Purpose Currently, four‐dimensional (4D) cone‐beam computed tomography (CBCT) requires a 3–4 min full‐fan scan to ensure usable image quality. Recent advancements in sparse‐view 4D‐CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Using this framework, the AAPM‐sponsored SPARE Challenge was conducted in 2018 to identify and compare state‐of‐the‐art algorithms. Methods A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D‐Lung database. The selected patients had multiple 4D‐CT sessions, where the first 4D‐CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU‐based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root‐mean‐squared‐error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region‐of‐interests (ROI) — patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. Results Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion‐compensation, and deformation of the prior 4D‐CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp–Davis–Kress (FDK) algorithm. The RMS of the three‐dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA‐ROOSTER method, which utilizes a 4D‐CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. Conclusion The SPARE Challenge represents the first framework for systematically evaluating state‐of‐the‐art algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Results suggest the potential for reducing scan time and dose for 4D‐CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Data-driven groupwise registration and motion-compensated reconstruction offer a feasible means of improving the quality of 4D-CBCT images acquired under clinical conditions. The addition of motion compensation reconstruction after groupwise registration visibly reduced the impact of view aliasing artifacts for the clinical image datasets studied.

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PurposeTo develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes.MethodsWe propose a deep learning method to flag registration errors. The method involves preparation of a dataset for machine learning model training and testing, design of a three‐dimensional (3D) convolutional neural network architecture that classifies registrations into good or poor classes, and evaluation of a metric called registration error index (REI) which provides a quantitative measure of registration error.ResultsOur study shows that, despite having limited number of training images available (10 CT scan pairs for training and 17 CT scan pairs for testing), the method achieves 0.882 AUC‐ROC on the test dataset. Furthermore, the combined standard uncertainty of the estimated REI by our model lies within ± 0.11 (± 11% of true REI value), with a confidence level of approximately 68%.ConclusionsWe have developed and evaluated our method using original clinical registrations without generating any synthetic/simulated data. Moreover, test data were acquired from a different environment than that of training data, so that the method was validated robustly. The results of this study showed that our algorithm performs reasonably well in challenging scenarios.

Purpose: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process.Methods: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period.Results: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 AE 0.053 (P = 0.159) and is 0.011 AE 0.224 mm (P = 0.355), respectively.Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. Conclusion:The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.