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Computed Tomography Image Estimation by Statistical Learning Methods
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- Bayisa, Fekadu L. (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik
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- Liu, Xijia (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik
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- Garpebring, Anders (author)
- Umeå universitet,Institutionen för strålningsvetenskaper
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- English.
- Other publication (other academic/artistic)
Abstract
Subject headings
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- There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images canbe utilised for attenuation correction, patient positioning, and dose planningin diagnostic and radiotherapy workflows. This study presents a statisticallearning method for CT image estimation. We have used predefined tissuetype information in a Gaussian mixture model to explore the estimation.The performance of our method was evaluated using cross-validation on realdata. In comparison with the existing model-based CT image estimationmethods, the proposed method has improved the estimation, particularly inbone tissues. Evaluation of our method shows that it is a promising methodto generate CT image substitutes for the implementation of fully MR-basedradiotherapy and PET/MRI applications.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Keyword
- Computed tomography
- magnetic resonance imaging
- CT image estimation
- pseudo-CT image
- supervised learning
- Gaussian mixture model
Publication and Content Type
- vet (subject category)
- ovr (subject category)
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- NATURAL SCIENCES
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