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- Bayisa, FekaduUmeå universitet,Institutionen för matematik och matematisk statistik (author)
Statistical learning in computed tomography image estimation
- Article/chapterEnglish2018
Publisher, publication year, extent ...
- 2018-11-08
- John Wiley & Sons,2018
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:umu-153283
- https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-153283URI
- https://doi.org/10.1002/mp.13204DOI
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- Language:English
- Summary in:English
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- Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance 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 trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 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 CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications
Subject headings and genre
- NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
- NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
- TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
- Computed tomography
- CT image estimation
- Gaussian mixture model
- magnetic resonance imaging
- supervised learning
- matematisk statistik
- Mathematical Statistics
Added entries (persons, corporate bodies, meetings, titles ...)
- Liu, XijiaUmeå universitet,Institutionen för matematik och matematisk statistik(Swepub:umu)xili0017 (author)
- Garpebring, AndersUmeå universitet,Radiofysik(Swepub:umu)anga0014 (author)
- Yu, Jun,1962-Umeå universitet,Institutionen för matematik och matematisk statistik,Mathematical Statistics(Swepub:umu)juyu0002 (author)
- Umeå universitetInstitutionen för matematik och matematisk statistik (creator_code:org_t)
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- In:Medical physics (Lancaster): John Wiley & Sons45:12, s. 5450-54600094-24052473-4209
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