# Model-based Computed Tomography Image Estimation: Partitioning Approach

## TL;DR

This paper introduces a partitioning approach using skew Gaussian and Gaussian mixture models to improve CT image estimation from MR images, focusing on enhanced bone tissue accuracy for MR-based radiotherapy.

## Contribution

It proposes novel mixture models that partition data into tissue types, significantly improving bone tissue estimation over existing model-based methods.

## Key findings

- Outperforms existing methods in dense bone tissue estimation
- Uses partitioning to improve CT estimation from MR images
- Validated with leave-one-out cross-validation on real data

## Abstract

There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. The existing model-based methods perform poorly on bone tissues. This paper was aimed at obtaining improved bone tissue estimation. Skew Gaussian mixture model and Gaussian mixture model were proposed to investigate CT image estimation from MR images by partitioning the data into two major tissue types. The performance of the proposed models was evaluated using leave-one-out cross-validation method on real data. In comparison with the existing model-based approaches, the model-based partitioning approach outperformed in bone tissue estimation, especially in dense bone tissue estimation.