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<p>The 2-D INADEQUATE experiment is a useful experiment for determining carbon structures of organic molecules known for having low signal-to-noise ratios. A non-linear optimization method for solving low-signal spectra resulting from this experiment is introduced to compensate. The method relies on the peak locations defined by the INADEQUATE experiment to create boxes around these areas and measure the signal in each. By measuring pairs of these boxes and applying penalty functions that represent a priori information, we are able to quickly and reliably solve spectra with an acquisition time under a quarter of that required by traditional methods. Examples are shown using the spectrum of sucrose. The concept of a non-uniform Fourier transform and its potential advantages are introduced. The possible application of this type of transform to the INADEQUATE experiment and the previously explained optimization program is detailed.</p> / Master of Applied Science (MASc)

<p>Our group aims to develop a dedicated PET/MR system for breast cancer imaging. In order to evaluate and optimize the performance of the PET component, Monte Carlo simulation was made to help us choose the configuration parameters for hardware design. A resolution modeling method was also proposed and implemented on the GPU device to not only improve the spatial resolution of the reconstructed images but also accelerate the reconstruction speed. The PET component is configured with a ring shape composed of LYSO+SiPM detectors. Such design is compatible to MRI, and feasible for time of flight PET. Several aspects are included to be investigated in the simulation which are geometry configuration, counting performance and image quality. From the simulation result, the system configured using 2x2x20mm3 LYSO crystal with two DOI layers and 3 detector rings results in 6.2% photon sensitivity. The Noise equivalent count rate is improved with better time resolution, the peak NEC is found to be 7886 cps with 250 ps time resolution. The system is able to achieve 2.0 mm spatial resolution which is found to be more uniform with the addition of DOI layers. With the help of TOF, the lesion is visualizable with shorter scan time than the non-TOF system. The resolution modeling method is based on the coincidence detection response function modeling and multiray projection. It is found to improve the spatial resolution uniformity and contrast recovery. At the same time it reduces the position offset and background noise. The speed and accuracy improvement for this model is also discussed.</p> / Master of Science (MSc)

Radiation therapy has a significant role in the management of cancer patients. Computed tomography (CT) has been at the forefront of radiation therapy planning due to its widespread diagnostic use and its electron density information. Magnetic resonance (MR) imaging is another proven diagnostic modality, which can achieve superior soft tissue contrast and margin delineation, relative to CT. As such it has become a valuable tool for cancer diagnoses and staging. In this study, a centre-out radial acquisition using an off-resonance reception (co-RASOR) MR sequence, sensitive to magnetic field inhomogeneity, was applied to excite a broader frequency range of spins in the vicinity of metallic seeds. The resultant images display local hyperintensities around metallic markers. These images were then reconstructed with frequency offsets to rewind these hyperintensities to the geometric centre to obtain positive contrast. The contrast-to-noise ratio (CNR) was measured between a fiducial and its surrounding to compare Fourier and iterative reconstruction methods for undersampled co-RASOR. The motivation was to reduce the sequence acquisition time, while preserving sufficient CNR and resolution. For single slices, acquisition was 2.8 sec and multi-slice acquisition could acquire more than 50 slices in 73 sec, by reducing the acquired data by a factor of 8. This effectively encodes acquisition to 1.4 sec/slice. The noise present in undersampled images decreased significantly using iterative reconstruction methods, but a total variation based penalty better preserved the edges. Further extensions to the reconstruction method applied frequency-based filters that could isolate signals from different metallic compounds. The local hyperintensities rewind using opposite signed frequency offsets for diamagnetic and paramagnetic seeds. This allowed individual visualization of a low dose rate (LDR) brachytherapy seed and a gold fiducial marker. Phantom validation showed that each seed contains its maximal CNR in opposing frequency regions. The relative difference between global and local maxima in each frequency band ranged from 1.19 -- 3.73, and a single cut-off frequency was successfully applied for each acquisition plane. Image guidance systems rely on the position of these fiducial markers to compare daily setup images with CT and MR simulations. Phantom experiments and porcine tissue samples were used to assess the minimum separation of fiducials, geometric accuracy, and random errors associated with using the co-RASOR sequence. co-RASOR images were able to resolve fiducials separated by 0.5 - 1 cm, depending on image resolution. No systematic biases were observed by comparing co-RASOR to CT using rigid body registrations. The standard deviation of the systematic errors were \textless 0.5 mm between MR and CT registrations, and \textless 0.4 mm between MR scans without CT. These values are smaller than the current total systematic uncertainties, which should be limimed to <3 mm. The methods presented here can aid in understanding the trade-offs between acquisition speed and signal properties, differentiating cases where brachytherapy seeds are used in combination with fiducial markers for external beam boost, and aid in co-registration of multimodality imaging. / Thesis / Doctor of Philosophy (PhD)

