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Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Accurate energy calibration is critical for the application of energy-resolved photon-counting detectors in spectral imaging. The aim of this study is to investigate the feasibility of energy response calibration and characterization of a photon-counting detector using x-ray fluorescence. A comprehensive Monte Carlo simulation study was performed using Geant4 Application for Tomographic Emission (GATE) to investigate the optimal technique for x-ray fluorescence calibration. Simulations were conducted using a 100 kVp tungsten-anode spectra with 2.7 mm Al filter for a single pixel cadmium telluride (CdTe) detector with 3 × 3 mm(2) in detection area. The angular dependence of x-ray fluorescence and scatter background was investigated by varying the detection angle from 20° to 170° with respect to the beam direction. The effects of the detector material, shape, and size on the recorded x-ray fluorescence were investigated. The fluorescent material size effect was considered with and without the container for the fluorescent material. In order to provide validation for the simulation result, the angular dependence of x-ray fluorescence from five fluorescent materials was experimentally measured using a spectrometer. Finally, eleven of the fluorescent materials were used for energy calibration of a CZT-based photon-counting detector. The optimal detection angle was determined to be approximately at 120° with respect to the beam direction, which showed the highest fluorescence to scatter ratio (FSR) with a weak dependence on the fluorescent material size. The feasibility of x-ray fluorescence for energy calibration of photon-counting detectors in the diagnostic x-ray energy range was verified by successfully calibrating the energy response of a CZT-based photon-counting detector. The results of this study can be used as a guideline to implement the x-ray fluorescence calibration method for photon-counting detectors in a typical imaging laboratory.