Clinical/methodological problem: In the reconstruction of three-dimensional image data, artifacts that interfere with the appraisal often occur as a result of trying to minimize the dose or due to missing data. Used iterative reconstruction methods are time-consuming and have disadvantages.
Standard radiological methods: These problems are known to occur in computed tomography (CT), cone beam CT, interventional imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging (PET and SPECT).
Methodological innovations: Using techniques based on the use of artificial intelligence (AI) in data analysis and data supplementation, a number of problems can be solved up to a certain extent.
Performance: The performance of the methods varies greatly. Since the generated image data usually look very good using the AI-based methods presented here while their results depend strongly on the study design, reliable comparable quantitative statements on the performance are not yet available in broad terms.
Evaluation: In principle, the methods of image reconstruction based on AI algorithms offer many possibilities for improving and optimizing three-dimensional image datasets. However, the validity strongly depends on the design of the respective study in the structure of the individual procedure. It is therefore essential to have a suitable test prior to use in clinical practice.
Practical recommendations: Before the widespread use of AI-based reconstruction methods can be recommended, it is necessary to establish meaningful test procedures that can characterize the actual performance and applicability in terms of information content and a meaningful study design during the learning phase of the algorithms.
Keywords: Computed tomography; Deep Learning; Dose reduction; Limitations; Machine Learning.