Abstract #3081

Assessment of idiopathic inflammatory myopathy using muscle T2 mapping segmented by Deep Anatomical Federated Network (DAFNE)

Fengdan Wang¹, Francesco Santini², Jinxia Zhu³, Tom Hilbert^4,5,6, Tobias Kober^4,5,6, and Zhengyu Jin¹

¹Radiology, Peking Union Medical College Hospital, Beijing, China, ²University Hospital Basel, Basel, Switzerland, ³Siemens Healthcare Ltd., Beijing, China, ⁴Siemens Healthcare AG, Lausanne, Switzerland, ⁵Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁶École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Synopsis

Idiopathic Inflammatory Myopathy (IIM) is a group of immune-mediated myopathies, with high morbidity and mortality. Prior work showed that dedicated accelerated T2 mapping of bilateral thighs can be performed within 3 minutes at high resolution and can detect IIM-induced muscle inflammation. However, manual drawing methods to delineate regions of interest are subject to sampling errors. This study investigated the first utility of a fully automated deep-learning method for segmentation of T2 maps in 64 patients with IIM against healthy controls, with results confirming its feasibility, accuracy, and efficiency.

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