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Abstract #3662

Inter-Frame distance metric-based Auto-Inversion-Time prediction for Cardiac MR

Subhashis Banerjee1, Sudhanya Chatterjee1, Gaspar Delso2, Sajith Rajamani1, Martin Janich3, and Dattesh Dayanand Shanbhag1
1GE HealthCare, Bengaluru, India, 2GE HealthCare, Barcelona, Spain, 3GE HealthCare, Munich, Germany

Synopsis

Keywords: Myocardium, MR Value, Ischemia, Myocardium, TI Scout

Motivation: Optimal inversion time (TI) is critical for Late Gadolinium Enhancement (LGE) cardiac imaging but currently TI estimation done visually on TI-Scout which introduces variations based on operator skill.

Goal(s): Completely automated TI prediction (Auto-TI) on post-contrast TI scout data opens access to cardiac imaging.

Approach: We abstract cropped TI-Scout frame data into inter-frame statistical distance space (Hellinger and Jensen-Shannon distance metrics) and do optimal TI prediction using a deep-learning network operating on distance maps.

Results: We obtained 82% accuracy in optimal TI prediction. We notice excellent performance in cases with motion, metal, pediatric and different cardiac views, even though sparse in training pool

Impact: Abstraction of time-series based TI scout data to a metric space and consequent Auto-TI prediction offers resilience to artifacts with a smaller footprint model, reduces training data variety needs, and offers explainability for prediction

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Keywords