A feasibility study of intrafractional tumor motion estimation based on 4D-CBCT using diaphragm as surrogate

Regional ventilation dynamics of electrical impedance tomography validated with four-dimensional computed tomography: single-center, prospective, observational study

BACKGROUND

The dynamic regional accuracy of electrical impedance tomography has not yet been validated. We aimed to compare the regional accuracy of electrical impedance tomography with that of four-dimensional computed tomography during dynamic ventilation.

METHODS

This single-center, prospective, observational study conducted in a general intensive care unit included adult patients receiving mechanical ventilation from July 2021 to February 2024. The patients were mechanically ventilated passively and underwent electrical impedance tomography and four-dimensional computed tomography on the same day.

RESULTS

Overall, 45 patients were analyzed. The correlation coefficients in regional dynamic ventilation between four-dimensional computed tomography and electrical impedance tomography in each region were 0.963, 0.963, 0.835 (ventral, central, and dorsal, respectively) in the right lung and 0.947, 0.927, 0.823 (ventral, central, and dorsal, respectively) in the left lung. The correlation coefficient was low when the regional ventilation distribution detected by the electrical impedance tomography was < 2%. After excluding nine patients with a regional ventilation distribution of < 2%, the ventral, central, and dorsal correlation coefficients were 0.963, 0.963, and 0.946 in the right lung and 0.942, 0.924, and 0.951, respectively, in the left lung.

CONCLUSIONS

Regional ventilation using electrical impedance tomography during dynamic ventilation was highly accurate and consistent with the time phase compared to four-dimensional computed tomography. Given the high correlation between these modalities, they can contribute significantly to further studies on regional ventilation dynamics. Trial registration number ClinicalTrials.gov (No. UMIN00044386).

A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications

Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D-CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x-ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low-dose, and high-quality 4D-CBCT. This paper aims to review the technical developments surrounding 4D-CBCT comprehensively. It will explore conventional algorithms and recent deep learning-based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D-CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D-CBCT's capabilities and potential to enhance radiation therapy.

Utilization of Diaphragm Motion to Predict the Displacement of Liver Tumors for Patients Treated with Carbon ion Radiotherapy

Objectives: To establish and validate a linear model utilizing diaphragm motion (DM) to predict the displacement of liver tumors (DLTs) for patients who underwent carbon ion radiotherapy (CIRT). A total of 60 pairs of planning and reviewing four-dimensional computed tomography (4DCT) sets over 23 patients were used. Method: We constructed an averaged computed tomography (CT) set for each either planning or reviewing 4DCT within respiratory phases between 20% of exhale and inhale. A rigid image registration to align bony structures was performed between planning and reviewing 4DCT. The position changes on top of diaphragm in superior-inferior (SI) direction between 2 CTs to present DM were obtained. The translational vectors in SI from matching to present DLT were obtained. The linear model was built by training data for 23 imaging pairs. A distance model utilized the cumulative probability distribution (CPD) of DM or DLT and was compared with the linear model. We conducted the statistical regression analysis with receiver operating characteristic (ROC) testing data of 37 imaging pairs to validate the performance of our linear model. Results: The DM within 0.5 mm was true positive (TP) with an area under the ROC curve (AUC) of 0.983 to predict DLT. The error of predicted DLT within half of its mean value indicated the reliability of prediction method. The 23 pairs of data showed (4.5 ± 3.3) mm for trend of DM and (2.2 ± 1.6) mm for DLT. A linear model of DLT = 0.46*DM + 0.12 was established. The predicted DLT was (2.2 ± 1.5) mm with a prediction error of (0.3 ± 0.3) mm. The accumulated probability of observed and predicted DLT with < 5.0 mm magnitude was 93.2% and 94.5%, respectively. Conclusion: We utilized the linear model to set the proper beam gating for predicting DLT within 5.0 mm to treat patients. We will investigate a proper process on x-ray fluoroscopy images to establish a reliable model predicting DLT for DM observed in x-ray fluoroscopy in the following two years.

Improved Tumor Image Estimation in X-Ray Fluoroscopic Images by Augmenting 4DCT Data for Radiotherapy

Measurement of tumor position is important for the radiotherapy of lung tumors with respiratory motion. Although tumors can be observed using X-ray fluoroscopy during radiotherapy, it is often difficult to measure tumor position from X-ray image sequences accurately because of overlapping organs. To measure tumor position accurately, a method for extracting tumor intensities from X-ray image sequences using a hidden Markov model (HMM) has been proposed. However, the performance of tumor intensity extraction depends on limited knowledge regarding the tumor motion observed in the four-dimensional computed tomography (4DCT) data used to construct the HMM. In this study, we attempted to improve the performance of tumor intensity extraction by augmenting 4DCT data. The proposed method was tested using simulated datasets of X-ray image sequences. The experimental results indicated that the HMM using the augmentation method could improve tumor-tracking performance when the range of tumor movement during treatment differed from that in the 4DCT data.

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers' positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior-inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment

BACKGROUND In this study, we assessed the usefulness of diaphragm surrogate tracking in the design of a respiratory model for CyberKnife Synchrony treatment of lung tumors. MATERIAL AND METHODS Twenty-four patients with lung cancer who underwent stereotactic body radiotherapy with CyberKnife between April and November 2019 were enrolled. Simulation plans for each patient were designed using Xsight lung tracking (XLT) and diaphragm tracking (DT) methods, and tumor visualization tests were performed. The offset consistency at each respiratory phase was analyzed. The relative distance along the alignment center of the superior-inferior (SI) axis in the 2 projections (dxAB), uncertainty (%), and average standard error (AvgStdErr)/maximum standard error (MAXStdErr) were also analyzed. RESULTS Bland-Altman analyses revealed that the average differences±standard deviation (SD) between XLT and DT tracking methods were 0.4±2.9 mm, 0.3±4.35 mm, and -1.8±6.8 mm for the SI, left-right (LR), and anterior-posterior (AP) directions, respectively. These results indicated high consistency in the SI and LR directions and poor consistency in the AP direction. Uncertainty differed significantly between XLT and DT (22.813±5.721% vs 9.384±3.799%; t=-5.236; P=0.0008), but we found no significant differences in dxAB, AvgStdErr, or MAXStdErr. CONCLUSIONS In the majority of cases, motion tracking by XLT and DT was consistent and synchronized in the SI directions, but not in the LR and AP directions. With a boundary margin of 0.3±4.35 mm and 1.8±6.8 mm for the LR and AP directions, DT may contribute to better implementation of CyberKnife Synchrony treatment in patients with lung tumors near the diaphragm that cannot be seen in tumor visualization tests.