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ORIGINAL ARTICLE
The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2019 December;63(4):394-8
DOI: 10.23736/S1824-4785.18.03002-9
Copyright © 2018 EDIZIONI MINERVA MEDICA
language: English
Data-driven respiratory gating for ventilation/perfusion lung scan
David MORLAND 1, 2, 3 ✉, Sofiane GUENDOUZEN 4, Edmond RUST 5, Dimitri PAPATHANASSIOU 1, 2, 3, Nicolas PASSAT 3, Fabrice HUBELÉ 6
1 Unit of Nuclear Medicine, Jean Godinot Institute, Reims, France; 2 Laboratory of Biophysics, Research Unit of Medicine, University of Reims Champagne-Ardenne, Reims, France; 3 EA 3804, Science and Information Technology Research Center (CReSTIC), University of Reims Champagne-Ardenne, Reims, France; 4 Unit of Radiophysics, Jean Godinot Institute, Reims, France; 5 Service of Nuclear Medicine, Diaconate Clinic, Mulhouse, France; 6 Service of Biophysics et Nuclear Medicine, Strasbourg University Hospitals, Strasbourg, France
BACKGROUND: Ventilation/perfusion lung scan is subject to blur due to respiratory motion whether with planar acquisition or single photon emission computed tomography (SPECT). We propose a data-driven gating method for extracting different respiratory phases from lung scan list-mode or dynamic data.
METHODS: The algorithm derives a surrogate respiratory signal from an automatically detected diaphragmatic region of interest. The time activity curve generated is then filtered using a Savitzky-Golay filter. We tested this method on an oscillating phantom in order to evaluate motion blur decrease and on one lung SPECT.
RESULTS: Our algorithm reduced motion blur on phantom acquisition: mean full width at half maximum 8.1 pixels on non-gated acquisition versus 5.3 pixels on gated acquisition and 4.1 pixels on reference image. Automated detection of the diaphragmatic region and time-activity curves generation were successful on patient acquisition.
CONCLUSIONS: This algorithm is compatible with a clinical use considering its runtime. Further studies will be needed in order to validate this method.
KEY WORDS: Data science; Nuclear medicine; Single-photon emission-computed tomography