Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM (original) (raw)
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Connections Between Noise Equivalent Count Rate and Image Noise in Pet Medical Imaging
RAD Conference Proceedings, 2016
Assessing PET image quality is a challenge due to its clinical subjectivity and difficulties in standardisation. Methods to evaluate PET image quality include image noise and noise equivalent count rate (NECR), which are automatic and objective measurements determined from the reconstructed medical PET image or PET raw emission data from phantoms. Although manufacturers emphasize scanner performance by stating peak NECR, the peak NECR is often outside clinically limited by radiation safety factors, as opposed to scanner performance. instead, image noise in the expectation maximization (EM) algorithm inferred that this could also be true for three algorithm. One consequence is that for traditionally based on NECR, should be based on the true produced by increasing the injected activity to match the peak true count rate for 3D
Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 2018
Introduction: The goals of the study are to characterize imaging properties in 2D PET images reconstructed with the iterative algorithm Ordered-Subset Expectation Maximization (OSEM) and to propose a new method for the generation of synthetic images. Material and methods: The noise is analyzed in terms of its magnitude, spatial correlation, and spectral distribution through standard deviation, Autocorrelation Function, and Noise Power Spectrum (NPS), respectively. Their variations with position and activity level are also analyzed. This noise analysis is based on phantom images acquired from 18 F uniform distributions. Experimental Recovery Coefficients of hot spheres in different backgrounds are employed to study the spatial resolution of the system through Point Spread Function (PSF). The NPS and PSF functions provide the baseline for the proposed simulation method: convolution with PSF as kernel and noise addition from NPS. Results: The noise spectral analysis shows that the main contribution is of random nature. It is also proven that attenuation correction does not alter noise texture but it modifies its magnitude. Finally, synthetic images of two phantoms, one of them an anatomical brain, are quantitatively compared with experimental images showing a good agreement in terms of pixel values and pixel correlations. Thus, the Contrast to Noise Ratio for the biggest sphere in the NEMA IEC phantom is 10.7 for the synthetic image and 8.8 for the experimental image. Conclusions: The properties of the analyzed OSEM-PET images can be described by NPS and PSF functions. Synthetic images, even anatomical ones, are successfully generated by the proposed method based on the NPS and PSF.
A Monte Carlo model of noise components in 3D PET
IEEE Transactions on Nuclear Science, 2002
This work presents a new model, developed by Monte Carlo methods, to estimate noise components (scatter and random coincidences) in three-dimensional (3-D) positron emission tomography (PET). The model allows the amount, spatial, and temporal distribution of true, scattered, and random coincidences to be estimated independently for any radioactive source (both phantoms and real patients), taking proper account of system dead time. The model was applied to a 3-D NaI(Tl) current-generation PET scanner for which there are no currently available methods for estimating scatter and random components in whole-body studies. The quantitative accuracy of the developed noise model was tested by comparing simulated and measured PET data in terms of physical parameters, count-rate curves, and spatial distribution profiles. Scatter and random components were assessed for phantoms representing brain, abdomen, and whole-body studies. Evidence was found of high scatter and random contribution in 3-D PET clinical studies. The clinical response of the PET system, in terms of signal-to-noise ratio, was assessed and optimized, confirming the suitability of the default energy window, although suggesting a possible improvement by setting a lower energy threshold higher than the current default. The proposed noise model applies to any current generation 3-D PET scanner and has been included in the Monte Carlo software package PET-EGS, devoted to 3-D PET and freely available from the authors.
