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

Statistical analysis of positron emission tomography data

Positron emission tomography (PET) is a noninvasive medical imaging tool that produces sequences of images describing the distribution of radiotracers in the object. PET images can be processed to evaluate functional, biochemical, and physiological parameters of interest in human body. However, images generated by PET are generally noisy, thereby complicating their geometric interpretation and affecting the precision. The use of physical models to simulate the performance of PET scanners is well established. Such techniques are particularly useful at the design stage as they allow alternative specifications to be examined. When a scanner is installed and begins to be used operationally, its actual performance may deviate somewhat from the predictions made at the design stage. Thus it is recommended that routine quality assurance (QA) measurements could be used to provide an operational understanding of scanning properties. While QA data are primarily used to evaluate sensitivity and bias patterns, there is a possibility to also make use of such data sets for a more refined understanding of the 3-D scanning properties. Therefore, a comprehensive understanding of the noise characteristics in PET images could lead to improvements in clinical decision making. The main goals of this thesis are to develop model-based approaches for describing and evaluating the statistical properties of noise and a practical approach for simulation of an operational PET scanner. We began with the empirical analysis of statistical characteristics—bias, variance and correlation patterns in a series of operational scanning data. A multiplicative Gamma model had been developed for representing the structure of reconstructed PET data. The novel iteratively re-weighted least squares (IRLS) techniques were proposed for the model fitting. These included the use of a Gamma-based probability transform for normalising residuals, which could be used for model diagnostics. Building on the Gamma based modelling and probability transformation, we developed a 3-D spatial autoregressive (SAR) model to represent the 3-D spatial auto-covariance structure within the normalised data. Auto-regressive coefficients were also estimated based on the minimisation of difference between 3-D auto-correlations calculated from the normalised data and model. Both traditional filtered back-projection (FBP) and expectation-maximisation (EM) reconstructions were considered. Numerical simulation studies were carried out to evaluate the performance of the above models. The proposed models led to a very trivial process for simulation of the scanner—one that can be implemented in R. This provided a very practical mechanism to be routinely used in clinical practice—assessing error characteristics associated with quantified PET measures. Moreover, this fast and simplified approach has a potential usage in enhancing the quality of inferences produced from operational clinical PET scanners

The effect of image reconstruction algorithms in positron emission tomography on the expression of characteristic metabolic brain network for Parkinson’s disease

