Quantitative computed tomography evaluation of pulmonary disease

Area of Science:

• Veterinary Radiology
• Pulmonary Imaging
• Parasitology

Background:

• Objective assessment of pulmonary disease using computed tomography (CT) is challenging.
• Relating CT findings to the underlying pathophysiology is crucial for accurate diagnosis.
• Automated volume histogram analysis offers a potential solution for quantitative lung assessment.

Purpose of the Study:

• To evaluate automated volume histogram analysis for differentiating normal from diseased lung tissue in foxes.
• To determine if CT indices derived from histogram analysis correlate with Angiostrongylus vasorum infection severity.
• To assess the utility of this method for monitoring disease progression and treatment response.

Main Methods:

• Pulmonary CT images from 34 foxes (controls and Angiostrongylus vasorum-infected) were analyzed.
• Automated segmentation isolated lung tissue from surrounding structures.
• Volume histograms were generated, and key indices (inter-quartile range, 95th percentile CT number) were calculated.

Main Results:

• Automated segmentation was successful, though severely diseased areas were sometimes excluded.
• Both inter-quartile range and 95th percentile CT number were significantly influenced by infection status (P < 0.001).
• These CT indices demonstrated a strong correlation with worm burden, indicating disease severity.

Conclusions:

• Automated volume histogram analysis provides quantitative data for assessing pulmonary disease in Angiostrongylus vasorum-infected foxes.
• This method is readily achievable and can aid in evaluating disease severity, progression, and treatment efficacy.
• Objective CT indices derived from histogram analysis are valuable for understanding parasitic lung disease.