Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?

• Published in: European journal of radiology 2015-10, Vol.84 (10), p.1873-1878
• Main Authors: Parvizi, Nassim, Franklin, James M, McGowan, Daniel R, Teoh, Eugene J, Bradley, Kevin M, Gleeson, Fergus V
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Bayes Theorem; Colorectal liver metastases; Colorectal Neoplasms - pathology; Female; Fluorodeoxyglucose F18 - administration & dosage; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Likelihood Functions; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - secondary; Male; Middle Aged; Multimodal Imaging - methods; Multimodal Imaging - statistics & numerical data; Positron emission tomography; Positron-Emission Tomography - methods; Positron-Emission Tomography - statistics & numerical data; Radiology; Radiopharmaceuticals - administration & dosage; Reconstruction; Retrospective Studies; Signal-to-background; Signal-to-noise; Signal-To-Noise Ratio; Tomography, X-Ray Computed - methods; Tomography, X-Ray Computed - statistics & numerical data
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2015.06.025

Abstract Purpose Iterative reconstruction algorithms are widely used to reconstruct positron emission tomography computerised tomography (PET/CT) data. Lesion detection in the liver by 18F-fluorodeoxyglucose PET/CT (18F-FDG-PET/CT) is hindered by 18F-FDG uptake in background liver parenchyma. The aim of this study was to compare semi-quantitative parameters of histologically-proven colorectal liver metastases detected by 18F-FDG-PET/CT using data based on a Bayesian penalised likelihood (BPL) reconstruction, with data based on a conventional time-of-flight (ToF) ordered subsets expectation maximisation (OSEM) reconstruction. Methods A BPL reconstruction algorithm was used to retrospectively reconstruct sinogram PET data. This data was compared with OSEM reconstructions. A volume of interest was placed within normal background liver parenchyma. Lesions were segmented using automated thresholding. Lesion maximum standardised uptake value (SUVmax ), standard deviation of background liver parenchyma SUV, signal-to-background ratio (SBR), and signal-to-noise ratio (SNR) were collated. Data was analysed using paired Student’s t -tests and the Pearson correlation. Results Forty-two liver metastases from twenty-four patients were included in the analysis. The average lesion SUVmax increased from 8.8 to 11.6 ( p < 0.001) after application of the BPL algorithm, with no significant difference in background noise. SBR increased from 4.0 to 4.9 ( p < 0.001) and SNR increased from 10.6 to 13.1 ( p < 0.001) using BPL. There was a statistically significant negative correlation between lesion size and the percentage increase in lesion SUVmax ( p = 0.03). Conclusions This BPL reconstruction algorithm improved SNR and SBR for colorectal liver metastases detected by 18F-FDG-PET/CT, increasing the lesion SUVmax without increasing background liver SUV or image noise. This may improve the detection of FDG-avid focal liver lesions and the diagnostic performance of clinical 18F-FDG-PET/CT in this setting, with the largest impact for small foci.