title: Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: Nassim.parvizi@gmail.com.
authors.forename: Nassim
authors.initials: N
authors.lastname: Parvizi
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: James.franklin@oncology.ox.ac.uk.
authors.forename: James M
authors.initials: JM
authors.lastname: Franklin
authors.affiliation: Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, Oxfordshire OX3 7DQ,UK; Radiation Physics and Protection, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: daniel.mcgowan@oncology.ox.ac.uk.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: McGowan
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: Eugene.teoh@ouh.nhs.uk.
authors.forename: Eugene J
authors.initials: EJ
authors.lastname: Teoh
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: Kevin.bradley@ouh.nhs.uk.
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Bradley
authors.affiliation: Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK. Electronic address: Fergus.gleeson@oncology.ox.ac.uk.
authors.forename: Fergus V
authors.initials: FV
authors.lastname: Gleeson
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000473:pathology; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000008:administration & dosage*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary*; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods* / Q000706:statistics & numerical data; D049268:Positron-Emission Tomography / Q000379:methods* / Q000706:statistics & numerical data; D019275:Radiopharmaceuticals / Q000008:administration & dosage*; D012189:Retrospective Studies; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: Colorectal liver metastases; Positron emission tomography; Reconstruction; Signal-to-background; Signal-to-noise