Temporal resolution of the method was high enough to allow characterization of individual gate cycles and was primary limited by the sampling speed of the data recording device. Significant variation of mean gate ON/OFF lag time was found between different gating systems. For certain gating devices, individual gating cycle lag times can vary significantly.

H ypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, affecting up to one in 500 people (1). HCM is diagnosed by using conventional echocardiographic or cardiac MRI by a maximal left ventricular (LV) wall thickness greater than 15 mm in adults and a z score greater than 2 in children in the absence of other causes for wall thickening (2). Modified criteria are typically used for at-risk relatives (3), and a z score greater than 3 has been suggested for children to better match disease prevalence (4). Sarcomere gene mutations are the most prevalent genetic cause of HCM (5). However, phenotypic expression of overt LV hypertrophy is often delayed until adulthood and penetrance is incomplete. Sarcomere mutation carriers without LV hypertrophy are termed preclinical HCM and are at risk for developing overt

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Purpose There is a strong clinical need to evaluate different multi‐criteria optimization (MCO) algorithms, including inverse optimization sampling algorithms and machine learning‐based predictions. This study aims to develop and compare several interpolated Pareto surface similarity metrics. Materials and methods The first metric is the root‐mean‐square error (RMSE) evaluated between vertices on the interpolated surfaces, augmented by intra‐simplex sampling of the barycentric coordinates of the surfaces’ simplicial complexes. The second metric is the average projected distance (APD), which evaluates the displacements between the vertices and computes their projections along the mean displacement. The third metric is the average nearest‐point distance (ANPD), which numerically integrates point‐to‐simplex distances over the sampled simplices of the interpolated surfaces. These metrics were compared by their convergence rates, the times required to achieve convergence, and their representation of the underlying surface interpolations. For analysis, several interpolated Pareto surface pairs were constructed abstractly, with one pair from a nasopharyngeal treatment planning case using MCO. Results Convergence within 1% is typically achieved at approximately 50 and 80 samples per barycentric dimension for the RMSE and the ANPD, respectively. Calculation requires approximately 1 and 10 ms to achieve convergence for the RMSE and the ANPD in two dimensions, respectively, while the APD always requires < 1 ms. These time costs are much higher in higher dimensions for just the RMSE and ANPD. The APD values more closely approximated the ANPD limits than the RMSE limits. Conclusion The ANPD’s formulation and generality make it likely more meaningful than the RMSE and APD for representing the similarity between the underlying interpolated surfaces rather than the sampling points on the surfaces. However, in situations requiring high‐speed evaluations, the APD may be more desirable due to its speed, independence from a subjectively chosen sampling rate, and similarity to the ANPD limits.

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Treatment planning for prostate volumetric modulated arc therapy (VMAT) can take 5–30 min per plan to optimize and calculate, limiting the number of plan options that can be explored before the final plan decision. Inspired by the speed and accuracy of modern machine learning models, such as residual networks, we hypothesized that it was possible to use a machine learning model to bypass the time-intensive dose optimization and dose calculation steps, arriving directly at an estimate of the resulting dose distribution for use in multi-criteria optimization (MCO). In this study, we present a novel machine learning model for predicting the dose distribution for a given patient with a given set of optimization priorities. Our model innovates upon the existing machine learning techniques by utilizing optimization priorities and our understanding of dose map shapes to initialize the dose distribution before dose refinement via a voxel-wise residual network. Each block of the residual network individually updates the initialized dose map before passing to the next block. Our model also utilizes contiguous and atrous patch sampling to effectively increase the receptive fields of each layer in the residual network, decreasing its number of layers, increasing model prediction and training speed, and discouraging overfitting without compromising on the accuracy. For analysis, 100 prostate VMAT cases were used to train and test the model. The model was evaluated by the training and testing errors produced by 50 iterations of 10-fold cross-validation, with 100 cases randomly shuffled into the subsets at each iteration. The error of the model is modest for this data, with average dose map root-mean-square errors (RMSEs) of 2.38 ± 0.47% of prescription dose overall patients and all optimization priority combinations in the patient testing sets. The model was also evaluated at iteratively smaller training set sizes, suggesting that the model requires between 60 and 90 patients for optimal performance. This model may be used for quickly estimating the Pareto set of feasible dose objectives, which may directly accelerate the treatment planning process and indirectly improve final plan quality by allowing more time for plan refinement.

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