Female pelvic synthetic CT generation based on joint intensity and shape analysis

Using MRI for radiotherapy treatment planning and image guidance is appealing as it provides superior soft tissue information over CT scans and avoids possible systematic errors introduced by aligning MR to CT images. This study presents a method that generates Synthetic CT (MRCT) volumes by perform...

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Bibliographic Details
Published in:Physics in medicine & biology 2017-04, Vol.62 (8), p.2935-2949
Main Authors: Liu, Lianli, Jolly, Shruti, Cao, Yue, Vineberg, Karen, Fessler, Jeffrey A, Balter, James M
Format: Article
Language:English
Subjects:
bone shape model
Female
Humans
magnetic resonance imaging
Magnetic Resonance Imaging - methods
Models, Theoretical
Pelvic Bones - diagnostic imaging
Pelvic Neoplasms - diagnostic imaging
Pelvic Neoplasms - pathology
Pelvic Neoplasms - radiotherapy
Prospective Studies
radiation therapy
Radiographic Image Interpretation, Computer-Assisted - methods
Radiotherapy Planning, Computer-Assisted - methods
Radiotherapy, Intensity-Modulated - methods
synthetic CT
tissue classification
Tomography, X-Ray Computed - methods
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Summary:Using MRI for radiotherapy treatment planning and image guidance is appealing as it provides superior soft tissue information over CT scans and avoids possible systematic errors introduced by aligning MR to CT images. This study presents a method that generates Synthetic CT (MRCT) volumes by performing probabilistic tissue classification of voxels from MRI data using a single imaging sequence (T1 Dixon). The intensity overlap between different tissues on MR images, a major challenge for voxel-based MRCT generation methods, is addressed by adding bone shape information to an intensity-based classification scheme. A simple pelvic bone shape model, built from principal component analysis of pelvis shape from 30 CT image volumes, is fitted to the MR volumes. The shape model generates a rough bone mask that excludes air and covers bone along with some surrounding soft tissues. Air regions are identified and masked out from the tissue classification process by intensity thresholding outside the bone mask. A regularization term is added to the fuzzy c-means classification scheme that constrains voxels outside the bone mask from being assigned memberships in the bone class. MRCT image volumes are generated by multiplying the probability of each voxel being represented in each class with assigned attenuation values of the corresponding class and summing the result across all classes. The MRCT images presented intensity distributions similar to CT images with a mean absolute error of 13.7 HU for muscle, 15.9 HU for fat, 49.1 HU for intra-pelvic soft tissues, 129.1 HU for marrow and 274.4 HU for bony tissues across 9 patients. Volumetric modulated arc therapy (VMAT) plans were optimized using MRCT-derived electron densities, and doses were recalculated using corresponding CT-derived density grids. Dose differences to planning target volumes were small with mean/standard deviation of 0.21/0.42 Gy for D0.5cc and 0.29/0.33 Gy for D99%. The results demonstrate the accuracy of the method and its potential in supporting MRI only radiotherapy treatment planning.
ISSN:0031-9155
1361-6560
DOI:10.1088/1361-6560/62/8/2935