Improved myocardial T1 mapping accuracy with Deep Learning reconstruction of low flip angle MOLLI series
Gaspar Delso1, Pablo García-Polo2, Margarita Gorodezky3, Ben Ariff4, Vicente Martínez de Vega5, and Javier Urmeneta6
1GE Healthcare, Barcelona, Spain, 2GE Healthcare, Madrid, Spain, 3GE Healthcare, London, United Kingdom, 4Imperial College Healthcare NHS Trust, London, United Kingdom, 5Servicio de diagnóstico por la imagen, Hospital Universitario Quirónsalud, Madrid, Spain, 6Servicio de cardiología, Hospital Universitario Quirónsalud, Madrid, Spain
In this study, we explore the possibility of leveraging Deep Learning regularized reconstruction to enable lower flip angle MOLLI acquisition for myocardial T1 mapping. It has been shown in the past that lowering flip angle helps reduce various artifact sources, at the cost of lower signal to noise ratio. Regularized reconstruction can effectively manage image noise as well as increase feature sharpness. This hypothesis has been tested on a group of clinical patients referred for a cardiac MR exam.
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