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Introduction of an abbreviated breast MRI service in the UK as part of the BRAID trial: practicalities, challenges, and future directions.

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Vinnicombe, S 
Harvey, H 
Healy, NA 
Papalouka, V 
Schiller, A 


Abstract Introduction Women with mammographically dense breasts have an increased risk of breast cancer. Dense breasts can limit detection of small breast lesions due to overlapping fibroglandular tissue. BRAID is a randomised, multi-centre UK study assessing the impact of supplementary imaging, including abbreviated breast MRI (ABB-MRI), in detection of breast cancer in women aged 50-70 years with dense breasts. ABB-MRI is a shorter protocol breast MRI, allowing much faster acquisition times. The aim of this study is to review the feasibility of introducing ABB-MRI into the NHS setting as a part of a research trial.

Protocol ABB-MRI is performed on a 1.5 tesla MRI scanner. Sequences obtained include a 2 mm axial T2 weighted sequence, a dual echo (Dixon water/fat separation) 3D DCE acquisition consisting of one pre- and two post-contrast images with generation of post-contrast subtracted images and post-processed MIP images.

Scheduling ABB-MRIs are performed on a dedicated breast MRI list. Participants arrive 30 minutes prior to appointment time with pre-filled safety questionnaire. ABB-MRIs are scheduled for a 20-minute appointment slot, allowing 10 women to be scanned in a 3-hour session. Specific roles are allocated to staff members.

Improving Efficiency of ABB-MRI in the UK Challenges encountered are similar to those encountered with FDP: IV cannulation, BMI and claustrophobia. Dedicated MR sessions with standard contrast doses and prefilled safety questionnaires help improve efficiency of sessions and allow examination times under 15 minutes.



Breast, Breast Neoplasms, Female, Humans, Magnetic Resonance Imaging, United Kingdom

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Clin Radiol

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Elsevier BV
Cancer Research UK (A26884)
Department of Health (via National Institute for Health Research (NIHR)) (NF-SI-0515-10067)
GE Healthcare NIHR Senior Investigator Award