Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ SPECT collimator

  • \(\bf Background\) The use of CT images for attenuation correction of myocardial perfusion imaging with single photon emission computer tomography (SPECT) increases diagnostic confidence. However, acquiring a CT image registered to a SPECT image is often not possible because most scanners are SPECT-only. It is possible to approximate attenuation maps using deep learning, but this has not yet been shown to work for a SPECT scanner with an IQ SPECT collimator. This study investigates whether it is possible to approximate attenuation maps from non-attenuation-corrected (nAC) reconstructions acquired with a SPECT scanner equipped with an IQ SPECT collimator. \(\bf Methods\) Attenuation maps and reconstructions were acquired retrospectively for 150 studies. A U-Net was trained to predict attenuation maps from nAC reconstructions using the conditional generative adversarial network framework. Predicted attenuation maps are compared to real attenuation maps using the normalized mean absolute error (NMAE). Attenuation-corrected reconstructions were computed, and the resulting polar maps were compared by pixel and by average perfusion per segment using the absolute percent error (APE). The training and evaluation code is available at \(\bf Results\) Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020\(\pm\)0.007. The same is true for polar maps generated from reconstructions with predicted attenuation maps compared to CT-based attenuation maps. Their pixel-wise absolute distance is 3.095\(\pm\)3.199, and the segment-wise APE is 1.155\(\pm\)0.769. \(\bf Conclusions\) It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ SPECT collimator using deep learning.

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Author:Tamino HuxohlGND, Gopesh PatelGND, Reinhard ZabelGND, Wolfgang BurchertORCiDGND
Parent Title (English):EJNMMI Physics
Publisher:Springer Nature
Place of publication:Berlin
Document Type:Article
Date of Publication (online):2024/03/14
Date of first Publication:2023/08/28
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:Open Access Fonds
Attenuation correction; Myocardial perfusion imaging; SPECT
GND-Keyword:Deep learning
Issue:Artikel 49
First Page:49-1
Last Page:49-11
Article Processing Charge funded by the Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publication Fund of Ruhr-Universität Bochum.
Institutes/Facilities:Herz- und Diabeteszentrum NRW, Institut für Radiologie, Nuklearmedizin und molekulare Bildgebung
Dewey Decimal Classification:Technik, Medizin, angewandte Wissenschaften / Medizin, Gesundheit
open_access (DINI-Set):open_access
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International