​​Electrical resistance tomography (ERT) and electrical impedance tomography (EIT) are imaging techniques reconstructing the internal conductivity distribution image of an object based on voltage measurements at the periphery of the object with a given applied current. ERT uses a direct current (DC), while EIT uses an alternating current (AC). However, for low frequencies both ERT and EIT have the same governing equation, which is often referred to as a non-linear and ill-posed inverse problem. Both methods have diverse applications in biology, biomedicine, and industry. ​This master’s degree project aims to create a low-cost imaging system for the ERT, which is the main focus, as well as for the EIT. The project includes three main components: 1) Simulations and reconstructions using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software), 2) Developing an experimental workbench (a measurement system), and 3) developing a machine learning model for the ERT. ​EIDORS was used to simulate and reconstruct ERT and EIT images. It was also used to generate training data for the machine learning model to be developed. ​The measurement system includes a circular water tank with electrodes, power supplies, and measurement units. Tanks with 8 and 16 electrodes were designed using 3D printers. Initially, aluminium electrodes provided inconsistent measurements due to magnetization and electrolysis, later replaced by graphite electrodes, offering better but not yet accurate enough results. ​After implementing reconstruction algorithms in EIDORS, a machine learning model was developed for ERT. It involved: 1) generating a training set, containing over 5000 simulated data points, 2) preprocessing the generated data set which included PCA dimensionality reduction, 3) and lastly a linear regression model developed. The model struggled with small object detection and occasional inconclusive results, likely due to limited training dataset diversity. Additionally, images of two cases were reconstructed using EIT and comparing it to ERT it can be concluded that EIT performs better than ERT. ​

High-resolution micro computed tomography (micro-CT) offers 3D image resolution of 1 um for non-destructive evaluation of various samples. However, the micro-CT performance is limited by several factors. Primarily, scan time is extremely long, and sample dimension is restricted by the x-ray beam and the detector size. The latter is the cause for the well-known interior problem. Recent advancement in image reconstruction, spurred by the advent of compressed sensing (CS) theory in 2006 and interior tomography theory since 2007, offers great reduction in the number of views and an increment in the volume of samples, while maintaining reconstruction accuracy. Yet, for a number of reasons, traditional filtered back-projection based reconstruction methods remain the de facto standard on all manufactured scanners. This work demonstrates that CS based global and interior reconstruction methods can enhance the imaging capability of micro-CT scanners. First, CS based few-view reconstruction methods have been developed for use with data from a real micro-CT scanner. By achieving high quality few-view reconstruction, the new approach is able to reduce micro-CT scan time to up to 1/8th of the time required by the conventional protocol. Next, two new reconstruction techniques have been developed that allow accurate interior reconstruction using just a limited number of global scout views as additional information. The techniques represent a significant progress relative to the previous methods that assume a fully sampled global scan. Of the two methods, the second method uses CS techniques and does not place any restrictions on scanning geometry. Finally, analytic and iterative reconstruction methods have been developed for enlargement of the field of view for the interior scan with a small detector. The idea is that truncated projections are acquired in an offset detector geometry, and the reconstruction procedure is performed through the use of a weighting function / weighted iteration updates, and projection completion. The CS based reconstruction yields the highest image quality in the numerical simulation. Yet, some limitations of the CS based techniques are observed in case of real data with various imperfect properties. In all the studies, physical micro-CT phantoms have been designed and utilized for performance analysis. Also, important guidelines are suggested for future improvements. / Ph. D.

