## 5 Role of MP  
### Imaging  
As already underlined in this paper, one of the major tasks in which the MP is deeply involved in the imaging field is the optimization process, i.e., finding the balance between dose and image quality. MP understands the components of an imaging device used and the basic physical mechanisms at the root of signal change and image contrast and comprehends the technical and/or physiological artifacts limiting the performance [4; 118]. Moreover, the MP understands the limitations and potential pitfalls of dose measurement, calculation, and prediction [90]. Thus, MP has knowledge and skills that are of value for the development, implementation, and use of AI in imaging. AI-based systems have been developed to estimate patient dose. MP shall validate and periodically check these systems to avoid possible errors in the estimation. For example, the dose to each voxel in the calculated distribution depends on the dose calculation algorithm used, on the calculation voxel spacing, and on the uncertainty in dose measurement in the dataset used for ML training. In phantom, dose measurements can be planned by the MP to test algorithms' predictions. MP shall also assess image quality through routine testing [119]. Recently, image quality enhancers, based on DL, have been introduced in clinical practice in order to ameliorate image quality. Consequently, image acquisition protocols could be updated to achieve dose reduction, and the MP will be involved in the optimization to ensure the minimum possible ionizing radiation dose to the patient [119; 120]. It is also necessary to verify to what extent the imaging parameters' change influences the quantitative image content and, consequently, the response of AI systems. To this purpose, various physical phantoms have been developed. The Credence Cartridge Radiomics (CCR) phantom for radiomics was created for CT [121] and CBCT [122] images. More recently, anthropomorphic phantoms with heterogeneous objects were designed in order to simulate the texture of lung nodules [123]. PET phantoms with 3D printed inserts simulating heterogeneities in FDG uptake have been proposed [124], as well as MR phantoms simulating relaxation times and texture of pelvic tissue and malignancies [125]. Using these kinds of phantoms, the sensitivity of radiomics-based ML classifications on image acquisition parameters has been investigated. In CT, the classification is affected by the device used [121], method of image reconstruction [126], noise reduction algorithms, slice thicknesses [127; 128]. PET features depend on acquisition mode [129; 130], reconstruction algorithm, image resolution, and discretization [131; 132]. MRI features are sensitive to the field of view, field strength, pulse sequence, reconstruction algorithm, and slice thickness [133]. Physical and digital phantoms could also be used to periodically verify the performances of image-based ML algorithms. Digital phantoms are usually representative scans of patients with known acquisition parameters. A dataset of CTs acquired twice on the same patient 15 min apart allows \"test-retest\", an assessment of the reproducibility of the radiomics workflow under the same conditions [127]. The accuracy of AI-generated segmentation, image reconstruction, and synthetic images (e.g., MRI) can be assessed using a ground truth digital phantom, for example of brain glioma patients [133] and image simulators, capable of simulating MRI acquired with different pulse sequence or field strength and reconstructed with different methods [133]. Specific tests allow assessing the accuracy of AI-based image registration [134]. In addition, MP can ensure correct extraction and quantitative analysis of imaging data. Thus, before performing quantitative analysis with AI algorithms, the accuracy and precision associated with the quantitative parameters within the images (e.g., tumors) should be assessed [29]. Moreover, MP is responsible for the pre-processing of images necessary for correct AI application.