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Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment.

ABSTRACT: In this work, we propose a new method of calibrating cone beam computed tomography (CBCT) data sets for radiotherapy dose calculation and plan assessment. The motivation for this patient-specific calibration (PSC) method is to develop an efficient, robust, and accurate CBCT calibration process that is less susceptible to deformable image registration (DIR) errors.Instead of mapping the CT numbers voxel-by-voxel with traditional DIR calibration methods, the PSC methods generates correlation plots between deformably registered planning CT and CBCT voxel values, for each image slice. A linear calibration curve specific to each slice is then obtained by least-squares fitting, and applied to the CBCT slice's voxel values. This allows each CBCT slice to be corrected using DIR without altering the patient geometry through regional DIR errors. A retrospective study was performed on 15 head-and-neck cancer patients, each having routine CBCTs and a middle-of-treatment re-planning CT (reCT). The original treatment plan was re-calculated on the patient's reCT image set (serving as the gold standard) as well as the image sets produced by voxel-to-voxel DIR, density-overriding, and the new PSC calibration methods. Dose accuracy of each calibration method was compared to the reference reCT data set using common dose-volume metrics and 3D gamma analysis. A phantom study was also performed to assess the accuracy of the DIR and PSC CBCT calibration methods compared with planning CT.Compared with the gold standard using reCT, the average dose metric differences were ? 1.1% for all three methods (PSC: -0.3%; DIR: -0.7%; density-override: -1.1%). The average gamma pass rates with thresholds 3%, 3 mm were also similar among the three techniques (PSC: 95.0%; DIR: 96.1%; density-override: 94.4%).An automated patient-specific calibration method was developed which yielded strong dosimetric agreement with the results obtained using a re-planning CT for head-and-neck patients.

SUBMITTER: MacFarlane M

PROVIDER: S-EPMC5849848 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment.

MacFarlane Michael M Wong Daniel D Hoover Douglas A DA Wong Eugene E Johnson Carol C Battista Jerry J JJ Chen Jeff Z JZ

Journal of applied clinical medical physics 20180226 2

<h4>Purpose</h4>In this work, we propose a new method of calibrating cone beam computed tomography (CBCT) data sets for radiotherapy dose calculation and plan assessment. The motivation for this patient-specific calibration (PSC) method is to develop an efficient, robust, and accurate CBCT calibration process that is less susceptible to deformable image registration (DIR) errors.<h4>Methods</h4>Instead of mapping the CT numbers voxel-by-voxel with traditional DIR calibration methods, the PSC met ...[more]

PMID: 29479821

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Project description:BackgroundThe imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed.MethodsIncluded in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions.ResultsThe phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains.ConclusionsThe proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

Dataset Information

Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment.

Publications

Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment.

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