No / We propose a hybrid scheme to implement an object driven, block based algorithm to achieve low bit-rate compression of stereo image pairs. The algorithm effectively combines the simplicity and adaptability of the existing block based stereo image compression techniques with an edge/contour based object extraction technique to determine appropriate compression strategy for various areas of the right image. Unlike the existing object-based coding such as MPEG-4 developed in the video compression community, the proposed scheme does not require any additional shape coding. Instead, the arbitrary shape is reconstructed by the matching object inside the left frame, which has been encoded by standard JPEG algorithm and hence made available at the decoding end for those shapes in right frames. Yet the shape reconstruction for right objects incurs no distortion due to the unique correlation between left and right frames inside stereo image pairs and the nature of the proposed hybrid scheme. Extensive experiments carried out support that significant improvements of up to 20% in compression ratios are achieved by the proposed algorithm in comparison with the existing block-based technique, while the reconstructed image quality is maintained at a competitive level in terms of both PSNR values and visual inspections

<p dir="ltr">Computed tomography (CT) imaging is widely used in industrial and medical appli- cations for non-destructive visualization of internal sample morphology. Traditional CT reconstruction methods use projection images from a single rotation axis with a predefined set of orientations. However, for objects containing dense materials like metal, the use of a single rotation axis may leave some regions of the object obscured by the metal, even though projections from other rotation axes (or poses) might contain complementary information that would better resolve these obscured regions. Additionally, for certain CT applications, it is also desirable to reduce data acquisition time with an adaptive orientation selection strategy. </p><p dir="ltr">In this thesis, we propose advanced algorithms to improve reconstruction quality and reduce data acquisition time by efficiently leveraging the complementary information from the different orientations and rotation axes of a single object.</p><p dir="ltr">In the first portion of this thesis, we propose Multi-pose Fusion, an algorithm for reducing CT reconstruction artifacts by integrating CT measurements from multiple poses of a single object. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug- and-play, as a framework for integrating projection data from different poses. We present experimental results using both synthetic and measured CT data, and demonstrate that the Multi-pose Fusion reconstruction method is effective in reducing artifacts and improving image quality.</p><p dir="ltr">In the second portion of the this thesis, we propose an adaptive orientation selection method for the application of neutron computed tomography (nCT), in which the information from previously acquired measurements is used to decide the next measurement orientation. Using simulated and experimental data, we demonstrate that our method produces high- quality reconstructions using significantly fewer total measurements than the conventional approach.</p>

This thesis presents three distinct methodologies aimed at exploring pivotal aspects within the domain of fiber optics and piezoelectric materials. The first approach offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an Autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this thesis entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works. The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.

Traditionally, medical imaging techniques such as PET (positron emission tomography) and SPECT (single-photon emission computed tomography) have relied on mechanical collimators to detect the sources of photons. This limits the image's resolution and field of view. To improve upon this, Compton cameras have emerged as a promising alternative. The principle is to measure the angle of a photon scattered in the detector, which indicates the likely sources in the form of a cone culminating in the position of the interaction. The cones from multiple events may be superimposed in order to generate an image. The object of this work is to use list-mode likelihood methods to better reconstruct the source image from the data recorded by a simulated Compton camera in the case of a solid detector volume with good spatial- and energy resolution.  The results demonstrate an improvement of image quality for reconstructions of single point source and multiple extended sources. In addition, the results indicate that our used algorithm converges for point sources. The minimum number of measured events for accurate reconstruction for different source distributions remains to be determined, and the algorithm's ability to resolve closely adjacent sources should be investigated more.