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Background: Current commercially available hybrid magnetic resonance linear accelerators (MR‐Linac) use 2D+t cine MR imaging to provide intra‐fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra‐frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. Purpose: The aim of this work is to investigate intra‐frame motion deterioration effects in MR‐guided radiotherapy (MRgRT) by simulating the motion‐corrupted image acquisition, and to explore the feasibility of deep‐learning‐based compensation approaches, relying on the intra‐frame motion information which is spatially and temporally encoded in the raw data (k‐space). Methods: An intra‐frame motion model was defined to simulate motion‐corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra‐frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra‐frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U‐Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss‐L1, NNFloss‐L1 and NNL2‐Loss were trained to extract information from the motion‐corrupted image and used to estimate the ground truth final‐position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean‐squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical‐flow image registration. Results: Image degradation caused by intra‐frame motion was observed: for a linearly and fully acquired Cartesian readout k‐space trajectory, intra‐frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion‐corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra‐frame motion amplitude categories: NNImgLoss‐L1 excelled for small/medium amplitudes, whereas NNFloss‐L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion‐corrupted image highlighted the major contribution of the later acquired k‐space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. Conclusions: Our results demonstrate the deep‐learning‐based approaches have the potential to compensate for intra‐frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k‐space components. [ABSTRACT FROM AUTHOR]

A method of Covariance-Oriented Sample Transformation (COST) has been proposed in our previous work to provide the converged uncertainty analysis results with a minimal sample size. The transient calculation of nuclear reactor is a key part of the reactor-physics simulation, so the accuracy and confidence of the neutron kinetics results have attracted much attention. In this paper, the Uncertainty Quantification (UQ) function of the high fidelity neutronics code NECP-X has been developed based on our home-developed uncertainty analysis code UNICORN, building a platform for the UQ of the transient calculation. Furthermore, the well-known space-time heterogeneous neutron kinetics benchmark C5G7 and its uncertainty propagation from the nuclear data to the interested key parameters of the core have been investigated. To address the problem of “the curse of dimensionality” caused by the large number of input parameters, the COST method has been applied to generate multivariate normal-distribution samples in uncertainty analysis. As a result, the law of the assembly/pin normalized power and their uncertainty with respect to time after introducing an instantaneous perturbation has been obtained. From the numerical results, it can be observed that the maximum relative uncertainties for the assembly normalized power can up to be about 1.65% and the value for the pin-wise power distributions can be about 2.71%.