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Abstract #2132

Fully Automated Cardiac Bounding Box Detection for Localized Higher-Order Shimming Using Deep Learning

Asha K. Kumara Swamy1,2, Chandrashekar M. Patil2, Punith B. Venkategowda1, Vikram Nagalli1, Michael Wangler3, Michaela Schmidt4, and Jens Wetzl4

1Siemens Healthcare Private Ltd., Bangalore, India, 2Vidya Vardhaka College of Engineering, Mysore, India, 3Syngo, Siemens Healthcare GmbH, Forchheim, Germany, 4Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany

Localized higher-order shimming is a common method for improving image quality in phase sensitive sequences used in cardiac imaging, but requires the manual placement of a three-dimensional bounding box around the heart in which the localized shimming is performed. We present an automated method for detecting such a bounding box from the localizer images using Deep Learning. Two-dimensional bounding boxes are first detected in each localizer slice and then combined to one three-dimensional bounding box. We compare two approaches, either training individual models for each localizer orientation or a joint model for all orientations.

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