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Statistical learning in computed tomography image estimation
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DOI：10.1002/mp.13204.png)
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摘要
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Purpose There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilized 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 knowledge about tissue types from CT images was used together with a Gaussian mixture model (GMM) to explore CT image estimations from MR images. Since the prior knowledge is 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 tenfold 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.
Keyword:
computed tomography
CT image estimation
Gaussian mixture model
magnetic resonance imaging
supervised learning
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