Namen: Značilen presnovni možganski vzorec pri parkinsonovi bolezni (angleško: Parkinson’s Disease Related Pattern - PDRP) je vzorec možganske aktivnosti, ki je specifičen za parkinsonovo bolezen (PB). Določimo ga s statistično analizo slik možganov bolnikov s PB, posnetih s 18F-fluorodeoksiglukozo (FDG) in pozitronsko emisijsko tomografijo (PET). PDRP je slikovni biološki označevalec PB. Izraženost PDRP lahko izračunamo iz slik FDG PET za vsakega preiskovanca posebej. V raziskavi nas je zanimalo, ali vrsta rekonstrukcijskega algoritma slik FDG PET vpliva na izraženost PDRP. Zasnova raziskave, opis metod in preiskovancev: Pri slovenski skupini 40 bolnikov s PB, 40 zdravih preiskovancev (ZP), ter 25 bolnikov z atipičnim parkinsonizmom (AP) smo s kamero Biograph mCT na Kliniki za nuklearno medicino Univerzitetnega kliničnega centra Ljubljana (UKCL) posneli FDG PET možganov. Ameriška skupina 20 bolnikov s PB in 7 ZP je slikanje FDG PET možganov s kamero GE Advance opravila na The Feinstein Institute for Medical Research (FIMR). Slike 20 slovenskih bolnikov s PB in 20 ZP smo uporabili za določitev slovenskega PDRP(SLOV) z multivariatno statistično analizo SSM/PCA (angleško: Scaled Subprofile Model/Principal Component Analysis). Ostale slike smo uporabili za validacijo vzorca, tako da smo preverili, ali izraženost PDRP(SLOV) zanesljivo razlikuje med skupinami preiskovancev, ter ali izraženost PDRP(SLOV) in izvirnega PDRP(FIMR) dobro korelirata. Nato smo raziskali, kakšen vpliv imajo različni rekonstrukcijski algoritmi slik PET na izraženost PDRP(SLOV) in PDRP(FIMR). Proučili smo naslednje rekonstrukcijske algoritme: analitične FBP (angleško: Filtered Backprojection), FBP+TOF (TOF – angleško: Time Of Flight), 3DRP (angleško: 3D Reprojection) in FORE-FBP (FORE – angleško: Fourier rebinning), ter iterativne OSEM (angleško: Ordered Subset Expectation Maximization), OSEM+TOF, OSEM+PSF (PSF – angleško: Point Spread Function), OSEM+PSF+TOF in FORE-Iterative. Rezultati: Določili smo presnovni možganski vzorec PDRP(SLOV), ki se kaže z relativno povišano presnovo v palidumu, putamnu, talamusu, možganskem deblu, malih možganih in senzomotorični skorji, povezano z relativno znižano presnovo v posteriorno parietalni, okcipitalni in frontalni skorji. Z analizo izraženosti PDRP(SLOV) lahko razlikujemo med bolniki s PB in ZP oziroma bolniki z AP (p<0,001). S korelacijo izraženosti PDRP(SLOV) in PDRP(FIMR) (r=0,977, p<0,001) smo pokazali zelo dobro primerljivost med obema mrežnima vzorcema. Potrdili smo, da izraženost PDRP dobro korelira med referenčnim in ostalimi rekonstrukcijskimi algoritmi (r&#88050,993, p<0,001). Zaključki: Z raziskavo smo določili PDRP(SLOV) in pokazali, da njegova izraženost dobro razlikuje med bolniki s PB in ZP ter bolniki z AP in da je vzorec dobro primerljiv z izvirnim PDRP(FIMR). Potrdili smo, da različne vrste rekonstrukcijskih algoritmov slik PET nimajo pomembnega vpliva na izraženost PDRP. Rezultati raziskave bodo pripomogli k širši klinični in raziskovalni uporabi tega slikovnega biološkega označevalca PB v svetovnem merilu.Aim: Parkinson’s disease related pattern (PDRP) is a metabolic brain network characteristic for Parkinson\u27s disease (PD). It is identified with statistical analysis of PD patients’ brain images acquired with 18F-fluorodeoxyglucose (FDG) and positron emission tomography (PET). PDRP is an imaging biomarker of disease process in PD. PDRP expression can be determined prospectively for each individual patient. The aim of our study was to explore whether a type of FDG PET image reconstruction algorithm affects the PDRP expression. Study design, methods and participants: Brain scans of a Slovenian cohort of 40 PD patients, 40 healthy control (HC) participants and 25 patients with atypical parkinsonism (AP) were acquired with FDG PET at Department of nuclear medicine of University Medical Centre Ljubljana (UMCL), using Biograph mCT scanner. An American cohort of 20 PD patients and 7 HC subjects was scanned at The Feinstein Institute for Medical Research (FIMR) using GE Advance camera. Images of 20 Slovenian PD patients and 20 HC were used to identify a Slovenian PDRP(SLOV) with Scaled Subprofile Model/Principal Component Analysis (SSM/PCA). Other images were used for PDRP(SLOV) validation, performed by testing discrimination between subject groups based on the expression of PDRP(SLOV) and correlation between expressions of PDRP(SLOV) and PDRP(FIMR). Afterwards we explored the effect of various PET image reconstruction algorithms on the expression of PDRP(SLOV) and PDRP(FIMR). We studied the following reconstruction algorithms: analytical FBP (Filtered Backprojection), FBP+TOF (TOF = Time of flight), 3DRP (3D Reprojection), FORE-FBP (FORE = Fourier rebinning) and iterative OSEM (Ordered Subset Expectation Maximization), OSEM+TOF, OSEM+PSF (PSF = Point Spread Function), OSEM+PSF+TOF, FORE-Iterative. Results: We determined metabolic brain network PDRP(SLOV), characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, cerebellum and sensory-motor cortex, associated with relative hypometabolism in posterior parietal, occipital and frontal cortex. PDRP(SLOV) discriminates PD, AP and HC subjects (p<0.001). PDRP(SLOV) and PDRP(FIMR) expression correlation (r=0.977, p<0.001) implies very good similarity between the brain networks. We confirmed significant correlation of PDRP expression between the reference and other reconstruction algorithms (r&#88050.993, p<0.0001). Conclusions: We identified PDRP(SLOV) and showed that its expression reliably discriminated PD patients from HC and AP subjects. PDRP(SLOV) has good similarity to the original PDRP(FIMR). We confirmed that different types of PET reconstruction algorithms have no significant impact on the expression of PDRP. Our work will contribute to implementation of this imaging biomarker in clinical and research applications worldwide

