Vesna Sossi
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Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Hybrid PET/MRI scanners are becoming more common in research and clinical settings, in particular for their ability to simultaneously acquire unique functional and structural information to probe the healthy and diseased brain. Data from both modalities need to be thoroughly processed to enhance signal-to-noise ratios (SNR), and the development and optimization of analysis algorithms is required to extract meaningful physiological quantities for medical research applications. This work provides significant contributions to these fields through the development, testing, and validation of four algorithms. PET data are intrinsically noisy due to the Poisson nature of positron emission events and the relatively low fraction of events detected by scanners, so we first improve upon an existing PET denoising algorithm to overcome limitations in quantitative accuracy in precision. This proves important in task-based PET imaging, where tasks presented to a subject during scanning result in a small temporal signal change that is difficult to distinguish from noise. The developed PET denoising algorithm is thus coupled with an advanced PET detection algorithm which provides a significant improvement in the detection sensitivity of task-induced signal changes. Motivated by the success of existing PET denoising algorithms, we then develop an MRI denoising algorithm for low SNR functional MRI data, which demonstrates significant improvements in quantitative accuracy and precision of downstream modeling metrics. Finally, we propose a pattern analysis extension of an existing joint PET/MRI analysis algorithm to identify disease-related alterations to simultaneously acquired multimodal data. We identify immediate applications of each algorithm in novel medical research studies, and proof-of-concept testing on human data is performed when available.
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The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
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Targeted alpha therapies using actinium-225 (²²⁵Ac, t₁/₂ = 9.9 d) can treat advanced metastatic disease, yet insufficient ²²⁵Ac availability limits their development (63 GBq/year is produced globally via ²²⁹Th generators). This thesis describes efforts to produce ²²⁵Ac and apply multi-nuclide SPECT imaging in preclinical evaluation of ²²⁵Ac-radiopharmaceuticals. Initial ²²⁵Ac production used ᴺᵃᵗU-spallation-produced and mass-separated ion beams, producing up to 8.6 MBq of ²²⁵Ra (an ²²⁵Ac parent) and 18 MBq of ²²⁵Ac. This material helped characterize the performance of ²²⁵Ac decay chain imaging on a microSPECT/PET/CT scanner in terms of contrast recovery, spatial resolution, and noise. Larger ²²⁵Ac quantities were produced via thorium target irradiation with a 438 MeV, 72 μA proton beam for 36 hours, producing (521 ±18) MBq of ²²⁵Ac and (91 ± 14) MBq of ²²⁵Ra. These irradiations enabled ²³²Th(p,x) cross sections measurements for ²²⁵Ac, ²²⁵Ra, and ²²⁷Ac: (13.3 ± 1.2) mb, (4.2 ± 0.4) mb, and (17.7 ± 1.7) mb, respectively. Thirty-five other cross sections were measured and compared to FLUKA simulations; measured and calculated values generally agree within a factor of two. Ac separation from irradiated thorium and co-produced radioactive by-products used a thorium peroxide precipitation followed by cation exchange and extraction chromatography. Studies showed this method separates Ac from most elements, providing a directly-produced Ac product (²²⁷˒²²⁵Ac†) with measured ²²⁷Ac content of (0.15 ± 0.04)%, a hazardous long-lived (t₁/₂ = 21.8 y) impurity with prohibitively low waste disposal limits. A second, indirectly-produced ²²⁵Ra/²²⁵Ac-generator-derived Ac product (²²⁵Ac*) with ²²⁷Ac content of
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Positron emission tomography (PET) is commonly used to investigate changes within the brain due to aging and disease. Because our brain works as an integrated system where multiple brain regions work together to perform complex tasks, network pattern analyses (a subset of machine-learning methods) are often found to provide complementary, more sensitive and more robust information compared to traditional univariate analyses, especially in the field of magnetic resonance imaging (MRI). However, network pattern analyses have not been commonly used to study neurotransmitter changes using PET data. In addition, the emergence of multi-tracer imaging studies highlights the needs to develop novel joint analysis methods to extract and combine complementary information from each imaging dataset to obtain a complete picture of the complex brain states. This thesis constitutes one of the first applications of such methods in the PET field. Parkinson’s disease (PD) is the second most common neurodegenerative disorder. It has a long prodromal stage, and non-motor symptoms occur alongside or even before motor symptoms. Initially thought to affect predominantly the dopaminergic system, PD is now deemed to be associated with alterations in several other non-dopaminergic neurotransmitter systems. Such changes, specific to PD, are sometimes difficult to detect, especially in prodromal and early stages of the disease; the interactions between different disease-related mechanisms also remain largely unclear. In addition, the disease origin is unknown and there is currently no effective cure for PD. In this thesis work, we 1) explored deterministic spatial connectivity changes in the serotonergic system that are sensitive for detecting subtle changes in the prodromal and early disease stages; 2) introduced dynamic mode decomposition to extract spatio-temporal patterns of dopaminergic denervation for modelling disease progression; 3) introduced a novel joint pattern analysis approach to extract complementary information in the dopaminergic and serotonergic systems and their relationships with treatment response and treatment-induced complications. These novel methods not only lead to new understandings of PD, but also provide more sensitive and deterministic tools for the analysis of PET data in a variety of clinical applications.
