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European Journal of Nuclear Medicine and Molecular Imaging
Erschienen in:

26.03.2020 | Original Article

Deep learning-based attenuation map generation for myocardial perfusion SPECT

verfasst von: Luyao Shi, John A. Onofrey, Hui Liu, Yi-Hwa Liu, Chi Liu

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 10/2020

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Abstract

Purpose

Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods.

Methods

Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy.

Results

The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool.

Conclusion

We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.
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Metadaten
Titel
Deep learning-based attenuation map generation for myocardial perfusion SPECT
verfasst von
Luyao Shi
John A. Onofrey
Hui Liu
Yi-Hwa Liu
Chi Liu
Publikationsdatum
26.03.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 10/2020
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-020-04746-6

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