Program Information
A Dixon Based Pseudo-CT Generation Method for MR-Only Radiotherapy Treatment Planning of the Pelvis and Head and Neck
M. Maspero1, P.R. Seevinck2 , G.J. Meijer1 , J.J.W. Lagendijk1 , M.A. Viergever2 , C.A.T. van den Berg1*, (1) Department of Radiotherapy, UMC Utrecht, Nederland, (2) Image Science Institute, UMC Utrecht, Nederland
Presentations
SU-E-J-219 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: To develop an image processing method for MRI-based generation of electron density maps, known as pseudo-CT (pCT), without usage of model- or atlas-based segmentation, and to evaluate the method in the pelvic and head-neck region against CT.
Methods: CT and MRI scans were obtained from the pelvic region of four patients in supine position using a flat table top only for CT. Stratified CT maps were generated by classifying each voxel based on HU ranges into one of four classes: air, adipose tissue, soft tissue or bone.
A hierarchical region-selective algorithm, based on automatic thresholding and clustering, was used to classify tissues from MR Dixon reconstructed fat, In-Phase (IP) and Opposed-Phase (OP) images. First, a body mask was obtained by thresholding the IP image. Subsequently, an automatic threshold on the Dixon fat image differentiated soft and adipose tissue. K-means clustering on IP and OP images resulted in a mask that, via a connected neighborhood analysis, allowing the user to select the components corresponding to bone structures.
The pCT was estimated through assignment of bulk HU to the tissue classes. Bone-only Digital Reconstructed Radiographs (DRR) were generated as well. The pCT images were rigidly registered to the stratified CT to allow a volumetric and voxelwise comparison. Moreover, pCTs were also calculated within the head-neck region in two volunteers using the same pipeline.
Results: The volumetric comparison resulted in differences <1% for each tissue class. A voxelwise comparison showed a good classification, ranging from 64% to 98%. The primary misclassified classes were adipose/soft tissue and bone/soft tissue. As the patients have been imaged on different table tops, part of the misclassification error can be explained by misregistration.
Conclusion: The proposed approach does not rely on an anatomy model providing the flexibility to successfully generate the pCT in two different body sites.
Funding Support, Disclosures, and Conflict of Interest: This research is founded by ZonMw IMDI Programme, project name :"RASOR sharp: MRI based radiotherapy planning using a single MRI sequence ", project number: 10-104003010
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