To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRI _prior . Principal component analysis (PCA) is used to extract three major respiratory deformation modes from the DFMs generated between the prior and remaining phases. VC-MRI at each time-step is considered a deformation of MRI _prior , where the DFM is represented as a weighted linear combination of the PCA components. The PCA weightings are solved by minimizing the differences between on-board 2D cine MRI and its corresponding VC-MRI slice. The PCA weightings solved during the initial training period are used to train an ADMLP-NN to predict PCA weightings ahead of time during the prediction period. The predicted PCA weightings are used to build predicted 3D DFM and ultimately, predicted VC-MRIs for 4D target tracking. The method was evaluated using a 4D computerized phantom (XCAT) with patient breathing curves and MRI data from a real liver cancer patient. Effects of breathing amplitude change and ADMLP-NN parameter variations were assessed. The accuracy of the PCA curve prediction was evaluated. The predicted real-time 3D tumor was evaluated against the ground-truth using volume dice coefficient (VDC), center-of-mass-shift (COMS), and target tracking errors. For the XCAT study, the average VDC and COMS for the predicted tumor were 0.92 ± 0.02 and 1.06 ± 0.40 mm, respectively, across all predicted time-steps. The correlation coefficients between predicted and actual PCA curves generated through VC-MRI estimation for the 1st/2nd principal components were 0.98/0.89 and 0.99/0.57 in the SI and AP directions, respectively. The optimal number of input neurons, hidden neurons, and MLP-NN for ADMLP-NN PCA weighting coefficient prediction were determined to be 7, 4, and 10, respectively. The optimal cost function threshold was determined to be 0.05. PCA weighting coefficient and VC-MRI accuracy was reduced for increased prediction-step size. Accurate PCA weighting coefficient prediction correlated with accurate VC-MRI prediction. For the patient study, the predicted 4D tumor tracking errors in superior–inferior, anterior–posterior and lateral directions were 0.50 ± 0.47 mm, 0.40 ± 0.55 mm, and 0.28 ± 0.12 mm, respectively. Preliminary studies demonstrated the feasibility to use VC-MRI and artificial neural networks to predict real-time 3D DFMs of the tumor for 4D target tracking.

Introduction. Real-time 2D-kV-triggered images used to evaluate intra-fraction motion during abdominal radiotherapy only provides 2D information with poor soft-tissue contrast. The main goal of this research is to evaluate a novel method that generates synthetic 3D-MRI from single 2D-kV images for online motion monitoring in abdominal radiotherapy. Methods. Deformable image registration (DIR) is performed between one 4D-MRI reference phase and all other phases, and principal-component-analysis (PCA) is implemented on their respective deformation vectors. By sampling 1,000 times the PCA eigenvalues and applying the new deformations over a reference CT, 1,000 digital reconstructed radiographs (DRRs) were generated to train a convolutional neural network (CNN) to predict their respective eigenvalues. The method was implemented and tested using a digital phantom (XCAT) and an MRI-compatible phantom (ZEUS) with five DRR angles (0°, 45°, 90°, 135°, 180°). Seven motion scenarios were tested. For model performance, mean absolute error (MAE) and root mean square error (RMSE) were reported. Image quality was evaluated with structure similarity index (SSIM) and normalized RMSE (nRMSE), and target-volume variations were evaluated with volumetric dice coefficient (VDC) and Hausdorff-distance (HD). Results. The model performance across the evaluated angles were MAE_{(XCAT, ZEUS)}=(0.053±0.003, 0.094±0.003), and RMSE_{(XCAT, ZEUS)}=(0.054±0.007, 0.103±0.002). Similarly, SSIM_{(XCAT, ZEUS)}=(0.994±0.001, 0.96±0.02), and nRMSE_{(XCAT, ZEUS)}=(0.13±0.01, 0.17±0.03). For all motion scenarios for XCAT and ZEUS, SSIM were 0.98±0.01 and 0.84±0.02, nRMSE were 0.14±0.01 and 0.27±0.02, VDC were 0.98±0.01 and 0.90±0.01, and HD were 0.24±0.02 mm and 2.3±0.8 mm, respectively, averaged across all angles. Finally, SSIM, nRMSE, VDC and HU values for ZEUS using the deformed images as ground truth, presented an improvement of 13%, 28%, 4%, and 76%, respectively. Conclusion. Results from a digital and physical phantom demonstrate a novel approach to generate real-time 3D synthetic MRI from onboard kV images on a conventional LINAC for intra-fraction monitoring in abdominal radiotherapy.

Purpose: A comprehensive 4DCT QA program that includes the assessment of spatial integrity and image quality of 4D phantom scans can be resource‐intensive, especially because the analysis burden scales with the number of motion traces used for QA. This work presents a streamlined and scalable workflow, enabled by the use of a widely available phantom and an automated analysis tool.Methods: For 4DCT imaging QA, the Catphan was placed on the QUASAR motion platform, driven with sinusoidal motion traces of various amplitudes and frequencies. The acquired image sets were automatically analyzed using a newly designed 4DCT analysis module in TotalQA. The metrics of interest for 4DCT QA were analyzed for image sets of all motion bins and compared to the stationary reference image set. Three broad categories of imaging tests for 4DCT QA were defined: spatial integrity, HU constancy, and image quality. The sensitometry plugs in the Catphan were used as motion surrogates for evaluating spatial integrity, including the dimensions of the plugs and the observed motion amplitude in the scans. HU values of the sensitometry plugs in different phases were evaluated for consistency. For image quality, spatial resolution, low contrast resolution, and image noise were evaluated.Results: Compared to the stationary reference CT, spatial integrity was within ±1 mm and the HU values were within two HU in the 4DCT for all phases. The spatial resolution was consistent while slightly higher noise was observed in the 4DCT images. The automated analysis in TotalQA was completed in approximately 20 min per motion trace, improving efficiency by more than 80% compared to the manual workflow.Conclusions: A streamlined 4DCT imaging QA workflow with automated analysis offers a robust assessment of imaging quality and motion accuracy in 4DCT scans, enabling an efficient and consistent QA process in large healthcare networks.