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Statistical learning in computed tomography image estimation
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. (Mathematical Statistics)ORCID-id: 0000-0001-5673-620X
2018 (Engelska)Ingår i: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, nr 12, s. 5450-5460Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2018. Vol. 45, nr 12, s. 5450-5460
Nyckelord [en]
Computed tomography, CT image estimation, Gaussian mixture model, magnetic resonance imaging, supervised learning
Nationell ämneskategori
Sannolikhetsteori och statistik Medicinsk bildbehandling
Forskningsämne
matematisk statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-153283DOI: 10.1002/mp.13204ISI: 000452799400010PubMedID: 30242845Scopus ID: 2-s2.0-85056189706OAI: oai:DiVA.org:umu-153283DiVA, id: diva2:1263302
Projekt
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Forskningsfinansiär
Vetenskapsrådet, 340-2013-5342Tillgänglig från: 2018-11-15 Skapad: 2018-11-15 Senast uppdaterad: 2019-01-07Bibliografiskt granskad

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Bayisa, FekaduLiu, XijiaGarpebring, AndersYu, Jun

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Institutionen för matematik och matematisk statistikInstitutionen för strålningsvetenskaper
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Medical physics (Lancaster)
Sannolikhetsteori och statistikMedicinsk bildbehandling

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