Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset
Please always quote using this URN: urn:nbn:de:bvb:20-opus-300697
- Background In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECTBackground In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations. Methods A database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system. Results No statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system. Conclusion Our study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net.…
Author: | Julian Leube, Johan Gustafsson, Michael Lassmann, Maikol Salas-Ramirez, Johannes Tran-Gia |
---|---|
URN: | urn:nbn:de:bvb:20-opus-300697 |
Document Type: | Journal article |
Faculties: | Medizinische Fakultät / Klinik und Poliklinik für Nuklearmedizin |
Language: | English |
Parent Title (English): | EJNMMI Physics |
ISSN: | 2197-7364 |
Year of Completion: | 2022 |
Volume: | 9 |
Article Number: | 47 |
Source: | EJNMMI Physics (2022) 9:47. https://doi.org/10.1186/s40658-022-00476-w |
DOI: | https://doi.org/10.1186/s40658-022-00476-w |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Tag: | 177Lu; Deep learning; Denoising; Monte Carlo; SPECT |
Release Date: | 2023/05/02 |
Open-Access-Publikationsfonds / Förderzeitraum 2022 | |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |