Abstract #2607

Complex-valued deep learning based denoising of gradient echo images in high-resolution quantitative susceptibility mapping

Sandhanakrishnan Ravichandran¹, Christof Boehm¹, Kilian Weiss², Alexander Ziller³, Georgios A Kaissis^1,3, Kerstin Hammernik⁴, Daniel Rueckert^3,5, Thomas Huber¹, Tabea Borde^1,6, Jakob Meineke⁷, Marcus Makowski¹, Eva Maria Fallenberg¹, and Dimitrios C Karampinos¹

¹Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ²Philips GmbH Market DACH, Hamburg, Germany, ³Artificial Intelligence in Healthcare and Medicine, Technical University of Munich, Munich, Germany, ⁴School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ⁵Department of Computing, Imperial College London, London, United Kingdom, ⁶Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, United States, ⁷Philips Research, Hamburg, Germany

Synopsis

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, Breast

Motivation: Quantitative susceptibility mapping (QSM) has recently been used to detect breast microcalcifications (MCs) which could be the precursor lesions to breast-carcinoma. However, acquiring high-resolution (HR) QSM maps reduces the signal-to-noise ratio (SNR), making detection of MCs challenging.

Goal(s): Improve the SNR in HR-QSM for better MCs visualization using deep-learning-based denoising.

Approach: A complex-valued bias-free CNN (CV-BFCNN), adapted from real-valued BFCNN, was trained on complex-valued MR data with Gaussian noise to denoise multi-echo gradient-echo images used for QSM processing.

Results: CV-BFCNN improves SNR in HR-QSM and processes complex-valued MR data directly when compared to real-valued BFCNN, and allows enhanced visualisation and detection of MCs.

Impact: The application of complex-valued deep-learning-based denoising in high-resolution QSM has substantially improved SNR and detection of micro-calcifications, a precursor to breast cancer. This helps QSM, an ionizing radiation-free alternative in detection and visualization of microcalcifications in the breast.

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