Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls
01 Jun 2013-Medical Physics (American Association of Physicists in Medicine)-Vol. 40, Iss: 6, pp 064301-064301
TL;DR: The authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision for small regions of interest, which can be a considerable pitfall depending on the task of interest.
Abstract: In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore, attention is paid to issues arising from this approach including the pervasive problem of edge artifacts, as well as explanation and potential remedies for this phenomenon. Furthermore, the authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision (reproducibility) for small regions of interest, which can be a considerable pitfall depending on the task of interest.
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TL;DR: Performance measurements of the ToF-PET whole body GE SIGNA PET/MR system indicate that it is a promising new simultaneous imaging platform.
Abstract: Purpose:
The GE SIGNA PET/MR is a new whole body integrated time-of-flight (ToF)-PET/MR scanner from GE Healthcare. The system is capable of simultaneous PET and MR image acquisition with sub-400 ps coincidence time resolution. Simultaneous PET/MR holds great potential as a method of interrogating molecular, functional, and anatomical parameters in clinical disease in one study. Despite the complementary imaging capabilities of PET and MRI, their respective hardware tends to be incompatible due to mutual interference. In this work, the GE SIGNA PET/MR is evaluated in terms of PET performance and the potential effects of interference from MRI operation.
Methods:
The NEMA NU 2-2012 protocol was followed to measure PET performance parameters including spatial resolution, noise equivalent count rate, sensitivity, accuracy, and image quality. Each of these tests was performed both with the MR subsystem idle and with continuous MR pulsing for the duration of the PET data acquisition. Most measurements were repeated at three separate test sites where the system is installed.
Results:
The scanner has achieved an average of 4.4, 4.1, and 5.3 mm full width at half maximum radial, tangential, and axial spatial resolutions, respectively, at 1 cm from the transaxial FOV center. The peak noise equivalent count rate (NECR) of 218 kcps and a scatter fraction of 43.6% are reached at an activity concentration of 17.8 kBq/ml. Sensitivity at the center position is 23.3 cps/kBq. The maximum relative slice count rate error below peak NECR was 3.3%, and the residual error from attenuation and scatter corrections was 3.6%. Continuous MR pulsing had either no effect or a minor effect on each measurement.
Conclusions:
Performance measurements of the ToF-PET whole body GE SIGNA PET/MR system indicate that it is a promising new simultaneous imaging platform.
199 citations
TL;DR: Overall, multi-modal fusion shows significant benefits in clinical diagnosis and neuroscience research and widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
180 citations
TL;DR: The optimum penalization factor (beta) for clinical use of Q.Clear is determined and improved CR and reduced BV is demonstrated when using Q. clear instead of OSEM.
Abstract: Q.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions. Methods: A National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for β (B) values of 100–1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body 18F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability). Results: As β is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly 18F-FDG–avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction. Conclusion: Our phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A β value of 400 is recommended for oncology body PET/CT using Q.Clear.
180 citations
Cites methods from "Resolution modeling in PET imaging:..."
...In our center, OSEM PSF is not used because of the noise seen in the clinical images (23), and so the B100 reconstruction can be discounted for the clinical part of the investigation because of its similarity to OSEM PSF....
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TL;DR: The history of technical developments over 40 years is reviewed and the important clinical research and healthcare applications that have been made possible by these technical advances are summarized.
Abstract: Instrumentation for positron emission tomography (PET) imaging has experienced tremendous improvements in performance over the past 60 years since it was first conceived as a medical imaging modality. Spatial resolution has improved by a factor of 10 and sensitivity by a factor of 40 from the early designs in the 1970s to the high-performance scanners of today. Multimodality configurations have emerged that combine PET with computed tomography (CT) and, more recently, with MR. Whole-body scans for clinical purposes can now be acquired in under 10 min on a state-of-the-art PET/CT. This paper will review the history of these technical developments over 40 years and summarize the important clinical research and healthcare applications that have been made possible by these technical advances. Some perspectives for the future of this technology will also be presented that promise to bring about new applications of this imaging modality in clinical research and healthcare.
155 citations
TL;DR: Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features.
Abstract: The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings Phantom and patient studies were conducted, including two PET/CT scanners Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied Lesions were delineated and one hundred radiomic features were extracted All radiomics features were categorized based on coefficient of variation (COV) Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20% All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies • PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake
• Radiomic features robustness is an important issue over different image reconstruction settings
• Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent
• Robust radiomic features can be considered as good candidates for tumour quantification
146 citations
References
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TL;DR: In this paper, the authors proposed a more accurate general mathematical model for ET where an unknown emission density generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, they gave an algorithm for determining an estimate? of? which maximizes the probability p(n*|?) of observing the actual detector count data n* over all possible densities?.
Abstract: Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density ? = ?(x, y, z) generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate ? of ? which maximizes the probability p(n*|?) of observing the actual detector count data n* over all possible densities ?. Let independent Poisson variables n(b) with unknown means ?(b), b = 1, ···, B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ···, D with p(b, d) a one-step transition matrix, assumed known. We observe the total number n* = n*(d) of emissions in each detector unit d and want to estimate the unknown ? = ?(b), b = 1, ···, B. For each ?, the observed data n* has probability or likelihood p(n*|?). The EM algorithm of mathematical statistics starts with an initial estimate ?0 and gives the following simple iterative procedure for obtaining a new estimate ?new, from an old estimate ?old, to obtain ?k, k = 1, 2, ···, ?new(b)= ?old(b) ?Dd=1 n*(d)p(b,d)/??old(b?)p(b?,d),b=1,···B.
