Data-driven respiratory gating for ventilation/perfusion lung scan.

Area of Science:

• Nuclear Medicine
• Medical Imaging
• Image Processing

Background:

• Respiratory motion introduces significant blur in ventilation/perfusion (V/P) lung scans, affecting both planar imaging and single-photon emission computed tomography (SPECT).
• Motion artifacts degrade diagnostic accuracy and image quality in V/P lung scintigraphy.

Purpose of the Study:

• To develop and evaluate a data-driven gating method for reducing respiratory motion blur in V/P lung scans.
• To extract distinct respiratory phases from dynamic or list-mode V/P lung scan data.

Main Methods:

• An algorithm was developed to derive a respiratory signal from an automatically identified diaphragmatic region of interest.
• The generated time-activity curve was processed using a Savitzky-Golay filter.
• The method's efficacy was assessed on an oscillating phantom and a patient lung SPECT acquisition.

Main Results:

• The algorithm successfully reduced motion blur in phantom acquisitions, with mean full width at half maximum decreasing from 8.1 pixels (non-gated) to 5.3 pixels (gated), compared to a 4.1-pixel reference.
• Automated diaphragmatic region detection and time-activity curve generation were effective in patient data.
• The gating method demonstrated a significant reduction in motion-induced artifacts.

Conclusions:

• The proposed data-driven gating algorithm is compatible with clinical workflows due to its efficient runtime.
• Further research is warranted to validate this method in larger patient cohorts.
• This technique offers a promising solution for improving V/P lung scan quality.