Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.

Clinical widespread use of edge-preserving penalized-likelihood (PL) methods has been hindered by the properties of the resulting images such as blocky background noise textures, piecewise-constant appearances of organs and relative noise strengths in high and low activity regions despite their potential for improved lesion quantitation over OSEM and quadratically penalized PL. Here, we investigate the use of the convex relative difference penalty first introduced by Nuyts et at. (TNS '02) for improved quantitation over OSEM in whole-body clinical PET imaging while maintaining visual image properties similar to OSEM and therefore clinical acceptability. We perform data-independent axial smoothing modulation based on the system sensitivity profile in order to avoid excessively smooth bed-position overlap regions. We also perform data-independent transaxial smoothing modulation to avoid oversmoothing the central portions of the field-of-view that occur with the use of a constant smoothing parameter. The resulting overall smoothing modulation profile allows for improved resolution uniformity in regions with high sensitivity and improved noise uniformity between regions of low and high sensitivity. We evaluate our approach in multiple clinical datasets with lesions inserted into representative locations with time-of-flight (TOF) and non-TOF reconstructions. Such "hybrid" datasets combine clinically realistic image backgrounds with known lesionactivities. We demonstrate that using the relative difference penalty with proper smoothing modulation, superior quantitation over early-stopped and post-filtered OS EM can be achieved while maintaining clinically acceptable image quality. Furthermore, the approach lends itself to theoretical contrast recovery prediction and bias correction for improved contrast recovery consistency across lesions and further improvements in quantitation.

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was [Formula: see text] ([Formula: see text]), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast [Formula: see text] and [Formula: see text], respectively. For all other cases, there was no statistically significant difference between PL and OSEM ([Formula: see text]). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Both 201Tl uptake and 99mTc-teboroxime kinetic parameters were well correlated with flow. The 99mTc-teboroxime washin parameters offer semiquantitative flow values and provide greater defect contrast than can be obtained with 201Tl uptake values.

In this study, we implemented a fully 3D Maximum Likelihood Ordered Subsets Expectation Maximization (ML-OSEM) reconstruction algorithm with two methods for corrections of randoms, and scatter coincidences: (a) Measured data were pre-corrected for randoms and scatter, and (b) Corrections were incorporated into the iterative algorithm. In 3D PET acquisitions, the random and scatter coincidences constitute a significant fraction of the measured coincidences. ML-OSEM reconstruction algorithms make assumptions of Poisson distributed data. Pre-corrections for random and scatter coincidences result in deviations from that assumption, potentially leading to increased noise and inconsistent convergence. Incorporating the corrections inside the loop of the iterative reconstruction preserves the Poisson nature of the data. We performed Monte Carlo simulations with different randoms fractions and reconstructed the data with the two methods. We also reconstructed clinical patient images. The two methods were compared quantitatively through contrast and noise measurements. The results indicate that for high levels of randoms, incorporating the corrections inside the iterative loop results in superior image quality.