Estimating normal metabolic activity for disease quantification via PET/CT images

Area of Science:

• Medical Imaging
• Radiomics
• Quantitative Imaging

Background:

• Accurate disease quantification in medical imaging is crucial for diagnosis and treatment monitoring.
• Positron Emission Tomography/Computed Tomography (PET/CT) offers valuable metabolic information but requires robust quantification methods.
• Current methods often rely on lesion segmentation, which can be challenging and time-consuming.

Purpose of the Study:

• To develop and validate a novel pipeline for disease quantification in PET/CT images.
• To enable accurate measurement of total lesion burden (TLB) without explicit lesion segmentation.
• To differentiate between normal metabolic activity and disease-specific activity within anatomical objects.

Main Methods:

• A pipeline integrating standardized uptake value (SUV) standardization, object segmentation, and disease quantification (DQ) was developed.
• DQ estimates total metabolic activity (TMA) and normal metabolic activity (TMA_n) using object-specific SUV distribution models derived from normal subjects.
• Two SUV modeling strategies (hard and fuzzy) were explored to define normal tissue activity.
• Evaluation utilized clinical and phantom datasets, with TLB/TMA ratio as the primary metric.

Main Results:

• The proposed pipeline successfully quantified disease burden in PET/CT images.
• The ratio of absolute TLB to TMA was close to 0 for normal objects and approached 1 for lesions.
• This indicates minimal contribution from normal tissue and significant contribution from lesion tissue to the quantified burden.

Conclusions:

• The novel pipeline provides an effective method for disease quantification in PET/CT, focusing on total lesion burden.
• The approach accurately distinguishes disease-related metabolic activity from normal tissue activity.
• This technique holds promise for improved assessment of disease extent in various conditions depicted by PET/CT.