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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.

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Purpose Auxiliary devices such as immobilization systems should be considered in synthetic CT (sCT)-based treatment planning (TP) for MRI-only brain radiotherapy (RT). A method for auxiliary device definition in the sCT is introduced, and its dosimetric impact on the sCT-based TP is addressed. Methods T1-VIBE DIXON was acquired in an RT setup. Ten datasets were retrospectively used for sCT generation. Silicone markers were used to determine the auxiliary devices’ relative position. An auxiliary structure template (AST) was created in the TP system and placed manually on the MRI. Various RT mask characteristics were simulated in the sCT and investigated by recalculating the CT-based clinical plan on the sCT. The influence of auxiliary devices was investigated by creating static fields aimed at artificial planning target volumes (PTVs) in the CT and recalculated in the sCT. The dose covering 50% of the PTV (D50) deviation percentage between CT-based/recalculated plan (∆D50[%]) was evaluated. Results Defining an optimal RT mask yielded a ∆D50[%] of 0.2 ± 1.03% for the PTV and between −1.6 ± 3.4% and 1.1 ± 2.0% for OARs. Evaluating each static field, the largest ∆D50[%] was delivered by AST positioning inaccuracy (max: 3.5 ± 2.4%), followed by the RT table (max: 3.6 ± 1.2%) and the RT mask (max: 3.0 ± 0.8% [anterior], 1.6 ± 0.4% [rest]). No correlation between ∆D50[%] and beam depth was found for the sum of opposing beams, except for (45° + 315°). Conclusion This study evaluated the integration of auxiliary devices and their dosimetric influence on sCT-based TP. The AST can be easily integrated into the sCT-based TP. Further, we found that the dosimetric impact was within an acceptable range for an MRI-only workflow.