Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules

Abstract

Objectives

Investigate the effect of a novel Bayesian penalised likelihood (BPL) reconstruction algorithm on analysis of pulmonary nodules examined with 18F-FDG PET/CT, and to determine its effect on small, sub-10-mm nodules.

Methods

18F-FDG PET/CTs performed for nodule evaluation in 104 patients (121 nodules) were retrospectively reconstructed using the new algorithm, and compared to time-of-flight ordered subset expectation maximisation (OSEM) reconstruction. Nodule and background parameters were analysed semi-quantitatively and visually.

Results

BPL compared to OSEM resulted in statistically significant increases in nodule SUV _max (mean 5.3 to 8.1, p < 0.00001), signal-to-background (mean 3.6 to 5.3, p < 0.00001) and signal-to-noise (mean 24 to 41, p < 0.00001). Mean percentage increase in SUV _max (%ΔSUV _max) was significantly higher in nodules ≤10 mm ( n = 31, mean 73 %) compared to >10 mm ( n = 90, mean 42 %) ( p = 0.025). Increase in signal-to-noise was higher in nodules ≤10 mm (224 %, mean 12 to 27) compared to >10 mm (165 %, mean 28 to 46). When applying optimum SUV _max thresholds for detecting malignancy, the sensitivity and accuracy increased using BPL, with the greatest improvements in nodules ≤10 mm.

Conclusion

BPL results in a significant increase in signal-to-background and signal-to-noise compared to OSEM. When semi-quantitative analyses to diagnose malignancy are applied, higher SUV _max thresholds may be warranted owing to the SUV _max increase compared to OSEM.

Key Points

• Novel Bayesian penalised likelihood PET reconstruction was applied for lung nodule evaluation.

• This was compared to current standard of care OSEM reconstruction.

• The novel reconstruction generated significant increases in lung nodule signal-to-background and signal-to-noise.

• These increases were highest in small, sub-10-mm pulmonary nodules.

• Higher SUV _max thresholds may be warranted when using semi-quantitative analyses to diagnose malignancy.