Development and assessment of estimate methods for internal dosimetry using PET/CT

The aim of this thesis was to assess and develop internal dose calculations methods in diagnostic and therapeutic nuclear medicine procedures to patients undergone PET/CT explorations. Towards this objective, the accuracy and precision of different classical methods commonly used to estimate internal dosimetry were investigated. Biodistribution studies were used in order to compare these methods. The main study aspects included region-of-interest (ROI) delineation methods, reconstruction algorithms, scatter correction and radiopharmaceutical's biokinetic. Optimization of internal dosimetry in this thesis was completed with the development of a Monte Carlo (MC) technique for estimating the patient-specific PET/CT dosimetry. The development of a mathematical model using MC techniques allowed us to have a gold standard to which compare classical techniques and study the aspects discussed previously. It was observed that effective dose (ED) estimations were sensitive to whichever delineation ROI method was applied. Furthermore, it was perceived that the biokinetics of the radioligand also influences in the ED estimation. On the other hand, similar quantitative accuracy was found regarding image reconstruction (FBP and OSEM) and scatter correction methods studied (FSC and SSC). Analysis of the impact of inter- and intra-operator variability in dose estimations revealed higher reproducibility in 3D methods in comparison with 2D planar method. The last one, showed the highest interoperator variability, which implies an overestimation of the ED. In this dissertation, specific routines were developed to be applied with the MC code PENELOPE/penEasy to perform individualized internal dosimetry estimations. Voxel-level absorbed dose maps which include self- and cross-irradiation doses were generated from the morphological and functional patient images. Further parameters such as cumulative organ dose, maximum and minimum voxel organ values, volume of the organ and dose-volume histograms of interest were reported. The model implemented was applied to a theoretical study using simulated PET images of a voxelized Zubal phantom. The results were benchmarked with the ones obtained using the OLINDA/EXM software. The comparison was in good agreement for those organs were both phantoms considered (Zubal and the reference one in OLINDA/EXM) were close. Undoubtedly, the implementation of a patient-specific internal dosimetry method not only leads to an improvement in diagnostic examinations where the risk could be quantified, but also NM therapy could become more effective in terms that patients receiving an optimal care.L'objectiu d'aquesta tesi va ser avaluar i desenvolupar mètodes de càlcul de dosis interna en procediments de diagnòstic i terapèutics de medicina nuclear per a pacients sotmesos a exploracions PET / TC. Amb aquest objectiu, es va investigar l'exactitud i la precisió dels diferents mètodes clàssics utilitzats habitualment per estimar la dosimetria interna. Es van utilitzar estudis de biodistribució per comparar aquests mètodes. Els principals aspectes d'estudi incloïen mètodes de delimitació de la regió d'interès (ROI), algoritmes de reconstrucció, correcció de dispersió i biocinètiques de radiofàrmacs. L'optimització de la dosimetria interna en aquesta tesi es va completar amb el desenvolupament d'una tècnica de Monte Carlo (MC) per a estimar la dosimetria PET / TC específica del pacient. El desenvolupament d'un model matemàtic amb tècniques de MC ens va permetre tenir una referència amb la que comparar les tècniques clàssiques i estudiar els aspectes descrits anteriorment. Es va observar que les estimacions de la dosi efectiva (DE) eren sensibles a qualsevol mètode de delimitació de la ROI aplicada. A més a més, es va percebre que la biocinètica del radiolligand també influeix en l'estimació de la DE. D'altra banda, es va trobar una exactitud quantitativament similar pel que fa a la reconstrucció d'imatges (FBP i OSEM) i els mètodes de correcció de dispersió estudiats (FSC i SSC). L'anàlisi de l'impacte de la variabilitat entre operadors i intra-operadors en les estimacions de dosis va mostrar una major reproductibilitat en els mètodes 3D en comparació amb el mètode planar 2D. Aquest últim, va mostrar la màxima variabilitat entre operadors, la qual cosa implica una sobreestimació de la DE. En aquesta tesi, es van desenvolupar rutines específiques per aplicar-les amb el codi MC PENELOPE / penEasy per a realitzar estimacions de dosimetria interna individualitzades. Es van generar mapes de dosis absorbida a nivell de voxel que incloïen dosis d? autoirradiació i irradiació creuada a partir de les imatges morfològiques i funcionals del pacient. Es van reportar altres paràmetres d?interès com la dosi d'òrgan acumulada, els valors màxims i mínims de l'òrgan i del vòxel, el volum de l'òrgan i els histogrames de dosi-volum. El model implementat es va aplicar a un estudi teòric mitjançant imatges simulades de PET d'un maniquí de Zubal voxelitzat. Els resultats es van comparar amb els obtinguts mitjançant el programa OLINDA / EXM. Es va observar un bon acord per a aquells òrgans semblants entre el maniquí de Zubal i el maniquí de referència del software OLINDA/EXM. Sens dubte, la implementació d'un mètode de dosimetria interna específic per al pacient no només condueix a una millora en les exploracions de diagnòstic on es pot quantificar el risc d?irradiació, sinó que la teràpia amb medicina nuclear podria ser més eficaç en termes que els pacients rebin un tractament òptim.Postprint (published version