Noise Equivalent Count Rate and Image in Pet Medical Imaging
2016
Assessing PET image quality is a challenge due to its clinical subjectivity and difficulties in standardisation. Methods to evaluate PET image quality include image noise and noise equivalent count rate (NECR), which are automatic and objective measurements determined from the reconstructed medical PET image or PET raw emission data from phantoms. Although manufacturers emphasize scanner performance by stating peak NECR, the peak NECR is often outside clinically limited by radiation safety factors, as opposed to scanner performance. instead, image noise in the expectation maximization (EM) algorithm inferred that this could also be true for three algorithm. One consequence is that for traditionally based on NECR, should be based on the true produced by increasing the injected activity to match the peak true count rate for 3D
Poisson Noise Obscures Hypometabolic Lesions in PET
The Yale Journal of Biology and Medicine, 2012
The technology of fluoro-deoxyglucose positron emission tomography (PET) has drastically increased our ability to visualize the metabolic process of numerous neurological diseases. The relationship between the methodological noise sources inherent to PET technology and the resulting noise in the reconstructed image is complex. In this study, we use Monte Carlo simulations to examine the effect of Poisson noise in the PET signal on the noise in reconstructed space for two pervasive reconstruction algorithms: the historical filtered back-projection (FBP) and the more modern expectation maximization (EM). We confirm previous observations that the image reconstructed with the FBP biases all intensity values toward the mean, likely due to spatial spreading of high intensity voxels. However, we demonstrate that in both algorithms the variance from high intensity voxels spreads to low intensity voxels and obliterates their signal to noise ratio. This finding has profound impacts on the clinical interpretation of hypometabolic lesions. Our results suggest that PET is relatively insensitive when it comes to detecting and quantifying changes in hypometabolic tissue. Further, the images reconstructed with EM visually match the original images more closely, but more detailed analysis reveals as much as a 40 percent decrease in the signal to noise ratio for high intensity voxels relative to the FBP. This suggests that even though the apparent spatial resolution of EM outperforms FBP, the signal to noise ratio of the intensity of each voxel may be higher in the FBP. Therefore, EM may be most appropriate for manual visualization of pathology, but FBP should be used when analyzing quantitative markers of the PET signal. This suggestion that different reconstruction algorithms should be used for quantification versus visualization represents a major paradigm shift in the analysis and interpretation of PET images.
2017
This study was to assess quantitatively the accuracy of 18F-FDG PET/CT images reconstructed by TOF + PSF and TOF only, considering the noise-matching concept to minimize probable bias in evaluating algorithm performance caused by noise. PET images of similar noise level were considered. Measurements were made on an in-house phantom with hot inserts of Φ10–37 mm, and oncological images of 14 patients were analyzed. The PET images were reconstructed using the OSEM, OSEM + TOF and OSEM + TOF + PSF algorithms. Optimal reconstruction parameters including iteration, subset, and FWHM of post-smoothing filter were chosen for both the phantom and patient data. In terms of quantitative accuracy, the recovery coefficient (RC) was calculated for the phantom PET images. The signal-to-noise ratio (SNR), lesion-to-background ratio (LBR), and SUVmax were evaluated from the phantom and clinical data. The smallest hot insert (Φ10 mm) with 2:1 activity concentration ratio could be detected in the PET ...
Tomography
For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on th...
The Influence of Noise in Dynamic PET Direct Reconstruction
IFMBE Proceedings, 2016
In the present work a study is carried out in order to assess the efficiency of the direct reconstruction algorithms on noisy dynamic PET data. The study is performed via Monte Carlo simulations of a uniform cylindrical phantom whose emission values change in time according to a kinetic law. After generating the relevant projection data and properly adding the effects of different noise sources on them, the direct reconstruction and parametric estimation algorithm is applied. The resulting kinetic parameters and reconstructed images are then quantitatively evaluated with appropriate indexes. The simulation is repeated considering different sources of noise and different values of them. The results obtained allow us to affirm that the direct reconstruction algorithm tested maintains a good efficiency also in presence of noise.
Enhanced Parameter Estimation From Noisy PET Data
Academic Radiology, 2005
Rationale and Objectives. The reliability of positron emission tomographic (PET) images depends on the number of annihilation events that are detected. Short image durations are required to capture rapid tracer dynamics, and the resultant images are noisy. Consequently, direct parameter estimation from time-activity curves at high resolution often is unreliable. If adjacent voxels are combined into larger regions of interest the reliability of parameter estimation may be improved, but at the expense of decreased spatial resolution. In this report, a method is presented that provides an alternative to degrading image resolution.