Fully automated regional lung perfusion quantification in SPECT/CT images with open-source software

Area of Science:

• Nuclear medicine
• Medical imaging
• Computational imaging

Background:

• Lung perfusion scintigraphy is crucial for assessing various health conditions.
• Current segmentation and quantification methods for SPECT/CT lung perfusion images often rely on expensive software and manual input.
• There is a need for automated, accessible tools for lung perfusion analysis.

Purpose of the Study:

• To develop a fully automated algorithm for segmenting and quantifying lung perfusion in SPECT and SPECT/CT images.
• To utilize only free software, making the tool widely accessible.
• To improve efficiency and reliability in lung perfusion imaging analysis.

Main Methods:

• A fully automated algorithm was developed using Python for the 3D Slicer platform.
• The algorithm performs segmentation and quantification of lobar and whole lung perfusion.
• The algorithm was validated on 37 retrospectively collected lung perfusion SPECT/CT images.

Main Results:

• The algorithm successfully performed fully automated lobar perfusion quantification.
• Accurate relative perfusion values were obtained for all lung lobes (e.g., LUL 23.5%, LLL 22.3%, RUL 24.6%, RML 7.9%, RLL 21.7%).
• Quantification of left (44.6%) and right (55.3%) lung perfusion showed no significant difference with or without CT data.

Conclusions:

• The developed algorithm is fully automated, requiring no user interaction.
• It demonstrates good agreement with existing literature and is significantly faster than previous methods.
• This free, efficient, and reliable tool can aid in the diagnosis of lung conditions.