• Overview
• Model-Based Segmentation Engine
• Lung Volumetry
• Lung Attenuation Analysis
• Lung Texture Classification
• Dynamic Imaging
• Airway Morphometry
• Fissure Integrity Scoring
• Emphysema Treatment Targeting and Outcome Assessment
• Lung Fibrosis Scoring
• Functional Imaging in Asthma

Lung Fibrosis Scoring

The UCLA Thoracic Imaging Research Group in collaboration with MedQIA has developed a new imaging biomarker for the quantitative assessment of lung fibrosis on CT. This technique is based on Lung Texture Classification and includes noise removal and Support Vector Machine (SVM) classification as shown in the schematic below. The images are grid sampled to provide a voxel-by-voxel tissue classification. The classification technique is automated to maximize reproducibility. The technique has been validated against visual assessments by expert radiologists. Lung fibrosis scoring has also been demonstrated to be predictive of treatment efficacy in a scleroderma treatment study (Kim 2008). The image below shows voxels classified as fibrosis in a chest CT scan.

	Overview of the computation of the Quantitative Lung Fibrosis (QLF) Score.