Northern Ireland Regional Medical Physics Service, Belfast, United Kingdom, 9King’s College Hospital NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Software Tools, Software Tools, QA, Open Source

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.

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Keywords