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Positron emission tomography (PET) is a molecular imaging modality that allows to quantitatively assess the physiological function of tissues in-vivo. Subject motion during imaging degrades the quantitative accuracy of the data. In small animal imaging, motion is minimized by the use of anesthesia, which interferes with the normal physiology of the brain. This can be circumvented by imaging awake rodents; however, in this case correction for non-cyclic motion with rigid and deformable components is required. In the first part of the thesis, the problem of motion correction in PET imaging of unrestrained awake rodents is addressed. A novel method of iterative image reconstruction is developed that incorporates correction for non-cyclic deformable motion. Point clouds were used to represent the imaged objects in the image space, and motion was accounted by using time-dependent point coordinates. The quantitative accuracy and noise characteristics of the proposed method were quantified and compared to traditional methods by reconstructing projection data from digital and physical phantoms. A digital phantom of a freely moving mouse was constructed, and the efficacy of motion correction was tested by reconstructing the simulated coincidence data from the phantom. In the second part of the thesis, novel approaches to PET image analysis were explored. In brain PET, analysis based on the tracer kinetic modeling (KM) may not always be possible due to the complexity of the scanning protocols and inability to find a suitable reference region. Therefore, the ability of KM-independent shape and texture metrics to convey useful information on the disease state was investigated, based on an ongoing Parkinson's disease study with radiotracers that probe the dopaminergic system. The pattern of the radiotracer distribution in the striatum was characterized by computing the metrics from multiple regions of interest defined using PET and MRI images. Regression analysis showed a significant correlation between the metrics and clinical disease measures (p
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Positron Emission Tomography (PET) provides in vivo functional information about a living subject by imaging the distribution of biologically meaningful radiotracers such as 18F-fluoro-deoxy-glucose. PET data is complemented by anatomical information from an imaging modality that produces high tissue contrast such as MRI. The work presented in this thesis is a contribution to a collaboration aimed at creating an MRI compatible high-resolution small animal PET insert for simultaneous PET/MR imaging. The PET system was designed with an outer diameter of 114 mm in order to fit inside of a pre-existing 7T small animal MRI. During the design of the PET system, Monte-Carlo simulations were created to estimate the resolution and count rate performance of various iterations of the design. These simulations showed that the proposed dual-layer detector design would be effective in mitigating off-centre spatial resolution degradation, and produced resolution of ~ 1 mm full-width at half-maximum in the centre of the field of view. The effective count rate of our system was estimated to be low in comparison to other small animal PET systems due to the small solid angle subtended by the detectors. A prototype detector block was built incorporating an array of digital photon counters (DPCs) to test the suitability of DPCs as an alternative to silicon photomultipliers for our small animal PET application. Based on the characterization of energy resolution, timing resolution, and rates of count loss as a function of device settings, the most appropriate combination of device settings for our small animal PET application was identified. Once constructed, the prototype PET system was characterized in terms of spatial resolution and count rate performance. Phantom and rodent images were reconstructed using filtered back projection – 3D reprojection (FBP-3DRP) and a novel point-spread-function modelling maximum likelihood expectation maximization (PSF-MLEM) algorithm. The PSF-MLEM reconstruction algorithm was updated to remove non-uniformity artefacts caused by lack of normalization and systematic inaccuracies present in its original implementation. PSF-MLEM resulted in higher quality PET images than FBP-3DRP, resolving feature sizes of 0.7 mm in a resolution phantom and showing contrast between the cortex and ganglia in rodent brains.