Motion-Compensated Image Reconstruction for Magnetic Resonance (MR) Imaging and for Simultaneous Positron Emission Tomography/MR Imaging

In this work, novel algorithms for 4D (3D + respiratory) and 5D (3D + respiratory + cardiac) motion-compensated (MoCo) magnetic resonance (MR) and positron emission tomography (PET) image reconstruction were developed. The focus of all methods was set on short MR acquisition times. Therefore, respiratory and cardiac patient motion were estimated on the basis of strongly undersampled radial MR data employing joint motion estimation and MR image reconstruction. In case of simultaneous PET/MR acquisitions, motion information derived from MR was incorporated into the MoCo PET reconstruction. 4D respiratory MoCo MR image reconstructions with acquisition times of 40 s achieved an image quality comparable to standard motion handling approaches, which require one order of magnitude longer MR acquisition times. Respiratory MoCo PET images using 1 min of the MR acquisition time for motion estimation revealed improved PET image quality and quantification accuracy when compared to standard reconstruction methods. Additional compensation of cardiac motion resulted in increased image sharpness of MR and PET images in the heart region and enabled time-resolved 5D imaging allowing for reconstruction of any arbitrary combination of respiratory and cardiac motion phases. The proposed methods for MoCo image reconstruction may be integrated into clinical routine, reducing MR acquisition times for improved patient comfort and increasing the diagnostic value of MR and simultaneous PET/MR examinations of the thorax and abdomen

Improved brain PET quantification using partial volume correction techniques

Positron emission tomography (PET) suffers from a degradation in quantitative accuracy due to a phenomenon known as the partial volume effect (PVE). The effects are due to the limited spatial resolution of the scanner. Methods that correct for PVEs are known as partial volume correction (PVC) techniques and are either data-driven or make use of anatomical information from other modalities such as magnetic resonance (MR) imaging. This thesis reports investigations into PVC techniques for improving the quantification of brain amyloid PET tracers. These tracers image amyloid plaque aggregation in-vivo, which is a pathological hallmark of Alzheimer’s disease. An extension to existing anatomy-based PVC methods is reported. Region-based voxelwise (RBV) correction has been shown to reduce PVE-induced regional bias and variance when compared to commonly applied PVC techniques. This has been proven in phantom studies and observed in clinical data. In addition, RBV has been used to demonstrate that white matter variability exists in two different amyloid tracers. This finding has implications for the application of PVC in amyloid imaging and also how scans should be normalised. Alternative reference regions were investigated in two amyloid PET tracers. The brain stem, in combination with PVC, was found to result in the strongest agreement between tracers. Anatomy-based PVC techniques rely on parcellations of structural images. These parcellations are not necessarily representative of the PET data. A further extension to RBV is proposed which iteratively modifies the parcellations to find an optimal PVC in terms of the observed PET data. This novel technique reduces quantification errors due to PET-MR mismatch and has the potential to provide an additional parameter of ‘functional volume change’ in longitudinal studies

