Voxel-wise uncertainty in CT substitute derived from MRI

• Published in: Medical physics (Lancaster) 2012-06, Vol.39 (6), p.3283-3290
• Main Authors: Johansson, Adam, Karlsson, Mikael, Yu, Jun, Asklund, Thomas, Nyholm, Tufve
• Format: Article
• Language: English
• Subjects: attenuation correction; biomedical MRI; bone; brain; Computed tomography; computed tomography substitute; Computerised tomographs; computerised tomography; Digital computing or data processing equipment or methods, specially adapted for specific applications; Distribution theory and Monte Carlo studies; dose calculation; Gaussian distribution; Gaussian mixture; Giant magnetoresistance; Image data processing or generation, in general; Image Processing, Computer-Assisted - methods; image registration; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical Image Processing; Medical image segmentation; Medical imaging; Medical magnetic resonance imaging; Medicinsk bildbehandling; Positron emission tomography; Probability Theory and Statistics; quality assurance; Radiation therapy; regression analysis; Sannolikhetsteori och statistik; Sensitivity and Specificity; Tissues; Tomography, X-Ray Computed - methods; ultrashort echo time; Uncertainty
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4711807

Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences. Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities. Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation. Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.