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The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4 Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field. [ABSTRACT FROM AUTHOR]

Purpose: Establishing the tolerance limits of patient‐specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify‐Eliminate‐Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs. Materials and Methods: A total of 1671 volumetric‐modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson‐Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify‐Eliminate‐Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in‐control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed. Results: It was found that 75% of the initial PSQA processes and 63% of the in‐control processes were non‐normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in‐control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify‐Eliminate‐Recalculate" procedure, the quantile methods could not identify the out‐of‐control PSQAs effectively due to the influence of outliers. The tolerance limits of the in‐control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in‐control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in‐control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively. Conclusions: The GPR distributions of PSQA processes vary with different linacs and treatment sites but most are skewed. In applying SPC methodologies to PSQA processes, heuristic methods are robust. For in‐control PSQA processes, the tolerance limits calculated by heuristic methods are in good agreement with the ground truth. For unknown PSQA processes, the tolerance limits calculated by the heuristic methods after the iterative "Identify‐Eliminate‐Recalculate" procedure are closest to the ground truth. Setting linac‐ and treatment site‐specific tolerance limits for PSQA processes is necessary for clinical applications. [ABSTRACT FROM AUTHOR]

Martian dust plays an important role in modulating climate. However, the effects of dust on polar CO2 ice caps, atmospheric circulation processes and the relationship between them are not yet clear. In this study, a Mars general circulation model (MarsWRF) was applied to investigate the impacts of the magnitude and timing of Martian dust storms on the spatiotemporal characteristics of polar CO2 ice caps and atmospheric circulation. The results show that dust can inhibit the sublimation of southern CO2 ice through the radiation effect. Under the "High Dust" scenario, the ice edge can extend up to 5° more equatorward than that under the "Standard" scenario around Ls = 220°. The extent of northern CO2 ice cap is hardly affected by dust because the impacts induced by dust mainly appear north of 80°N. By shifting the timing of maximum dust loading during the dust season, the "Early Dust" scenario inhibits the sublimation of the southern ice cap, while the "Late Dust" scenario accelerates the sublimation process. In addition, the variations in dust loading strength can lead to changes in three near‐surface wind belts, which are related to intensified Hardly circulation. The appearance of the warming vortex under the "Early Dust" scenario is delayed, and its intensity is 60 K lower than that observed in the "High Dust" and "Late Dust" scenarios. When dust loading increases, the northern CO2 condensation process in the Western Hemisphere accelerates, which is due to the increased pole‐to‐equator temperature gradient and the increased meridional wind speed. Plain Language Summary: Dust has a strong influence on the Martian climate. However, the dust effects on polar CO2 ice caps and wind fields have not yet been fully investigated. In this study, a numerical model that simulates the Martian climate was applied to investigate changes in the spatial and temporal patterns of CO2 ice caps and three‐dimensional wind fields. The results show that dust can inhibit the sublimation of southern CO2 ice by influencing the surface energy balance, while the coverage area of the northern CO2 ice cap is hardly affected by dust because the impacts induced by dust mainly appear north of 80°N. In terms of the timing of Martian dust storms, when a dust storm starts early (late), the sublimation of the southern ice cap is inhibited (accelerated). The appearance of the warming vortex will be delayed and its temperature will be 60 K lower than that in the general scenario if the dust storm starts early. When dust loading increases, the northern surface CO2 ice in the Western Hemisphere also increases, which is due to the increased pole‐to‐equator temperature gradient and increased meridional wind speed. Key Points: High dust loading decelerates the recession of southern CO2 ice cap, while the extent of northern CO2 ice cap is hardly affected by dustWhen Martian dust storm starts early, the appearance of the warming vortex is delayed and its intensity weakensThe increase in northern surface CO2 ice is induced by the increased polar‐equator temperature gradient and the increased meridional flow [ABSTRACT FROM AUTHOR]