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Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator
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. I...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462587/ https://www.ncbi.nlm.nih.gov/pubmed/37639082 http://dx.doi.org/10.1186/s40658-023-00568-1 |
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author | Huxohl, Tamino Patel, Gopesh Zabel, Reinhard Burchert, Wolfgang |
author_facet | Huxohl, Tamino Patel, Gopesh Zabel, Reinhard Burchert, Wolfgang |
author_sort | Huxohl, Tamino |
collection | PubMed |
description | 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[Formula: see text] 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[Formula: see text] SPECT collimator. 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 https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map. RESULTS: Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020±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±3.199, and the segment-wise APE is 1.155±0.769. CONCLUSIONS: It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ[Formula: see text] SPECT collimator using deep learning. |
format | Online Article Text |
id | pubmed-10462587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-104625872023-08-30 Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator Huxohl, Tamino Patel, Gopesh Zabel, Reinhard Burchert, Wolfgang EJNMMI Phys Original Research 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[Formula: see text] 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[Formula: see text] SPECT collimator. 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 https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map. RESULTS: Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020±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±3.199, and the segment-wise APE is 1.155±0.769. CONCLUSIONS: It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ[Formula: see text] SPECT collimator using deep learning. Springer International Publishing 2023-08-28 /pmc/articles/PMC10462587/ /pubmed/37639082 http://dx.doi.org/10.1186/s40658-023-00568-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Huxohl, Tamino Patel, Gopesh Zabel, Reinhard Burchert, Wolfgang Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title | Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title_full | Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title_fullStr | Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title_full_unstemmed | Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title_short | Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text] SPECT collimator |
title_sort | deep learning approximation of attenuation maps for myocardial perfusion spect with an iq[formula: see text] spect collimator |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462587/ https://www.ncbi.nlm.nih.gov/pubmed/37639082 http://dx.doi.org/10.1186/s40658-023-00568-1 |
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