Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB)

Purpose: CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop a deep learning method to synthesize pseudo-CT (pCT) images for MR high-resolution pediatric cranial bone imaging to eliminating ionizing radiation from CT. Methods: 3D golden-angle stack-of-stars MRI were obtained from 44 pediatric participants. Two patch-based residual UNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third residual UNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCT_NetWH) in the brain area and NetBA (pCT_NetBA) in the nonbrain area were combined to generate pCT_Com. A manual processing method using inverted MR images was also employed for comparison. Results: pCT_Com (68.01 ± 14.83 HU) had significantly smaller mean absolute errors (MAEs) than pCT_NetWH (82.58 ± 16.98 HU, P < 0.0001) and pCT_NetBA (91.32 ± 17.2 HU, P < 0.0001) in the whole head. Within cranial bone, the MAE of pCT_Com (227.92 ± 46.88 HU) was significantly lower than pCT_NetWH (287.85 ± 59.46 HU, P < 0.0001) but similar to pCT_NetBA (230.20 ± 46.17 HU). Dice similarity coefficient of the segmented bone was significantly higher in pCT_Com (0.90 ± 0.02) than in pCT_NetWH (0.86 ± 0.04, P < 0.0001), pCT_NetBA (0.88 ± 0.03, P < 0.0001), and inverted MR (0.71 ± 0.09, P < 0.0001). Dice similarity coefficient from pCT_Com demonstrated significantly reduced age dependence than inverted MRI. Furthermore, pCT_Com provided excellent suture and fracture visibility comparable to CT. Conclusion: MR high-resolution pediatric cranial bone imaging may facilitate the clinical translation of a radiation-free MR cranial bone imaging method for pediatric patients.