Nassim Parvizi1, James M Franklin2, Daniel R McGowan3, Eugene J Teoh4, Kevin M Bradley5, Fergus V Gleeson6. 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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.
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.
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.
Authors: Kristen A Wangerin; Sangtae Ahn; Scott Wollenweber; Steven G Ross; Paul E Kinahan; Ravindra M Manjeshwar Journal: J Med Imaging (Bellingham) Date: 2016-11-22
Authors: Eugene J Teoh; Daniel R McGowan; Ruth E Macpherson; Kevin M Bradley; Fergus V Gleeson Journal: J Nucl Med Date: 2015-07-09 Impact factor: 10.057