Creating a Pseudo-CT from MRI for MRI-only based Radiation Therapy Planning | Daniel Andreasen
| | Abstract | Background: In the planning process of external radiation therapy, CT is used as the main imaging modality. The advantage of using CT is that the voxel intensity values are directly related to electron density which is needed for dose calculations. Furthermore, CT provides an accurate geometrical representation of bone needed for constructing digitally reconstructed radiographs. In recent years, interest in replacing CT with MRI in the treatment planning process has emerged. This is due to the fact that MRI provides a superior soft tissue contrast; a desirable property that could increase the accuracy of target and risk volume delineation. The challenge in replacing CT with MRI is that the MRI intensity values are not related to electron densities and conventional MRI sequences cannot obtain signal from bone.
The purpose of this project was to investigate the use of Gaussian Mixture Regression (GMR) and Random Forest regression (RaFR) for creating a pseudo-CT image from MRI images. Creating a pseudo-CT from MRI would eliminate the need for a real CT scan and thus facilitate an MRI-only work ow in the radiation therapy planning process. The use of GMR for pseudo-CT creation has previously been reported so the reproducibility of these results was investigated. dUTE and mDixon MRI image sets as well as Local Binary Pattern (LBP) feature images were investigated as input to the regression models.
Materials and methods: Head scans of three patients xated for whole brain radiation therapy were acquired on a 1 T open MRI scanner with ex coils. dUTE and mDixon image sets were obtained. CT head scans were also acquired using a standard protocol. A registration of the CT and MRI image sets was carried out and LBP feature images were derived from the dUTE image sets.
All RaFR and GMR models were trained with the dUTE image sets as basic input. Some of the models were trained with an additional mDixon or LBP input in order to investigate if these inputs could improve the quality of the predicted pseudo-CT. More speci cally, the impact of adding the LBP input was investigated using RaFR and the impact of adding an mDixon input was investigated using both RaFR and GMR. A study of the optimal tree depth for RaFR was also carried out. The quality of the resulting pseudo-CTs was quanti ed in terms of the prediction deviation, the geometrical accuracy of bone and the dosimetric accuracy.
Results: In the LBP input study, the results indicated that using LBPs could improve the quality of the pseudo-CT.
In the mDixon input study, the results suggested that both RaFR and GMR models were improved when adding the mDixon input. The improvement was mainly observed in terms of smaller prediction deviations in the bone region of the pseudo-CTs and a greater geometrical accuracy. When comparing RaFR and GMR, it was found that using RaFR produced pseudo-CTs with the smallest prediction deviations and greatest geometrical accuracy. In terms of the dosimetric accuracy, the dierence was less clear.
Conclusion: The use of GMR and RaFR for creating a pseudo-CT image from MRI images was investigated. The reproducibility of previously reported results using GMR was demonstrated. Furthermore, the impact of adding LBP and mDixon inputs to the regression models was demonstrated and showed that an improvement of the pseudo-CT could be obtained. The results serves as a motivation for further studies using more data and improved feature images. | | Type | Master's thesis [Academic thesis] | | Year | 2013 | | Publisher | Technical University of Denmark, DTU Compute, E-mail: compute@compute.dtu.dk | | Address | Matematiktorvet, Building 303-B, DK-2800 Kgs. Lyngby, Denmark | | Series | IMM-M.Sc.-2013-10 | | Note | DTU supervisor: Koen Van Leemput, Ph.D., kvle@dtu.dk, DTU Compute | | Electronic version(s) | [pdf] | | Publication link | http://www.compute.dtu.dk/English.aspx | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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