Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Purpose: MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo‐CT generation of the brain using a single‐acquisition, undersampled ultrashort echo time (UTE)‐mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190‐s, undersampled, single acquisition UTE‐mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point‐spread functions of three external MR markers. Two‐point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2∗ images (1/T2∗) were then estimated and were used to provide bone information. Three image features, i.e., Dixon‐fat, Dixon‐water, and R2∗, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c‐means (FCM) algorithm. A two‐step, automatic tissue‐assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo‐CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low‐dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM‐estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo‐CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low‐dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo‐CT generation.