Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

Shiri Lord, Isaac; AmirMozafari Sabet, Kiarash; Arabi, Hossein; Pourkeshavarz, Mozhgan; Teimourian, Behnoosh; Ay, Mohammad Reza; Zaidi, Habib

doi:10.1007/s12350-020-02119-y

Scientific article

English

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

ContributorsShiri Lord, Isaac

; AmirMozafari Sabet, Kiarash; Arabi, Hossein; Pourkeshavarz, Mozhgan; Teimourian, Behnoosh; Ay, Mohammad Reza; Zaidi, Habib

Published inJournal of Nuclear Cardiology, vol. 28, no. 6, p. 2761-2779

Publication date2021

Abstract

The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction of the number of angular projections.

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Affiliation

Research group

Imagerie Médicale (TEP et TEMP) (542)

Funding

Swiss National Science Foundation - Towards patient-specific hybrid whole-body PET parametric imaging [320030_176052]

Citation (ISO format)

SHIRI LORD, Isaac et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. In: Journal of Nuclear Cardiology, 2021, vol. 28, n° 6, p. 2761–2779. doi: 10.1007/s12350-020-02119-y

Article (Published version)

Identifiers

PID : unige:136987
DOI : 10.1007/s12350-020-02119-y
PMID : 32347527

Commercial URLhttp://link.springer.com/10.1007/s12350-020-02119-y

ISSN of the journal1071-3581

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Creation04/29/2020 6:36:00 PM

First validation04/29/2020 6:36:00 PM

Update time03/15/2023 10:05:01 PM

Status update03/15/2023 10:05:00 PM

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Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction of the number of angular projections.

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