Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function

Method to determine the statistical technical variability of SUV metrics

BACKGROUND

The Standardized Uptake Value (SUV) Max, SUVMean, and SUVPeak are metrics used to quantify positron emission tomography (PET) images. In order to assess the significance of a change in these metrics for diagnostic purposes, it is relevant to know their variation. The sources of variation can be biological or technical. In this study, we present a method to determine the statistical technical variation of SUV in PET images.

RESULTS

This method was tested on a NEMA quality phantom with spheres of various diameters with a full-length acquisition time of 150 s per bed position and foreground-to-background activity ratio of F18-2-fluoro-2-deoxy-D-glucose (FDG) of 10:1. Our method divides the 150 s acquisition into subsets with statistically independent frames of shorter reconstruction length. SUVMax, Mean and Peak were calculated for each reconstructed image in a subset. The coefficient of variation of SUV within each subset has been used to estimate the expected coefficient of variation at 150 s reconstruction length. We report the largest coefficient of variation of the SUV metrics for the smallest sphere and the smallest variation for the largest sphere. The expected variation at 150 s reconstruction length does not exceed 6% for the smallest sphere and 2% for the largest sphere.

CONCLUSIONS

With the presented method, we aim to determine the statistical technical variation of SUV. The method enables the evaluation of the effect of SUV metric choice (Max, Mean, Peak) and lesion size on the technical variation and, therefore, to evaluate its relevance on the total variation of the SUV value between clinical studies.

Image Noise Covariance Can be Adjusted by a Noise Weighted Filtered Backprojection Algorithm

It was believed that the noise covariance of an image reconstructed with the filtered backprojection (FBP) algorithm is anisotropic. This paper shows that the noise-weighted FBP algorithm is able to alter the noise covariance and make it approximately isotropic. For the noise-weighted FBP algorithm, this paper develops a closed-form expression for the noise variance image and a closed-form expression for the noise covariance image. Computer simulations are carried out to evaluate the noise covariance with and without the noise weighting in the FBP algorithm. Transmission and emission noise models are used in computer simulations. The noise weighted FBP algorithm has a parameter that emulates the iteration number in the iterative Landweber algorithm. It is observed that the noise covariance can be altered by this emulated iteration number when noise weighting is used. The noise weighting in the noise-weighted FBP algorithm is able to change the noise covariance and a proper selection of the emulated iteration number may be able to give an approximately isotropic image noise covariance function.

Mapping the landscape of human dopamine D2/3 receptors with [11C]raclopride

The dopamine D2/3 system is fundamental for sensory, motor, emotional, and cognitive aspects of behavior. Small-scale human histopathological and animal studies show high density of D2/3 dopamine receptors (D2/3DR) in striatum, but also demonstrate the existence of such receptors across cortical and limbic regions. Assessment of D2/3DR BP_ND in the extrastriatal regions with [¹¹C]raclopride has long been considered unreliable due to the relatively low density of D2/3DR outside the striatum. We describe the distribution and interregional links of D2/3DR availability measured with PET and [¹¹C]raclopride across the human brain in a large sample (N = 176; age range 64–68 years). Structural equation modeling revealed that D2/3DR availability can be organized according to anatomical (nigrostriatal, mesolimbic, mesocortical) and functional (limbic, associative, sensorimotor) dopamine pathways. D2/3DR availability in corticolimbic functional subdivisions showed differential associations to corresponding striatal subdivisions, extending animal and pharmacological work. Our findings provide evidence on the dimensionality and organization of [¹¹C]raclopride D2/3DR availability in the living human brain that conforms to known dopaminergic pathways.

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Purpose

PET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM).

Methods

In the present study, groups of subject-images with a 10%- and 20%- difference in binding of [¹²³I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BP_ND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [¹²³I]IMZ and [¹¹C]flumazenil (FMZ) clinical images (n = 4) and the variance of BP_ND was evaluated.

Results

Estimations from FA-denoised simulated images provided a more favorable bias-precision profile in SRTM and LNIGA quantification. Simulated differences were detected in a higher number of voxels when denoised simulated images were used for voxel-wise estimations, compared to quantification on raw simulated images. Variability of voxel-wise binding estimations on denoised clinical SPECT and PET images was also significantly diminished.

Conclusion

In conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BP_ND estimations and detection of biological differences using SPM.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson's disease

PURPOSE

The purpose of this study was to evaluate the effects of different image reconstruction algorithms on topographic characteristics and diagnostic performance of the Parkinson's disease related pattern (PDRP).

