Background: Total variation regularized expectation maximization (TVREM) reconstruction algorithm on the image quality of gallium (GA) prostate-specific membrane antigen-11 ([Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT).

Methods: Images of a phantom with small hot sphere inserts and the total-body PET/CT scans of 51 prostate cancer patients undergoing [Ga]Ga-PSMA-11 were reconstructed using TVREM with 5 different penalization factors between 0.09 and 0.45 and for 20-, 40-, 60-, 120-, and 300-second acquisition, respectively. As a comparison, the same data were also reconstructed using the ordered subset expectation maximization (OSEM) with 3 iterations, 20 subsets, and 300 second acquisition. The contrast recovery coefficients (CRC) and background variability (BV) of the phantom, the tumor-to-background ratios (TBR), the contrast recovery (CR) ratio, the image noise of the liver, and maximum standard uptake value (SUV) of the lesions were calculated to evaluate the image quality. The clinical performance of the images was evaluated by 2 radiologists with a 5-point scale (1-poor, 5-excellent).

Results: The TVREM reconstructions groups fwith 120 second acquisition and the penalization of 0.27 to 0.45 showed the best performance in terms of CR, TBR, image noise, and the gain of SUV compared to that obtained in the OSEM 300 second group. Even the image noise of the TVREM 120 second group with a penalization factor of 0.27 and 0.36 was comparable to the OSEM 300 second group; the lesions' SUV increased by 28% whereas the image noise decreased by 5% and 14%, respectively. The TVREM 120 second group with a penalization factor of 0.36 (5.00±0.00) had the highest qualitative score that equaled OSEM and TVREM for the 300 second (P>0.05) group.

Conclusions: Our study has shown the potential of the TVREM reconstruction algorithm with optimized penalization factors to achieve comparable [Ga]Ga-PSMA-11 total-body PET/CT image quality with a shorter acquisition time, compared with the conventional OSEM reconstruction algorithm.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423362PMC
http://dx.doi.org/10.21037/qims-22-1341DOI Listing

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