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

Abstract

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. C 2015 American Association of Physicists in Medicine.

Neuromodulation with single‐element transcranial focused ultrasound in human thalamus

“…This may not be feasible in some cases or not recommended to expose participants to unnecessary ionizing radiation. As such, ultrashort TE MRI may be a realistic alternative for accurate imaging of bone (Su et al, ) that can produce pseudo‐CTs that could be used for accurate modeling of individual bone morphology.…”

Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy

“…So far, a variety of approaches have been proposed in the literature to estimate the electron density map for MR-only radiotherapy. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Atlas-based techniques, [6][7][8][9][10][11][12] tissue classification with bulk density assignment [18][19][20] and voxel-based approaches [13][14][15][16][17] are the three main strategies. Mis-classification of air and bone along with loss of within-cluster details, due to bulk density assignment, are the primary limitations of clustering techniques.…”

UTE‐mDixon‐based thorax synthetic CT generation

“…In Step 5, voxel values in the sCT are computed as a weighted sum of the HU values of the component tissue types. 12 The HU values of air, body air, fat, water, soft tissue, low-density bone, and dense bone are set to À1000, À850, À98, 0, 40, 385, and 657, respectively. 33 Special HU value assignment for the component tissue types was found to be required for voxels within the lung mask.…”