Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi‐echo phase training data. Issue 4 (17th June 2022)

• Record Type: Journal Article
• Authors: Hanspach, Jannis; Bollmann, Steffen; Grigo, Johanna; Karius, Andre; Uder, Michael; Laun, Frederik B.
• Is Part Of: Magnetic resonance in medicine. Volume 88:Issue 4(2022)
• Page Start: 1548
• Page End: 1560
• Publication Date: 2022-06-17
• Subjects: deep learning -- dipole inversion -- fat–water separation -- magnetic resonance imaging (MRI) -- prostate seed detection -- quantitative susceptibility mapping (QSM); Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29265
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To enable a fast and automatic deep learning–based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain. Methods: A UNET was trained to reconstruct susceptibility maps using synthetically generated, unwrapped, multi‐echo phase data as input. The RMS error with respect to synthetic validation data was computed. The method was tested on two in vivo knee and two pelvis data sets. Comparisons were made to a conventional fat–water separation pipeline by applying a commonly used graph‐cut algorithm, both without and with an extended mask for background field removal (FWS‐CONV‐QSM and FWS‐MASK‐CONV‐QSM, respectively). Several regions of interest were segmented and compared. Furthermore, the approach was tested on a prostate cancer patient receiving low‐dose‐rate brachytherapy, to detect and localize the seeds by MRI. Results: The RMS error was 0.292 ppm with FWS‐CONV‐QSM and 0.123 ppm for the UNET approach. Susceptibility maps were reconstructed much faster (< 10 s) and completely automatically (no background masking needed) by the UNET compared with the other applied techniques (5 min 51 s and 22 min 44 s for CONV‐QSM and FWS‐MASK‐CONV‐QSM, respectively. Background artifacts, fat–water swaps, and hypointense artifacts between I‐125 seeds of a patient receiving low‐dose brachytherapy in the prostate were largely reduced in the UNET approach. Conclusions: Deep learning–based QSM reconstruction, trained solely with synthetic data, is well‐suited to rapidly reconstructing high‐quality susceptibility maps in the presence of fat without needing masking for background field removal. Abstract : Click here for author‐reader discussions