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Impact of FDG-PET/CT Reconstruction Parameters On PET Variability and Treatment Response Assessment

L Rankine

L Rankine1*, J Wilson2, J Bowsher3, D Brizel3, F Yin3, T Turkington4, S Das3, (1) Medical Physics Graduate Program, Duke University, Durham, NC, (2) Clinical Imaging Physics Group, Duke University Medical Center, Durham, NC, (3) Department of Radiation Oncology, Duke University Medical Center, Durham, NC, (4) Department of Radiology, Duke University Medical Center, Durham, NC

SU-E-J-200 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: To examine the effect of PET image reconstruction parameters on baseline and early-treatment FDG-PET/CT quantitative imaging. Early-treatment changes in tumor metabolism in primary tumor and nodes can potentially determine if the patient is responding to therapy, but this assessment can change based on the reconstruction parameters. We investigate the effect of the following reconstruction parameters: number of Ordered-Subset-Expectation-Maximization (OSEM) iterations, post-reconstruction smoothing, and quantitative metrics (SUV-max, SUV-mean, SUV-peak).

Methods: 11 patients on an IRB-approved study underwent 2 baseline PET scans (mean-separation=11 days) prior to chemoradiotherapy (70Gy,2Gy/fraction). An intra-treatment PET scan was performed early in the course of therapy (10-20Gy,mean=14Gy). The images were reconstructed with varying OSEM iterations (1-12) and Gaussian filtering (1-7mm). For each combination of iterations and smoothing, Bland-Altman analysis was applied to quantitative metrics (SUV-max, SUV-mean, SUV-peak) from the baseline scans to evaluate metabolic variability (repeatability=1.96σ). The number and extent of early treatment changes that were significant, i.e. exceeding repeatability, was assessed.

Results: Repeatability improved with increasing smoothing and decreasing iterations from SUV=3.9 (iterations=12,smoothing=1mm) to SUV=2.1 (iterations=1,smoothing=7mm). The average number of cases exceeding repeatability averaged over all metrics (SUV-max, SUV-mean, SUV-peak) and all structures (primary, nodes) improved with increasing smoothing and decreasing iterations from 3.3 cases (ΔSUV>repeatability=17%) for iterations=12 and smoothing=1mm to 3.9 cases (ΔSUV>repeatability=19%) for iterations=1 and smoothing=5.5mm. Smoothing beyond 5.5mm deteriorated results. Practically, several combinations provided comparable performance (low repeatability,high number/extent of intra-treatment cases exceeding repeatability): 1 iteration with 1-5.5 mm smoothing, 2 iterations with 2-5.5 mm smoothing, 4 iterations with 4-5.5 mm smoothing. In general, increasing iterations increased the lower limit of filter size required.

Conclusion: Images reconstructed for quantitative analysis may benefit from a low number of OSEM iterations (1-2). Some post-reconstruction smoothing was beneficial, however, oversmoothing for the sake of more qualitatively appealing images may prove detrimental to quantitative response assessment analysis.

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