METHODS

FDG-PET brain scans of 20 Parkinson's disease (PD) patients and 20 normal controls (NC) were reconstructed with six different algorithms in order to derive six versions of PDRP. Additional scans of 20 PD, 25 atypical parkinsonism (AP) patients and 20 NC subjects were used for validation. PDRP versions were compared by assessing differences in topographies, individual subject scores and correlations with patient's clinical ratings. Discrimination of PD from NC and AP subjects was evaluated across cohorts.

RESULTS

The region weights of the six PDRPs highly correlated (R ≥ 0.991; p < 0.0001). All PDRPs' expressions were significantly elevated in PD relative to NC and AP subjects (p < 0.0001) and correlated with clinical ratings (R ≥ 0.47; p < 0.05). Subject scores of the six PDRPs highly correlated within each of individual healthy and parkinsonian groups (R ≥ 0.972, p < 0.0001) and were consistent across the algorithms when using the same reconstruction methods in PDRP derivation and validation. However, when derivation and validation reconstruction algorithms differed, subject scores were notably lower compared to the reference PDRP, in all subject groups.

CONCLUSION

PDRP proves to be highly reproducible across FDG-PET image reconstruction algorithms in topography, ability to differentiate PD from NC and AP subjects and clinical correlation. When calculating PDRP scores in scans that have different reconstruction algorithms and imaging systems from those used for PDRP derivation, a calibration with NC subjects is advisable.

Characterization and simulation of noise in PET images reconstructed with OSEM: Development of a method for the generation of synthetic images

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 ¹⁸F 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 2 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.

Computer-assisted quantification of lung tumors in respiratory gated PET/CT images: phantom study

A computer-aided method was developed to automatically localize tumors in lung PET images of discrete bins within the breathing cycle, followed by an algorithm that registers all the information of a complete respiratory cycle into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body, and Optical Flow registration were compared as well as two registration schemes: Direct scheme and Successive scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level, and computation time. After motion correction, the best compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become faster and more precise.

A practical way to improve contrast-to-noise ratio and quantitation for statistical-based iterative reconstruction in whole-body PET imaging

In whole-body positron emission tomography (PET) imaging, the detection of small uptake foci (i.e., around two or three times the tomograph's spatial resolution) is a critical issue. Indeed, spatial resolution is altered by postreconstruction smoothing operations used to reduce the noise introduced by (among other things) an inaccurate system matrix. The authors previously proposed a device-dedicated projector, easily applicable on a clinical gantry, based on point-source measurements, which introduces less noise than a geometrical model. In the present study, they took advantage of the lower noise levels by reducing the postfilter and then quantified the approach's impact on image quality. This study was performed on an IEC Body Phantom Set filled with 18F (sphere-to-background activity ratio: 4:1). The same 3 min acquisition was reconstructed with either (i) a clinical system based on a geometrical tomographic operator (OSEM_CL) or (ii) an OSEM algorithm using the suggested system matrix (OSEM_DR). In order to compare the resulting images, they set the 3D Gaussian postfilter (3DGPF) for OSEM_DR so as to obtain similar background signal-to-noise ratio (SNR) to that of OSEM_CL with a Gaussian postfilter full width at half maximum of 5 mm (as recommended for whole-body imaging on a Biograph6). They then assessed the contrast-to-noise ratio (CNR) and quantitation [contrast recovery (CR)] for the phantom's four smallest spheres (with internal diameters of 10, 13, 17, and 22 mm). Evaluation of 3DGPFs ranging from 2.2 to 2.6 mm showed that a value of 2.4 mm in OSEM_DR gave the closest background SNR to that of OSEM_CL with a 3DGPF of 5 mm. For all studied targets, the CNR was higher with OSEM_DR than with OSEM_CL. For the 10 and 13 mm spheres, OSEM_DR increased the size of the CNR peaks by 37% and 20%, relative to OSEM_CL. The OSEM_DR technique yielded higher CR values than OSEM_CL did. For the 10, 13, 17, and 22 mm spheres, the CR values at eight iterations were 0.5, 0.6, 1.1, and 1.0 for OSEM_DR and 0.3, 0.4, 0.9, and 0.8 for OSEM_CL. They evaluated a practical method for determining a device-dedicated system matrix based on point-source acquisitions. This tomographic operator is more realistic than geometrical system matrix and introduces less noise into PET images during statistical reconstruction; it thus reduces the extent of postfiltering operations required. Thus, spatial resolution is better maintained with OSEM_DR than with clinical reconstruction. They showed that this method improves the contrast-to-noise ratio and quantification of uptake foci (especially those that are at the system's limit of detection) and, in a clinical context, could allow better detection and earlier diagnosis.

