Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples\' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

OBJECTIVE: MatriXX ionization chamber array has been widely used for the composite dose verification of IMRT/VMAT plans. However, in addition to its dose response dependence on gantry angle, there seems to be an offset between the beam axis and measured dose profile by MatriXX for oblique beam incidence at various gantry angles, leading to unnecessary quality assurance (QA) fails. In this study, we investigated the offset at various setup conditions and how to eliminate or decrease it to improve the accuracy of MatriXX for IMRT/VMAT plan verification with original gantry angles.
METHODS: We measured profiles for a narrow beam with MatriXX located at various depths in increments of 0.5 mm from the top to bottom of the sensitive volume of the array detectors and gantry angles from 0° to 360°. The optimal depth for QA measurement was determined at the depth where the measured profile had minimum offset.
RESULTS: The measured beam profile offset varies with incident gantry angle, increasing from vertical direction to lateral direction, and could be over 3 cm at vendor-recommended depth for near lateral direction beams. The offset also varies with depth, and the minimum offset (almost 0 for most oblique beams) was found to be at a depth of ∼2.5 mm below the vendor suggested depth, which was chosen as the optimal depth for all QA measurements. Using the optimal depth we determined, QA results (3%/2 mm Gamma analysis) were largely improved with an average of 99.4% gamma passing rate (no fails for 95% criteria) for 10 IMRT and VMAT plans with original gantry angles compared to 94.1% using the vendor recommended depth.
CONCLUSIONS: The improved accuracy and passing rate for QA measurement performed at the optimal depth with original gantry angles would lead to reduction in unnecessary repeated QA or plan changes due to QA system errors.

Functional magnetic resonance imaging (fMRI) has been a popular approach in brain research over the past 20 years. It offers a noninvasive method to probe the brain and uses blood oxygenation level dependent (BOLD) signal changes to access brain function. However, the BOLD signal only represents a small fraction of the total MR signal. System instability and various noise have a strong impact on the BOLD signal. Additionally, fMRI applies fast imaging technique to record brain cognitive process over time, requiring high temporal stability of MR scanners. Furthermore, data acquisition, image quality, processing, and statistical analysis methods also have a great effect on the results of fMRI studies. Quality assurance (QA) programs for fMRI can test the stability of MR scanners, evaluate the quality of fMRI and help to find errors during fMRI scanning, thereby greatly enhancing the success rate of fMRI. In this review, we focus on previous studies which developed QA programs and methods in SCI/SCIE citation peer-reviewed publications over the last 20 years, including topics on existing fMRI QA programs, QA phantoms, image QA metrics, quality evaluation of existing preprocessing pipelines and fMRI statistical analysis methods. The summarized studies were classified into four categories: QA of fMRI systems, QA of fMRI data, quality evaluation of data processing pipelines and statistical methods and QA of task-related fMRI. Summary tables and figures of QA programs and metrics have been developed based on the comprehensive review of the literature.