onr:"swepub:oai:lup.lub.lu.se:df187037-f5cf-4a7d-b518-1be854354d16"
Sökning: onr:"swepub:oai:lup.lub.lu.se:df187037-f5cf-4a7d-b518-1be854354d16" > Analysis of a deep ...
- 1 av 1
- Föregående post
- Nästa post
- Till träfflistan
Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset
-
- Leube, Julian (författare)
- Julius Maximilian University of Würzburg
-
- Gustafsson, Johan (författare)
- Lund University,Lunds universitet,Medicinsk strålningsfysik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Nuclear Medicine Physics,Forskargrupper vid Lunds universitet,Medical Radiation Physics, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund University Research Groups
-
- Lassmann, Michael (författare)
- Julius Maximilian University of Würzburg
- visa fler...
- visa färre...
- (creator_code:org_t)
- 2022-07-19
- 2022
- Engelska.
- Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 9:1
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- 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 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Lu
- Deep learning
- Denoising
- Monte Carlo
- SPECT
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
- 1 av 1
- Föregående post
- Nästa post
- Till träfflistan
Hitta mer i SwePub
- Av författaren/redakt...
- Leube, Julian
- Gustafsson, Joha ...
- Lassmann, Michae ...
- Salas-Ramirez, M ...
- Tran-Gia, Johann ...
- Om ämnet
-
- MEDICIN OCH HÄLSOVETENSKAP
- MEDICIN OCH HÄLS ...
- och Klinisk medicin
- och Radiologi och bi ...
- Artiklar i publikationen
- EJNMMI Physics
- Av lärosätet
- Lunds universitet
Sök utanför SwePub
- Sök vidare i:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - Nationella bibliotekssystem
- LIBRIS.kb.se
