Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

Abstract

PurposeQ.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT.MethodsThe NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region.ResultsBased on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast.ConclusionThe BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

“…Indeed, taking into account that the higher number of subsets used the higher the noise in the resulting image is [20], it would seem that lower β values were su cient to obtain a similar noise level and higher SUV max compared to the OSEM. Also, the optimal β values obtained in our study for 68 Ga-DOTA examinations are higher than the values obtained by Lindström et al [18] and Caribé et al [17] for FDG studies. However, the reference OSEM algorithm used in these studies for comparison with Q.Clear corresponds to the GE OSEM used in present study.…”

“…Indeed, Figure 3a shows that a β of about 500 is su cient to outperform GE OSEM with 1.5 min/bp (p<0.01). Caribé et al [17] investigated the optimal β value to be used for an acquisition time of 1.07 min/bp. As shown above in the result section, our results suggest that Q.Clear with β=700-800 is signi cantly better than GE OSEM with p<0.001.…”

“…Thus, an additional analysis adding β values of 1200, 1300, 1400, 1500 for 1.5 min/bp, 1400, 1500, 1600, 1700 for 1.0 min/bp and 2400, 2600, 2800, 3000 for 0.5 min/bp was performed for a random group of 8 patients. OSEM reconstruction recommended by the manufacturer to be used in clinical setting (3 iterations, 16 subsets, 5 mm post-processing lter) [17,18] was also added to this additional analysis and de ned as GE OSEM reconstruction.…”

Optimization of injected 68Ga-PSMA activity based on list-mode phantom data and clinical validation

“…As more sensitive PET/CT systems and different reconstruction protocols are available, small lesion detectability will improve [25][26][27]. Reduction of acquisition time can be applied without compromising the noise level.…”

Simultaneous emission and attenuation reconstruction in time-of-flight PET using a reference object

“…Further improvements could be included during the reconstruction process to avoid high noise levels and slow convergence of the algorithm [40]. However, the aim of this study was to evaluate the influence of the reference object under different conditions and further improvements of this method were left to future studies.…”