4,288 citations
Journal Article•
TL;DR: The general principles behind all EM algorithms are discussed and in detail the specific algorithms for emission and transmission tomography are derived and the specification of necessary physical features such as source and detector geometries are discussed.
Abstract: Two proposed likelihood models for emission and transmission image reconstruction accurately incorporate the Poisson nature of photon counting noise and a number of other relevant physical features As in most algebraic schemes, the region to be reconstructed is divided into small pixels For each pixel a concentration or attenuation coefficient must be estimated In the maximum likelihood approach these parameters are estimated by maximizing the likelihood (probability of the observations) EM algorithms are iterative techniques for finding maximum likelihood estimates In this paper we discuss the general principles behind all EM algorithms and derive in detail the specific algorithms for emission and transmission tomography The virtues of the EM algorithms include (a) accurate incorporation of a good physical model, (b) automatic inclusion of non-negativity constraints on all parameters, (c) an excellent measure of the quality of a reconstruction, and (d) global convergence to a single vector of parameter estimates We discuss the specification of necessary physical features such as source and detector geometries Actual reconstructions are deferred to a later time
1,921 citations
National Institutes of Health1, Imperial College London2, Aarhus University3, University of Oxford4, University of Wisconsin-Madison5, University of Toronto6, University of California, Los Angeles7, Columbia University8, National Institute of Radiological Sciences9, University of Michigan10, University of Copenhagen11, University of Turku12, VU University Amsterdam13, Brookhaven National Laboratory14, Pfizer15, Washington University in St. Louis16, Indiana University – Purdue University Indianapolis17, University of Pittsburgh18, University of British Columbia19, Emory University20, Johns Hopkins University21, Yale University22
TL;DR: An international group of experts in pharmacokinetic modeling recommends a consensus nomenclature to describe in vivo molecular imaging of reversibly binding radioligands.
Abstract: An international group of experts in pharmacokinetic modeling recommends a consensus nomenclature to describe in vivo molecular imaging of reversibly binding radioligands.
1,858 citations
TL;DR: What PVE is and its consequences in PET tumor imaging are described; the parameters on which PVE depends are reviewed; and actions that can be taken to reduce the errors attributable to PVE are described.
Abstract: PET has the invaluable advantage of being intrinsically quantitative, enabling accurate measurements of tracer concentrations in vivo. In PET tumor imaging, indices characterizing tumor uptake, such as standardized uptake values, are becoming increasingly important, especially in the context of monitoring the response to therapy. However, when tracer uptake in small tumors is measured, large biases can be introduced by the partial-volume effect (PVE). The purposes of this article are to explain what PVE is and to describe its consequences in PET tumor imaging. The parameters on which PVE depends are reviewed. Actions that can be taken to reduce the errors attributable to PVE are described. Various PVE correction schemes are presented, and their applicability to PET tumor imaging is discussed.
1,421 citations
Journal Article•
TL;DR: A new algorithm to correct for PVEs by characterizing the geometric interaction between the PET system and the brain activity distribution, which allows the correction for Pves simultaneously in all identified brain regions, independent of tracer levels.
Abstract: The accuracy of PET for measuring regional radiotracer concentra tions in the human brain is limited by the finite resolution capability of the scanner and the resulting partial volume effects (PVEs). We designed a new algorithm to correct for PVEs by characterizing the geometric interaction between the PETsystem and the brain activity distribution. Methods: The partial volume correction (PVC) algo rithm uses high-resolution volumetric MR Âimages correlated with the PET volume. We used a PET simulator to calculate recovery and cross-contamination factors of identified tissue components in the brain model. These geometry-dependent transfer coefficients form a matrix representing the fraction of true activity from each distinct brain region observed in any given set of regions of interest. This matrix can be inverted to correct for PVEs,independent of the tracer concentrations in each tissue component. A sphere phantom was used to validate the simulated point-spread function of the PET scanner. Accuracy and precision of the PVC method were assessed using a human basal ganglia phantom. A constant contrast experi ment was performed to explore the recovery capability and statistic error propagation of PVC in various noise conditions. In addition, a dual-isotope experiment was used to evaluate the ability of the PVC algorithm to recover activity concentrations in small structures surrounded by background activity with a different radioactive half-life. This models the time-variable contrast between regions that is often seen in neuroreceptor studies. Results: Data from the three-dimensional brain phantom demonstrated a full recovery ca pability of PVC with less than 10% root mean-square error in terms of absolute values, which decreased to less than 2% when results from four PET slices were averaged. Inaccuracy in the estimation of 18Ftracer half-life in the presence of11C background activity was in the range of 25%-50% before PVC and 0%-6% after PVC, for resolution varying from 6 to 14 mm FWHM. In terms of noise propagation, the degradation of the coefficient of variation after PVC was found to be easily predictable and typically on the order of 25%. Conclusion: The PVC algorithm allows the correction for PVEs simultaneously in all identified brain regions, independent of tracer levels.
897 citations