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Manganese is a magnetic resonance imaging (MRI) contrast agent for small animals that can provide a blood-flow-independent measure of neuronal activation. Established Mn MRI methods have limited ability to measure concentrations of Mn or details of its distribution in vivo, which limits their experimental power. Positron emission tomography (PET) can measure quantitative distributions in vivo in small animals of positron-emitting radionuclides such as Mn-52, although has poorer spatial resolution than MRI. Autoradiography (AR) can also measure quantitative distributions of Mn-52, in ex vivo brain tissue, with spatial resolution similar to MRI.This work has three primary goals: to develop and characterize Mn-52 as a radionuclide for PET in phantoms and in small animals, to develop a quantitative MRI method for measuring Mn concentration in the brain of small animals, and to validate the MRI results by comparisons with AR and PET.Mn-52 was produced by proton irradiation of natural Cr foil, isolated by column chromatography, and used as a PET tracer for the first time in phantoms and in vivo in rats. Mn-52 phantom image quality metrics were similar to F-18, an established PET radionuclide. After systemic administration in rats, Mn-52 accumulation was seen in bones, but little was seen in the brain, due to the blood-brain barrier. Direct injection into the lateral ventricle effectively delivers Mn-52 throughout the rat brain. Mn-52 AR images were acquired and used for comparison with MRI.MRI R1 relaxation rate maps of rat brain were acquired using a radiofrequency field strength independent inversion recovery Look-Locker sequence, and used to generate relaxation rate change and Mn concentration maps after Mn administration. These maps showed excellent quantitative agreement with PET and AR images of the same animal, confirming that MR R1 change accurately measures Mn concentration in rat brain in the range 0 to 0.1 mM, in the absence of other sources of R1 change. However, at some point above this concentration, measured R1 becomes inaccurate. Accordingly, Mn concentration mapping with MRI is a potentially useful tool to improve the experimental power of Mn-uptake imaging to assess neuronal activation.
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Modern positron emission tomography (PET) scanners have a large number of detectorcrystals which corresponds to an even larger number of lines of response (LOR)addressing the coincidence events. This increasing complexity makes the imagereconstruction much more challenging especially in dynamic imaging where theacquired number of counts per frame or per LOR is much less as compared to that in thestatic case. The low statistical quality of the data thus degrades the quantitative accuracyof the images. Moreover, the increasing number of LORs and dynamic frames requiresa longer computational time and larger data storage for the image reconstruction task.A dual reconstruction scheme, a novel scatter calibration, and a practical scatter andrandoms approximation methods were developed in this work. These methods havebeen validated using phantom, non-human primate, and human studies and have beendemonstrated to improve the quantification accuracy of the images, to accelerate theimage formation task, and to reduce the data storage requirement for dynamic brainimaging in high resolution PET.In conclusion, these studies contribute to increasing the accuracy and to decreasing thecomputational burden for dynamic high resolution quantitative PET imaging. Theproposed methods are modular and can be applied to any PET scanners with theexception of the dual reconstruction scheme which requires list-mode acquisitioncapability. These methods are particularly beneficial for high resolution scanners whichhave a large number of LORs, such as the high resolution research tomograph (HRRT).
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
A single positron emission tomography (PET) scan can be used to detect voxel-level transient dopamine release in response to mid-scan task or stimulus. A standard approach uses F-test (significance) thresholding followed by a cluster size threshold to limit false positives. The F statistic is computed from two neurophysiological kinetic models, one able to handle dopamine-induced perturbations to the time-varying radiotracer concentration, and the other unable to do so. Through simulation we first demonstrate that extensive denoising of the dynamic PET images is required for this method to have high detection sensitivity, though this often leads to a large cluster size threshold—limiting the detection of smaller cluster sizes—and poorer parametric accuracy. Our simulations also show that voxels near the peripheries of clusters are often rejected—becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We then suggest a novel Monte Carlo method that incorporates these two observations into a cost function, allowing erroneously-rejected voxels to be accepted under specified criteria. In simulations, the proposed method boosts sensitivity by up to 77% while preserving cluster size threshold, or up to 180% when optimizing for detection sensitivity. A further parametric-based voxelwise thresholding is then suggested to better estimate the dopamine release dynamics in detected clusters. Finally, we apply the Monte Carlo method coupled with the parametric thresholding approach to a pilot scan from a human gambling study, where additional parametrically-unique clusters are detected as compared to the current best method.
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PET and SPECT are nuclear medicine imaging techniques that allow for the study of physiological processes in vivo. These techniques allow to assess the dopaminergic system in subjects with Parkinson's disease (PD), which is the system most severely affected by the disease. Parkinson’s Progression Markers Initiative is a multicenter, longitudinal study aimed at identifying novel biomarkers of PD progression. This study utilizes brain SPECT/PET imaging to investigate the dopaminergic system, by examining the distribution of the dopamine transporter (DaT) or the vesicular monoamine transporter 2 (VMAT) in the striatum. Several imaging metrics can be used to quantify the dopaminergic tracer binding in the striatum. These metrics are typically calculated on regions of interest (ROIs) that require either manual placement or coregistration with MR structural images. In the first part of this work, an automated approach to quantifying dopaminergic tracer binding is presented; the method consists of a new metric, SI, evaluated over a bounding box that is automatically placed on the SPECT/PET images. In order to validate this metric, the correlation is computed between the SI values and the motor scores of PD subjects from the PPMI database. We find that sum intensity achieves correlations as strong as the ones obtained using conventional approaches such as the putamen binding ratio, evaluated on manually-placed ROIs, but using a simplified and operator-independent approach.The second part of this work focuses on predicting the rate of PD progression over the four years during which the PD subjects were enrolled in the PPMI study. Two methods of quantifying disease progression are considered. The first approach uses imaging features collected at year-0 of the study to predict the decline in the putamen binding ratios over the next four years. The model achieves a prediction error of 13% for the better side of the putamen, which is comparable to the test-retest reproducibility of this metric. The second approach uses imaging and clinical features at year-0 to predict the clinical outcome (quantified by year-4 motor and cognitive scores). Novel combinations of clinical and imaging features that are predictors of disease severity are identified.
