Dataset Information

Noise propagation in resolution modeled PET imaging and its impact on detectability.

ABSTRACT: Positron emission tomography imaging is affected by a number of resolution degrading phenomena, including positron range, photon non-collinearity and inter-crystal blurring. An approach to this issue is to model some or all of these effects within the image reconstruction task, referred to as resolution modeling (RM). This approach is commonly observed to yield images of higher resolution and subsequently contrast, and can be thought of as improving the modulation transfer function. Nonetheless, RM can substantially alter the noise distribution. In this work, we utilize noise propagation models in order to accurately characterize the noise texture of reconstructed images in the presence of RM. Furthermore we consider the task of lesion or defect detection, which is highly determined by the noise distribution as quantified using the noise power spectrum. Ultimately, we use this framework to demonstrate why conventional trade-off analyses (e.g. contrast versus noise, using simplistic noise metrics) do not provide a complete picture of the impact of RM and that improved performance of RM according to such analyses does not necessarily translate to the superiority of RM in detection task performance.

SUBMITTER: Rahmim A

PROVIDER: S-EPMC3866837 | biostudies-other | 2013 Oct

REPOSITORIES: biostudies-other

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Noise propagation in resolution modeled PET imaging and its impact on detectability.

Rahmim Arman A Tang Jing J

Physics in medicine and biology 20130913 19

Positron emission tomography imaging is affected by a number of resolution degrading phenomena, including positron range, photon non-collinearity and inter-crystal blurring. An approach to this issue is to model some or all of these effects within the image reconstruction task, referred to as resolution modeling (RM). This approach is commonly observed to yield images of higher resolution and subsequently contrast, and can be thought of as improving the modulation transfer function. Nonetheless, ...[more]

PMID: 24029682

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Project description:Super-resolution (SR) techniques are used in PET imaging to generate a high-resolution image by combining multiple low-resolution images that have been acquired from different points of view (POV). In this article, the authors propose a novel implementation of the SR technique whereby the required multiple low-resolution images are generated by shifting the reconstruction pixel grid during the image reconstruction process rather than being acquired from different POVs. The objective of this article is to compare the performances of the two SR implementations using theoretical and experimental studies. A mathematical framework is first provided to support the hypothesis that the two SR implementations have similar performance in current PET/CT scanners that use block detectors. Based on this framework, a simulation study, a point source study, and a NEMA/IEC phantom study were conducted to compare the performance of these two SR implementations with respect to contrast, resolution, noise, and SNR. For reference purposes, a comparison with a native reconstruction (NR) image using a high-resolution pixel grid was also performed. The mathematical framework showed that the two SR implementations are expected to achieve similar contrast and resolution but different noise contents. These results were confirmed by the simulation and experimental studies. The simulation study showed that the two SR implementations have an average contrast difference of 2.3%, while the point source study showed that their average differences in contrast and resolution were 0.5% and 1.2%, respectively. Comparisons between the SR and NR images for the point source study showed that the NR image exhibited averages of 30% and 8% lower contrast and resolution, respectively. The NEMA/IEC phantom study showed that the three images (two SR and NR) exhibited different noise structures. The SNR of the new SR implementation was, on average, 21.5% lower than the original implementation largely due to an increase in background noise, while the NR image had averages of 18.5% and 8% lower SNR and contrast, respectively, versus the two SR images. The new SR implementation can potentially replace the original SR approach in current PET scanners that use block detectors while maintaining similar contrast and resolution, but at a relatively lower SNR. A major advantage of the new SR implementation is its shorter overall scan duration which results in an increase in scanner throughput and a reduction in patient motion.

Project description:Several investigators have shown that noise equivalent count rate (NECR) is linearly proportional to the square of image signal-to-noise ratio (SNR) when PET images are reconstructed using filtered back-projection. However, to our knowledge, none have shown a similar relationship in fully 3D ordered-subset expectation maximization (OSEM) reconstruction. This paper has two aims. The first is to investigate the NECR-SNR relationship for 3D-OSEM reconstruction using phantom studies while the second aim is to evaluate the NECR-SNR relationship using patient data.An anthropomorphic phantom was scanned on a GE Discovery-STE (DSTE) PET∕CT scanner in 3D mode with an initial activity concentration of 66.34 kBq∕cc. PET data were acquired over the lower chest∕upper abdomen region in dynamic mode. The experiment was repeated with the same activity concentration on a GE Discovery-RX (DRX) scanner. Care was taken to place the phantom at identical positions in both scanners. PET data were then reconstructed using 3D Reprojection (3D-RP) and 3D-OSEM with different reconstruction parameters and the NECR and SNR for each frame∕image were calculated. SNR(2) was then plotted versus the NECR for each scanner, reconstruction method and parameters. In addition, 40 clinical PET∕CT studies from the two scanners (20 patients∕scanner) were evaluated retrospectively. The patient studies from each scanner were further divided into two subgroups of body mass indices (BMI). Each PET study was acquired in 3D mode and reconstructed using both 3D-OSEM and 3D-RP. The NECR and SNR of the bed position covering the patient liver were calculated for each patient and averaged for each subgroup. Comparisons of the NECR and SNR between scanner types and BMIs were performed using a t-test and a p value less than 0.05 was considered significant.Phantom results showed that SNR(2) versus NECR was linear for 3D-RP reconstruction across all activity concentration on both scanners, as expected. However, when 3D-OSEM was used, this relationship was nonlinear at activity concentrations beyond the peak NECR on both scanners. On the other hand, the plot of SNR(2) versus trues count rate was linear for 3D-OSEM across all activity concentrations on both scanners independent of reconstruction parameters used. In addition, for activity concentrations <30kBq∕cc, phantom results showed a higher SNR (by 12 ± 10%; p < 0.05) and NECR for the DRX scanner compared to DSTE for 3D-RP reconstruction. However, for 3D-OSEM reconstruction, these two scanners had similar SNRs (different by 2% ± 9%; p > 0.05), despite having different NECRs. Patient studies showed a statistically significant difference in NECR as well as the SNR for 3D-RP reconstruction between the two scanners. However, no statistically significant difference was found for 3D-OSEM. A statistically significant difference in both NECR and SNR were found between the different BMI subgroups for both 3D-RP and 3D-OSEM reconstructions.For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D-OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D-OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.

Dataset Information

Noise propagation in resolution modeled PET imaging and its impact on detectability.

Publications

Noise propagation in resolution modeled PET imaging and its impact on detectability.

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