Mika Kapanen1, Mikko Tenhunen. 1. Department of Oncology, Helsinki University Central Hospital, HUS, Finland. mika.kapanen@hus.fi
Abstract
BACKGROUND AND PURPOSE: In radiotherapy (RT), target soft tissues are best defined on MR images. In several cases, CT imaging is needed only for dose calculation and generation of digitally reconstructed radiographs (DRRs). Image co-registration errors between MRI and CT can be avoided by using MRI-only based treatment planning, especially in the pelvis. Since electron density information can not be directly derived from the MRI, a method is needed to convert MRI data into CT like data. We investigated whether there is a relationship between MRI intensity and Hounsfield unit (HU) values for the pelvic bones. The aim was to generate a method to convert bone MRI intensity into HU data surrogate for RT treatment planning. MATERIAL AND METHODS: The HU conversion model was generated for 10 randomly chosen prostate cancer patients and independent validation was performed in another 10 patients. Data consisted of 800 image voxels chosen within the pelvic bones in both T1/T2*-weighted gradient echo and CT images. Relation between MRI intensity and electron density was derived from calibrated HU-values. The proposed method was tested by constructing five "pseudo"-CT series. RESULTS: We found that the MRI intensity is related to the HU value within a HU range from 0 to 1400 within the pelvic bones. The mean prediction error of the conversion model was 135 HU. Dose calculation based on the pseudo-CT images was accurate and the generated DRRs were of good quality. CONCLUSIONS: The proposed method enables generation of clinically relevant pseudo-CT data for the pelvic bones from one MRI series. It is simpler than previously reported approaches which require either acquisition of several MRI series or T2* maps with special imaging sequences. The method can be applied with commercial clinical image processing software. The application requires segmentation of the bones in the MR images.
RCT Entities:
BACKGROUND AND PURPOSE: In radiotherapy (RT), target soft tissues are best defined on MR images. In several cases, CT imaging is needed only for dose calculation and generation of digitally reconstructed radiographs (DRRs). Image co-registration errors between MRI and CT can be avoided by using MRI-only based treatment planning, especially in the pelvis. Since electron density information can not be directly derived from the MRI, a method is needed to convert MRI data into CT like data. We investigated whether there is a relationship between MRI intensity and Hounsfield unit (HU) values for the pelvic bones. The aim was to generate a method to convert bone MRI intensity into HU data surrogate for RT treatment planning. MATERIAL AND METHODS: The HU conversion model was generated for 10 randomly chosen prostate cancerpatients and independent validation was performed in another 10 patients. Data consisted of 800 image voxels chosen within the pelvic bones in both T1/T2*-weighted gradient echo and CT images. Relation between MRI intensity and electron density was derived from calibrated HU-values. The proposed method was tested by constructing five "pseudo"-CT series. RESULTS: We found that the MRI intensity is related to the HU value within a HU range from 0 to 1400 within the pelvic bones. The mean prediction error of the conversion model was 135 HU. Dose calculation based on the pseudo-CT images was accurate and the generated DRRs were of good quality. CONCLUSIONS: The proposed method enables generation of clinically relevant pseudo-CT data for the pelvic bones from one MRI series. It is simpler than previously reported approaches which require either acquisition of several MRI series or T2* maps with special imaging sequences. The method can be applied with commercial clinical image processing software. The application requires segmentation of the bones in the MR images.
Authors: Tze Yee Lim; R Jason Stafford; Rajat J Kudchadker; Madhuri Sankaranarayanapillai; Geoffrey Ibbott; Arvind Rao; Karen S Martirosyan; Steven J Frank Journal: Phys Med Biol Date: 2014-04-28 Impact factor: 3.609