## 5 Discussion
### Research Methods
Small in-house datasets are a common challenge, and data augmentation techniques are often used to mitigate this issue [14]. Those methods use existing data and apply different transformations, such as rotation, scaling, and translations, to create new instances of data. However, data augmentation is limited by available data and can only introduce variations rather than new representations. When data are very limited or ground truth annotations are unavailable, augmentation may not suffice which poses a significant challenge for the exploration, development, and validation of novel and innovative models [77]. Synthetic data generation, using deep learning methods, offers a solution by creating entirely new data instances. [PERSON] et al. [77] presented a comprehensive analysis of SPECT projection generation using large simulated datasets. They evaluated the effects of different parameters (e.g., noise, dataset size, number of input projections and rotation angle, and detector orbit) on model performance. The authors reported strong agreement between simulated and measured projections (Jaszack and NEMA Phantoms), showing that despite models being trained with a completely simulated SPECT dataset, such models can generalize well to synthetic projections derived from physical phantom measurements. In another study, [PERSON] et al. [67] introduced a deep ensemble model based on GAN to predict labels for unannotated data. The authors evaluated their model on three datasets, including the UCI Heart SPECT dataset, and compared results with other existing algorithms. Their method outperformed the other methods highlighting its potential to increase the quantity of annotated data when labeled data are scarce. Similarly, [PERSON] et al. [71] proposed a GAN-based model for synthetic brain SPECT scans. The authors developed a model that creates scans indistinguishable from those of real patients and minimized training data requirements for rare diseases like Creutzfeldt-Jakob disease, albeit with a necessity for at least three anatomical compartments to achieve accurate results. Another key challenge is reducing radiation dose and scan time by reconstructing some slices from existing ones. [PERSON] et al. [74] applied deep learning methods to reconstruct full-acquisition-time images from short-acquisition-time renal pediatric scintigraphy images, demonstrating that acquisition time could be reduced to one-fifth while maintaining image quality and renal uptake measurement accuracy. Similarly, [PERSON] et al. [76] successfully reconstructed full SPECT renal images from half-acquisition-time scans showing the potential to reduce the scan time in classification of kidney renal cortical defect. Reconstruction methods also extend to enhancing 3D SPECT images from a limited number of projection scans. [PERSON] et al. [70] proposed a novel 3D transfer network for high-quality reconstruction from few-view cardiac SPECT scanners. Their approach enhanced cardiac defect contrast, a result validated by nuclear cardiologists and FDA-cleared clinical software. For centers without SPECT/CT scanners, automatic attenuation generation methods have been explored to eliminate the need for additional CT scans [68; 69; 75]. As deep learning architectures continue to improve and evolve, there is a potential for developing a single model capable of achieving multiple outcomes simultaneously. [PERSON] et al. [66] developed a cross-domain iterative network that simultaneously denoises, reconstructs, and corrects attenuation in SPECT images. This approach could improve clinical practices where cost-effectiveness is a critical consideration.