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Ga-67 decays with emission of gammas of 93 (42%), 184 (21%), 300 (17%) and 393 (5%) keV. The energy range of the gammas of Ga-67 may impact the quality of the SPECT imaging data in a collimator type-dependent way. The MILABs VECTor is a preclinical SPECT scanner utilizing multi-pinhole collimators (MPC). Several MPCs can be mounted on the camera. A General Purpose Multi-Pinhole (GPMP), a High Energy Clustered Multi-Pinhole (HECMP), and a High Sensitivity Multi-Pinhole (HSMP) collimator were used for this work. The main objective of this thesis is the performance characterization of the VECTor and MPCs in imaging Ga-67. The sensitivity profiles of the MPCs, and uniformity and contrast metrics in the acquired images were evaluated for this purpose. Other objectives include evaluation of the attenuation and scatter correction, and finally optimization for Ga-67 imaging which includes proper selection of a MPC and the photopeaks for data reconstruction. Our results showed that the peak sensitivity of the GPMP, HECMP, and HSMP collimators at (93, 184, 300, 393 keV) is respectively (0.2%, 0.3%, 0.3%, 0.4%), (0.4%, 0.3%, 0.2%, 0.2%), and (1.8%, 1.6%, 1.0%, 0.8%). Ga-67 images have the best uniformity when the HECMP collimator is used for data acquisition. The integral uniformity of the images with the GPMP, HECMP, and HSMP collimators at (93, 184, 300, 393 keV) is respectively (24%, 26%, 62%, 83%), (17%, 18%, 22%, 38%), and (49%, 45%, 42%, 56%). The best contrast at 93 and 184 keV is obtained using the GPMP collimator, and at 300 and 393 keV is obtained using the HECMP collimator. The attenuation and scatter correction methods are performing well for Ga-67 data. Finally, only the first two photopeaks should be used with the GPMP and the HSMP collimators, and all the four photopeaks should be used with the HECMP collimator for the image reconstruction. In addition, GPMP collimator should be the collimator to be used for Ga-67 studies since the images with this collimator have the best contrast at 93 and 184 keV and for object sizes
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Positron emission tomography (PET) produces images of functional processes of the body in-vivo. The analysis of PET data for research purposes traditionally involves kinetic modeling of the concentration of the radiotracer over time within a region of interest (ROI) in the body to derive parameters related to the uptake/binding of the radiotracer in that region. PET imaging is commonly used to study Parkinson's disease (PD), where loss of motor function is caused by the progressive death of neurons in the brain that produce the neurotransmitter dopamine. In PD, both the kinetic and the spatial distribution of the tracer change due to the disease: the posterior parts of the striatum (in particular in the putamen) are affected before the anterior parts. The purpose of this dissertation is to develop a novel analysis method for PET data that uses the spatial characteristics of the radiotracer's distribution within anatomically-defined ROIs to extract additional information about pathological states. The proposed analysis method is based on mathematical 3D shape descriptors that are invariant to translation, scaling, and rotation, called 3D moment invariants (3DMIs). The variable of interest in this case is not only the radiotracer's uptake rate constant or binding potential, but also the 3D spatial shape and distribution of the radioactivity within the ROI. This dissertation shows that 3DMIs were able to successfully quantify differences in the spatial distribution of PET radiotracer images between healthy controls and PD subjects. 3DMI values were found to correlate with a clinical measure of disease severity in all anatomical regions studied here (putamen, caudate and ventral striatum), as opposed to kinetic parameters which only showed significant correlation to clinically-assessed PD severity in the putamen. Levodopa-induced changes in spatial patterns of dopamine release (as measured using 3DMIs) were found to be significantly correlated with PD severity in all ROIs studied here. These findings suggest that quantitative studies of a radiotracer's spatial distribution can be complementary to kinetic modeling in extracting information about pathological states from PET data and have the potential to contribute novel information in PET neuroimaging studies.
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