Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information: potential application for MR-only radiotherapy and attenuation correction in positron emission tomography
2013 (English)In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 52, no 7, 1369-1373 p.Article in journal (Refereed) Published
Background: Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. Material and methods: MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. Results: The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. Conclusions: This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.
Place, publisher, year, edition, pages
2013. Vol. 52, no 7, 1369-1373 p.
Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
IdentifiersURN: urn:nbn:se:umu:diva-81537DOI: 10.3109/0284186X.2013.819119ISI: 000324776100016PubMedID: 23984810OAI: oai:DiVA.org:umu-81537DiVA: diva2:655944