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. 2013 Dec 7;58(23):8419-35.
doi: 10.1088/0031-9155/58/23/8419. Epub 2013 Nov 11.

Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy

Shu-Hui Hsu  1 , Yue Cao, Ke Huang, Mary Feng, James M Balter
Affiliations

Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy

Shu-Hui Hsu et al. Phys Med Biol. .

Abstract

Magnetic resonance (MR) images often provide superior anatomic and functional information over computed tomography (CT) images, but generally are not used alone without CT images for radiotherapy treatment planning and image guidance. This study aims to investigate the potential of probabilistic classification of voxels from multiple MRI contrasts to generate synthetic CT ('MRCT') images. The method consists of (1) acquiring multiple MRI volumes: T1-weighted, T2-weighted, two echoes from a ultra-short echo time (UTE) sequence, and calculated fat and water image volumes using a Dixon method, (2) classifying tissues using fuzzy c-means clustering with a spatial constraint, (3) assigning attenuation properties with weights based on the probability of individual tissue classes being present in each voxel, and (4) generating a MRCT image volume from the sum of attenuation properties in each voxel. The capability of each MRI contrast to differentiate tissues of interest was investigated based on a retrospective analysis of ten patients. For one prospective patient, the correlation of skull intensities between CT and MR was investigated, the discriminatory power of MRI in separating air from bone was evaluated, and the generated MRCT image volume was qualitatively evaluated. Our analyses showed that one MRI volume was not sufficient to separate all tissue types, and T2-weighted images was more sensitive to bone density variation compared to other MRI image types. The short echo UTE image showed significant improvement in contrasting air versus bone, but could not completely separate air from bone without false labeling. Generated MRCT and CT images showed similar contrast between bone and soft/solid tissues. These results demonstrate the potential of the presented method to generate synthetic CT images to support the workflow of radiation oncology treatment planning and image guidance.

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Figures

Figure 1
Figure 1
(a) CT and (b)–(h) MR images for the prospective protocol patient.
Figure 2
Figure 2
Example ROIs on T1-weighted images: (a) bone, (b) fat, (c) fluid, (d) WM, (e) GM, and (f) air.
Figure 3
Figure 3
Overall scheme of using multiple MRI volumes to generate MRCT.
Figure 4
Figure 4
An example of intensity variations for various tissue types on T1-weighted, T2-weighted, fat and water images. The error bar represents one standard deviation of tissue intensities within the ROI.
Figure 5
Figure 5
Results from FCM classification of each of 10 patients. For each tissue/material type, the average optimized intensity value is shown as well as their standard deviations across the patient population (error bars).
Figure 6
Figure 6
MR intensities for CT-defined ROIs within the skull for a patient scanned in a mask with an 18-channel anterior surface coil and a 4-channel posterior surface coil. (a) Absolute and (b) normalized intensities are shown.
Figure 7
Figure 7
(a) Contours of air (magenta) and bone (white) generated from CT images using thresholds of < −400 and > 200, respectively. Contours of air (yellow) are shown as generated from UTE1 images using threshold values of 50 (b), 100 (c) and 200 (d).
Figure 7
Figure 7
(a) Contours of air (magenta) and bone (white) generated from CT images using thresholds of < −400 and > 200, respectively. Contours of air (yellow) are shown as generated from UTE1 images using threshold values of 50 (b), 100 (c) and 200 (d).
Figure 8
Figure 8
ROC curves and areas under the curves (AUC) for differentiation of air from bone using UTE1, T1-weighted, UTE2, and T2-weighted images.
Figure 9
Figure 9
Correlations between CT intensities and (a) bone memberships and (b) MRCT intensities for ROIs within bone when using four (T1-weighted, T2-weighted, fat, water) and five (T1-weighted, T2-weighted, fat, water, UTE1) image volumes as input.
Figure 10
Figure 10
Axial (a) and sagittal images (c) through a MRCT image volume and corresponding cuts through the same patient’s CT volume ((b) and (d)). Left lateral DRR images generated from MRCT (e) and CT (f).
Figure 11
Figure 11
(a) An example cut of relative dose distributions on CT (left) and MRCT(right) with heterogeneity corrections. (b) DVHs (relative volume vs. relative dose) for both CT and MRCT with heterogeneity corrections and CT without heterogeneity corrections.
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