Statistical Methods in Computed Tomography Image Estimation

TL;DR

This paper introduces a novel statistical learning method combining Gaussian mixture models and a classifier to improve CT image estimation from MR images, especially enhancing bone tissue accuracy for radiotherapy and PET/MRI applications.

Contribution

The study presents a new two-stage statistical learning approach that outperforms existing methods in CT estimation from MR images, particularly for bone tissues.

Findings

Improved CT estimation by 5% on the whole brain.

Enhanced bone tissue estimation by 23%.

Method shows promise for fully MR-based radiotherapy and PET/MRI.

Abstract

Purpose: There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilised for attenuation correction, patient positioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introduce a novel statistical learning approach for improving CT estimation from MR images and to compare the performance of our method with the existing model based CT image estimation methods. Methods: The statistical learning approach proposed here consists of two stages. At the training stage, prior knowledges about tissue-types from CT images were used together with a Gaussian mixture model (GMM) to explore CT image estimations from MR images. Since the prior knowledges are not available at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimate the tissue-types from MR images. For a new patient, the trained classifier and GMMs were used to predict CT image from MR images. The classifier and GMMs were validated by using voxel level 10-fold cross-validation and patient-level leave-one-out cross-validation, respectively. Results: The proposed approach has outperformance in CT estimation quality in comparison with the existing model based methods, especially on bone tissues. Our method improved CT image estimation by 5% and 23% on the whole brain and bone tissues, respectively. Conclusions: Evaluation of our method shows that it is a promising method to generate CT image substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications.