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

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.