X-ray computed tomography traps and challenges for deep learning scientist

Computed tomography (CT) imaging is a non-invasive technique for analyzing internal structures slice by slice. Nowadays, CT is being influenced by the rise of neural network (NN) approaches that address limitations of traditional CT methods. NN applications in CT have shown promise in reducing radiation exposure, compensating instabilities of the X-ray source operation mode, and improving image reconstruction quality. Nevertheless, on the path towards development, approbation and testing of NN-based CT methods, pitfalls and challenges still remain. Many publicly available datasets offered to developers to validate their methods are non-physical and do not correlate with real-life measurements. This, in turn, leads to non-scalability and instability of the developed NN methods. The observed gap in validation database emphasizes the need for comprehensive benchmarking against suitable medical datasets. This paper highlights the importance of modeling realistic data and refining quality measurement methodologies to foster the development of human-reliable NN-based CT solutions. Herewith, the authors investigate peculiarities of the ICASSP-2024 3D-CBCT Challenge dataset, one of the well-known CT datasets, and in details indicate its anomalies to take into consideration while modeling CT data and validating CT methods.