Data-driven respiratory gating for ventilation/perfusion lung scan - Archive ouverte HAL Access content directly
Journal Articles The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of Radiopharmaceutic Year : 2019

Data-driven respiratory gating for ventilation/perfusion lung scan

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Abstract

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.
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Dates and versions

hal-01766863 , version 1 (26-06-2018)

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David Morland, Sofiane Guendouzen, Edmond Rust, Dimitri Papathanassiou, Nicolas Passat, et al.. Data-driven respiratory gating for ventilation/perfusion lung scan. The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of Radiopharmaceutic, 2019, 63 (4), pp.394-398. ⟨10.23736/S1824-4785.18.03002-9⟩. ⟨hal-01766863⟩
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