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Accuracy of image guidance using free-breathing cone-beam computed tomography for stereotactic lung radiotherapy.

ABSTRACT: Movement of the target object during cone-beam computed tomography (CBCT) leads to motion blurring artifacts. The accuracy of manual image matching in image-guided radiotherapy depends on the image quality. We aimed to assess the accuracy of target position localization using free-breathing CBCT during stereotactic lung radiotherapy. The Vero4DRT linear accelerator device was used for the examinations. Reference point discrepancies between the MV X-ray beam and the CBCT system were calculated using a phantom device with a centrally mounted steel ball. The precision of manual image matching between the CBCT and the averaged intensity (AI) images restructured from four-dimensional CT (4DCT) was estimated with a respiratory motion phantom, as determined in evaluations by five independent operators. Reference point discrepancies between the MV X-ray beam and the CBCT image-guidance systems, categorized as left-right (LR), anterior-posterior (AP), and superior-inferior (SI), were 0.33 ± 0.09, 0.16 ± 0.07, and 0.05 ± 0.04 mm, respectively. The LR, AP, and SI values for residual errors from manual image matching were -0.03 ± 0.22, 0.07 ± 0.25, and -0.79 ± 0.68 mm, respectively. The accuracy of target position localization using the Vero4DRT system in our center was 1.07 ± 1.23 mm (2 SD). This study experimentally demonstrated the sufficient level of geometric accuracy using the free-breathing CBCT and the image-guidance system mounted on the Vero4DRT. However, the inter-observer variation and systematic localization error of image matching substantially affected the overall geometric accuracy. Therefore, when using the free-breathing CBCT images, careful consideration of image matching is especially important.

SUBMITTER: Kamomae T

PROVIDER: S-EPMC4425686 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Accuracy of image guidance using free-breathing cone-beam computed tomography for stereotactic lung radiotherapy.

Kamomae Takeshi T Monzen Hajime H Nakayama Shinichi S Mizote Rika R Oonishi Yuuichi Y Kaneshige Soichiro S Sakamoto Takashi T

PloS one 20150508 5

Movement of the target object during cone-beam computed tomography (CBCT) leads to motion blurring artifacts. The accuracy of manual image matching in image-guided radiotherapy depends on the image quality. We aimed to assess the accuracy of target position localization using free-breathing CBCT during stereotactic lung radiotherapy. The Vero4DRT linear accelerator device was used for the examinations. Reference point discrepancies between the MV X-ray beam and the CBCT system were calculated us ...[more]

PMID: 25954809

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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.

Project description:PurposeIn our conventional image registration workflow, the four-dimensional (4D) CBCT was directly registered to the reference helical CT (HCT) using a dual registration approach within the Elekta XVI software. In this study, we proposed a new HCT-CBCT auto-registration strategy using a previously registered CBCT (CBCTpre) as the reference image and tested its clinical feasibility.MethodsFrom a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and the HCT-CBCTpre registration vector from the clinician's manual registration result was recorded. In the new CBCT session, auto-registration was performed between the new average 4D CBCT (CBCTtx) and CBCTpre (CBCTpre-CBCTtx). The overall HCT-CBCTtx registration result was then derived by combing the results from two registrations (i.e., HCT-CBCTpre + CBCTpre-CBCTtx). The results from the proposed method were compared with clinician's manually adjusted HCT-CBCTtx registration results ("ground truth") to evaluate its accuracy using a test dataset consisting of 32 challenging registration cases.ResultsThe uncertainty of the proposed auto-registration method was -0.1 ± 0.5, 0.1 ± 1.0, and -0.1 ± 0.7 mm in three translational directions (lateral, longitudinal, and vertical) and 0.0° ± 0.9°, 0.3° ± 0.9°, and 0.4° ± 0.7° in three rotation directions, respectively. Two patients (6.3%) had translational uncertainty > 2 mm (max = 3.1 mm) and both occurred in the longitudinal direction. Meanwhile, the uncertainty of the conventional direct HCT-CBCTtx auto-registration was -0.4 ± 2.6, -0.2 ± 7.4, -1.4 ± 3.6 mm for translations and -0.3° ± 1.2°, 0.0° ± 1.6°, and 0.1 ± 1.1° for rotations. Eleven patients (34.4%) had translation uncertainty > 2 mm (max = 26.2 mm) in at least one direction. Accuracy in translation was improved with the new method, while rotation accuracy stayed in the same order.ConclusionWe demonstrated the feasibility of incorporating prior clinical registration knowledge into the online HCT-CBCT registration process. The proposed auto-registration method provides a quick and reliable starting solution for online HCT-CBCT registration.

Dataset Information

Accuracy of image guidance using free-breathing cone-beam computed tomography for stereotactic lung radiotherapy.

Publications

Accuracy of image guidance using free-breathing cone-beam computed tomography for stereotactic lung radiotherapy.

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