Masked volume wise Principal Component Analysis of small adrenocortical tumours in dynamic [11C]-metomidate Positron Emission Tomography

BACKGROUND

In previous clinical Positron Emission Tomography (PET) studies novel approaches for application of Principal Component Analysis (PCA) on dynamic PET images such as Masked Volume Wise PCA (MVW-PCA) have been introduced. MVW-PCA was shown to be a feasible multivariate analysis technique, which, without modeling assumptions, could extract and separate organs and tissues with different kinetic behaviors into different principal components (MVW-PCs) and improve the image quality.

METHODS

In this study, MVW-PCA was applied to 14 dynamic 11C-metomidate-PET (MTO-PET) examinations of 7 patients with small adrenocortical tumours. MTO-PET was performed before and 3 days after starting per oral cortisone treatment. The whole dataset, reconstructed by filtered back projection (FBP) 0-45 minutes after the tracer injection, was used to study the tracer pharmacokinetics.

RESULTS

Early, intermediate and late pharmacokinetic phases could be isolated in this manner. The MVW-PC1 images correlated well to the conventionally summed image data (15-45 minutes) but the image noise in the former was considerably lower. PET measurements performed by defining "hot spot" regions of interest (ROIs) comprising 4 contiguous pixels with the highest radioactivity concentration showed a trend towards higher SUVs when the ROIs were outlined in the MVW-PC1 component than in the summed images. Time activity curves derived from "50% cut-off" ROIs based on an isocontour function whereby the pixels with SUVs between 50 to 100% of the highest radioactivity concentration were delineated, showed a significant decrease of the SUVs in normal adrenal glands and in adrenocortical adenomas after cortisone treatment.

CONCLUSION

In addition to the clear decrease in image noise and the improved contrast between different structures with MVW-PCA, the results indicate that the definition of ROIs may be more accurate and precise in MVW-PC1 images than in conventional summed images. This might improve the precision of PET measurements, for instance in therapy monitoring as well as for delineation of the tumour in radiation therapy planning.

Performance of principal component analysis and independent component analysis with respect to signal extraction from noisy positron emission tomography data - a study on computer simulated images

Multivariate image analysis tools are used for analyzing dynamic or multidimensional Positron Emission Tomography, PET data with the aim of noise reduction, dimension reduction and signal separation. Principal Component Analysis is one of the most commonly used multivariate image analysis tools, applied on dynamic PET data. Independent Component Analysis is another multivariate image analysis tool used to extract and separate signals. Because of the presence of high and variable noise levels and correlation in the different PET images which may confound the multivariate analysis, it is essential to explore and investigate different types of pre-normalization (transformation) methods that need to be applied, prior to application of these tools. In this study, we explored the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to extract signals and reduce noise, thereby increasing the Signal to Noise Ratio (SNR) in a dynamic sequence of PET images, where the features of the noise are different compared with some other medical imaging techniques. Applications on computer simulated PET images were explored and compared. Application of PCA generated relatively similar results, with some minor differences, on the images with different noise characteristics. However, clear differences were seen with respect to the type of pre-normalization. ICA on images normalized using two types of normalization methods also seemed to perform relatively well but did not reach the improvement in SNR as PCA. Furthermore ICA seems to have a tendency under some conditions to shift over information from IC1 to other independent components and to be more sensitive to the level of noise. PCA is a more stable technique than ICA and creates better results both qualitatively and quantitatively in the simulated PET images. PCA can extract the signals from the noise rather well and is not sensitive to type of noise, magnitude and correlation, when the input data are correctly handled by a proper pre-normalization. It is important to note that PCA as inherently a method to separate signal information into different components could still generate PC1 images with improved SNR as compared to mean images.

A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies—Clinical brain studies using [11C]-GR205171, [11C]-l-deuterium-deprenyl, [11C]-5-Hydroxy-l-Tryptophan, [11C]-l-DOPA and Pittsburgh Compound-B

Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM

Background

Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone.

Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT.

Methods

Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images.

Results

The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images.

Conclusion

The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.