Application of novel corrections for quantification of 123I SPECT

Introduction: The quantification of clinical images provides a useful adjunct to visual assessment in the differentiation of disease processes. In nuclear medicine imaging, the accurate quantification of Single Photon Emission Computed Tomography (SPECT) data is challenging due to limited spatial resolution and the corrections required for photon attenuation and scatter. Specific radionuclides used in SPECT imaging, such as Iodine-123 (123I), pose additional challenges to quantification due to their complex decay schemes. 123I has a predominantly low-energy photon emission of 159keV. However, 123I also has high-energy emissions which, due to septal penetration, are detected within the imaging window. Consequently, absolute quantification of 123I SPECT is not current clinical practice and remains a specialist task. A novel reconstruction correction scheme has been developed by Hermes Medical Solutions which incorporates Monte Carlo simulation of photon interactions in both the patient and the detector system. This Collimator and Detector Response Modelling (CDRM) algorithm has the potential to enhance image quality and, therefore, the quantitative accuracy of 123I SPECT studies. This thesis aims to optimise 123I SPECT quantification using advanced reconstruction algorithms and, furthermore, to assess the clinical applications of these optimised techniques. Method: With the ultimate aim of optimising quantification of 123I SPECT, work was undertaken to assess SPECT spatial uniformity, spatial resolution, contrast recovery, noise and scatter suppression. This work was used to specify the optimum collimator and reconstruction parameters required for accurate quantification. Using these parameters, absolute quantification was then assessed for accuracy with regard to neurology and oncology studies. The utility of Standardised Uptake Values (SUVs) was evaluated in 123I-DaTSCAN patient studies. Furthermore, human observer studies were used to verify the findings of the quantitative assessment. Results: Phantom studies demonstrated that Low Energy High Resolution (LEHR) collimators provide superior image quality for neurology applications where spatial resolution is essential. However, when imaging the torso, this work showed that Medium Energy General Purpose (MELP) collimators, with advanced reconstruction, can improve contrast recovery, noise characteristics and scatter suppression when compared with LEHR data. The accuracy of quantifying activity concentration for neurology studies was optimised using the novel CDRM correction scheme (measured activity concentration within +/-10% of true concentration). However, the accuracy of quantification in torso studies was shown to vary with lesion location in the Field of View (FOV). Therefore, neurology studies were identified as the best candidates for absolute quantification. In a subsequent evaluation of patient studies, measuring the mean SUV of the putamen in 123I-DaTSCAN studies marginally outperformed Hermes Medical Solutions BRASS automated analysis application with regard to the differentiation of normality. Direct quantitative assessment has the advantage that it removes the requirement for a normal database. Furthermore, the evaluation of clinical patient 123I-DaTSCAN studies by human observers demonstrated almost perfect agreement in diagnosis for the novel CDRM reconstruction correction scheme (Kappa coefficient=0.913). Image quality for the CDRM scheme rated significantly higher than current clinical practice (p-value<0.01). The torso phantom observer study suggested that optimised reconstruction of MELP data demonstrated superior image quality and lesion detectability when compared with LEHR reconstructions. Conclusions: For 123I-mIBG oncology studies, including quantification of serial studies, data should be acquired with MELP collimators and reconstructed with advanced corrections for attenuation, scatter and depth-dependent spatial resolution. However, quantification of 123I SPECT body section images for inter-patient comparison is not feasible due to variable accuracy with lesion location in the FOV. Absolute quantification of 123I-DaTSCAN studies, acquired with LEHR collimators, can be performed routinely with sufficient accuracy using the novel CDRM algorithm