Accuracy of 3-Dimensional Reconstruction Algorithms for the High-Resolution Research Tomograph

Velden, Floris H. P. van; Kloet, Reina W.; Berckel, Bart N.M. van; Lammertsma, Adriaan A.; Boellaard, Ronald

doi:10.2967/jnumed.108.052985

Cited by 43 publications

(49 citation statements)

References 34 publications

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“…Our procedure for quantitative analysis is similar to the one used by Bettinardi et al [23], who, as part of the performance characterization of the PET/CT Discovery-690 scanner, included an analysis of the 3D Hoffman brain phantom. A similar procedure was described by van Velden et al [24]. Nevertheless, their analyses were only based on the contrast ratio of a grey matter region.…”

Section: Discussionmentioning

confidence: 99%

Brain PET imaging optimization with time of flight and point spread function modelling

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Brain PET imaging optimization with time of flight and point spread function modelling

et al. 2015

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“…This problem of bias commonly occurs in dynamic PET scanning using the High Res- olution Research Tomograph (ECAT HRRT, Siemens/CTI), which is a high resolution human brain scanner [3]. Large biases for low-statistics reconstructions on this scanner have been reported previously [2], [4]. Other researchers using the same PET scanner report much lower levels of bias when using the same image reconstruction algorithm (ordinary-Poisson OSEM (OP-OSEM)) within different image reconstruction software [5].…”

Section: Introductionmentioning

confidence: 97%

“…These reconstruction methods do not always provide quantitative images when reconstructing data at low statistics, i.e. from few noiseequivalent counts (NEC) [1], [2]. This problem of bias commonly occurs in dynamic PET scanning using the High Res- olution Research Tomograph (ECAT HRRT, Siemens/CTI), which is a high resolution human brain scanner [3].…”

Section: Introductionmentioning

confidence: 99%

Bias in iterative reconstruction of low-statistics PET data: Benefits of a resolution model

Walker

Julyan

Talbot

et al. 2009

2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC)

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Ordered-subset expectation maximization (OSEM) is a widely used method of reconstructing PET data. Several authors have reported bias when reconstructing frames containing few counts via OSEM, although the level of bias reported varies substantially. Such bias may lead to errors in biological parameters as derived via dynamic PET. We examine low-statistics bias in OSEM reconstruction of patient data and estimate the subsequent errors in biological parameter estimates. Patient listmode data were acquired during a [ 11 C]-DASB scan using a brain PET scanner, the high resolution research tomograph (HRRT). These data were sub-sampled to create many independent, low-count replicates. Each replicate was reconstructed with and without the use of an image based resolution model (PSF), from which bias and variance were calculated as a function of the noise equivalent counts (NEC). Time-activity curves were subsequently generated by Monte Carlo simulation and used to study the propagation of bias from the images into the biological parameters of interest, for which noise and bias were based on the NEC. The investigation was complemented by simulation of a PET scanner. Significant bias was observed when reconstructing data of low statistical quality, for both human and simulated data. For human data, this bias was substantially reduced by including a PSF model (e.g. caudate head, 1.7 M NEC, -5.5 % bias with PSF, -13 % bias without PSF). For the observed levels of bias, Monte Carlo simulations predicted biases in the binding potential of -4 and -10 % (with/without PSF). The use of the PSF changed the variance characteristics of the images, reducing variance at the voxel level for low to moderate numbers of iterations. We conclude that OSEM reconstruction of dynamic PET data can yield parameter estimates of acceptable accuracy (for DASB), despite producing biased images at low statistics. This is however dependent upon the application. The use of a resolution model is shown to reduce bias and is thus recommended. The most likely mechanism for this reduction is the suppression of noise. The magnitude of the bias for other tracers and methods of data analysis is yet to be evaluated.

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“…The latter was studied for HRRT by Johansson et al (2007) and van Velden et al (2009b), and for HR + by Boellaard et al (2001a).…”

Section: Hr + Versus Hrrt Reconstructionsmentioning

confidence: 99%

In vivo Validation of Reconstruction-Based Resolution Recovery for Human Brain Studies

Mourik

Lubberink

Velden

et al. 2009

J Cereb Blood Flow Metab

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The aim of this study was to validate in vivo the accuracy of a reconstruction-based partial volume correction (PVC), which takes into account the point spread function of the imaging system. The NEMA NU2 Image Quality phantom and five healthy volunteers (using [ 11 C]flumazenil) were scanned on both HR + and high-resolution research tomograph (HRRT) scanners. HR + data were reconstructed using normalization and attenuation-weighted ordered subsets expectation maximization (NAW-OSEM) and a PVC algorithm (PVC-NAW-OSEM). HRRT data were reconstructed using 3D ordinary Poisson OSEM (OP-OSEM) and a PVC algorithm (PVC-OP-OSEM). For clinical studies, parametric volume of distribution (V T ) images were generated. For phantom data, good recovery was found for both OP-OSEM (0.84 to 0.97) and PVC-OP-OSEM (0.91 to 0.98) HRRT reconstructions. In addition, for the HR + , good recovery was found for PVC-NAW-OSEM (0.84 to 0.94), corresponding well with OP-OSEM. Finally, for clinical data, good correspondence was found between PVC-NAW-OSEM and OP-OSEM-derived V T values (slope: 1.02 ± 0.08). This study showed that HR + image resolution using PVC-NAW-OSEM was comparable to that of the HRRT scanner. As the HRRT has a higher intrinsic resolution, this agreement validates reconstruction-based PVC as a means of improving the spatial resolution of the HR + scanner and thereby improving the quantitative accuracy of positron emission tomography.

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Accuracy of 3-Dimensional Reconstruction Algorithms for the High-Resolution Research Tomograph

Cited by 43 publications

References 34 publications

Brain PET imaging optimization with time of flight and point spread function modelling

Brain PET imaging optimization with time of flight and point spread function modelling

Bias in iterative reconstruction of low-statistics PET data: Benefits of a resolution model

In vivo Validation of Reconstruction-Based Resolution Recovery for Human Brain Studies

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