James Franklin MA, MBBS, Abstract Co-Author: Nothing to Disclose

Daniel Robin McGowan, Abstract Co-Author: Nothing to Disclose

Nassim Parvizi MBBS, BSC, Presenter: Nothing to Disclose

Fergus Vincent Gleeson MBBS, Abstract Co-Author: Alliance Medical Ltd Consultant

Iterative reconstruction algorithms are widely used for clinical PET reconstructions. Background signal in the liver reduces the sensitivity of PET for liver lesions, particularly for small lesions. We tested whether a novel iterative reconstruction technique using a penalized likelihood reconstruction would improve lesion signal-to-background ration (SBR) in patients with colorectal liver metastases.

A Bayesian penalized likelihood reconstruction algorithm (QClear, GE Healthcare, Milwaukee, USA) was used to retrospectively reconstruct sinogram PET data. The resulting images were compared to a clinical time of flight-ordered subsets expectation maximization (TOF-OSEM) reconstruction. A volume of interest was placed within normal liver parenchyma and lesions were segmented using automated thresholding. Lesion SUVmax and SUVpeak, and background SUVmax and standard deviation of SUV (noise) were recorded. SBR was defined as the ratio of lesion SUVmax to background SUVmax. Paired t-tests were used for intergroup comparisons.

16 patients with 28 histologically proven hepatic metastases from colorectal adenocarcinoma were included. The novel and clinical algorithm were successfully applied to all datasets. The average lesion SUVmax increased from 8.35 to 11.4 (p<0.001) with no significant difference in background noise. SBR increased from 2.95 to 3.88 (p<0.001).

This penalized likelihood reconstruction algorithm improved signal-to-background for focal liver lesions, principally by increasing the measured lesion SUVmax, without increasing image noise.

Novel penalized likelihood reconstruction algorithms can significantly improve signal-to-background for focal liver lesions, which may improve the diagnostic performance of clinical PET.

Franklin, J, McGowan, D, Parvizi, N, Gleeson, F, Novel penalized Likelihood Reconstruction of [18]F-FDG-PET Data Improves the Signal-to-Background Ratio of Colorectal Liver Metastases.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14013128.html