Dataset Information

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy.

ABSTRACT:

Purpose

In 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.

Methods

From 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.

Results

The 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.

Conclusion

We 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.

SUBMITTER: Liang J

PROVIDER: S-EPMC8992944 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy.

Liang Jian J Liu Qiang Q Grills Inga I Guerrero Thomas T Stevens Craig C Yan Di D

Journal of applied clinical medical physics 20220202 4

<h4>Purpose</h4>In 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.<h4>Methods</h4>From a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and t ...[more]

PMID: 35112781

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Project description:BackgroundCurrent medical image registration methods based on Transformer still encounter challenges, including significant local intensity differences and limited computational efficiency when dealing with three-dimensional (3D) computed tomography (CT) and cone beam CT (CBCT) images. These limitations hinder the precise alignment necessary for effective diagnosis and treatment planning. Therefore, the aim of this study is to develop a novel method that overcomes these challenges by enhancing feature interaction and computational efficiency in 3D medical image registration.MethodsThis paper introduces a novel method that enhances feature interaction within Transformer by computing attention within resizable spatial perpendicular window (SPW). Additionally, it introduces a self-learning mapping control (SLMC) mechanism, which uses a mini convolutional neural network (CNN) to adaptively transform feature vectors into probability vectors. This approach is integrated into the UNet framework, resulting in the SPW-TransUNet. The effectiveness of the SPW-TransUNet is demonstrated through evaluations on two critical 3D medical imaging tasks: CT-CBCT registration and inter-CT registration. We utilized a range of evaluation metrics including Dice similarity coefficient (DICE), structural similarity index measure (SSIM), target registration error (TRE), and negative Jacobian percentage. The validation process involved comparative analysis against established baseline methods using statistical tests to ensure the robustness and reliability of our results.ResultsThe proposed method demonstrated outstanding performance in the registration of 124 pairs of CT-CBCT lung images from 20 patients, achieving the lowest TRE of 2.16 mm and a minimal negative Jacobian of 0.126. It also recorded the highest SSIM and Dice coefficient of 86.87% and 88.28%, respectively. For the liver CT task involving 150 patients, the method achieved peak SSIM and DICE scores of 76.92% and 85.77%, respectively. Furthermore, ablation studies confirmed the effectiveness of the designed structural components.ConclusionsThe SPW-TransUNet offers significant improvements in feature interaction and computational efficiency for medical image registration, providing an effective reference solution for patient and target localization in image-guided radiation therapy.

Dataset Information

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy.

Purpose

Methods

Results

Conclusion

Publications

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy.

Similar Datasets

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