Data-driven respiratory gating for ventilation/perfusion lung scan

David Morland; Sofiane Guendouzen; Edmond Rust; Dimitri Papathanassiou; Nicolas Passat; Fabrice Hubelé

Data-driven respiratory gating for ventilation/perfusion lung scan

David Morland ^{1, 2, 3} Sofiane Guendouzen ⁴ Edmond Rust ⁵ Dimitri Papathanassiou ^{1, 3} Nicolas Passat ³ Fabrice Hubelé ⁶

1 Département de Médecine Nucléaire, Institut Jean Godinot

2 Laboratoire de Biophysique

3 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

4 Institut Jean Godinot

5 Service de Médecine Nucléaire, Clinique du Diaconat, Mulhouse

6 Service de Biophysique et Médecine Nucléaire

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.

Document type :

Journal articles

Domain :

Life Sciences [q-bio] / Bioengineering / Imaging

Life Sciences [q-bio] / Cancer

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https://hal.univ-reims.fr/hal-01766863
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Morland QJNMMI 2018.pdf

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