Endorectal Digital Prostate Tomosynthesis

Several areas of prostate cancer (PCa) management, such as imaging permanent brachytherapy implants or small, aggressive lesions, benefit from high image resolution. Current PCa imaging methods can have inadequate resolution for imaging these areas. Endorectal digital prostate tomosynthesis (endoDPT), an imaging method that combines an external x-ray source and an endorectal x-ray sensor, can produce three-dimensional images of the prostate region that have high image resolution compared to typical methods. This high resolution may improve PCa management and increase positive outcomes in affected men. This dissertation presents the initial development of endoDPT, including system design, image quality assessment, and examples of possible applications to prostate imaging. Experiments using computational phantoms, physical phantoms, and canine prostate specimens were conducted. Initial system design was performed computationally and three methods of endoDPT image reconstruction were developed: shift and add (SAA), backprojection (BP), and filtered BP (FBP). A physical system was developed using an XDR intraoral x-ray sensor and a GE radiography unit. The resolution and radiation dose of endoDPT were measured and compared to a GE CT scanner. Canine prostate specimens that approximated clinical cases of PCa management were imaged and compared using endoDPT, the above CT scanner, and a GE MRI scanner. This study found that the resolution of endoDPT was significantly higher than CT. The radiation dose of endoDPT was significantly lower than CT in the regions of the phantom that were not in the endoDPT field of view (FoV). Inside the endoDPT FoV, the radiation dose ranged from significantly less than to significantly greater than CT. The endoDPT images of the canine prostate specimens demonstrated qualitative improvements in resolution compared to CT and MRI, but endoDPT had difficulty in visualizing larger structures, such as the prostate border. Overall, this study has demonstrated endoDPT has high image resolution compared to typical methods of PCa imaging. Future work will be focused on development of a prototype system that improves scanning efficiency that can be used to optimize endoDPT and perform pre-clinical studies

Comparison of different image reconstruction algorithms for Digital Breast Tomosynthesis and assessment of their potential to reduce radiation dose

Tese de mestrado, Engenharia Física, 2022, Universidade de Lisboa, Faculdade de CiênciasDigital Breast Tomosynthesis is a three-dimensional medical imaging technique that allows the view of sectional parts of the breast. Obtaining multiple slices of the breast constitutes an advantage in contrast to conventional mammography examination in view of the increased potential in breast cancer detectability. Conventional mammography, despite being a screening success, has undesirable specificity, sensitivity, and high recall rates owing to the overlapping of tissues. Although this new technique promises better diagnostic results, the acquisition methods and image reconstruction algorithms are still under research. Several articles suggest the use of analytic algorithms. However, more recent articles highlight the iterative algorithm’s potential for increasing image quality when compared to the former. The scope of this dissertation was to test the hypothesis of achieving higher quality images using iterative algorithms acquired with lower doses than those using analytic algorithms. In a first stage, the open-source Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox for fast and accurate 3D x-ray image reconstruction was used to reconstruct the images acquired using an acrylic phantom. The algorithms used from the toolbox were the Feldkamp, Davis, and Kress, the Simultaneous Algebraic Reconstruction Technique, and the Maximum Likelihood Expectation Maximization algorithm. In a second and final state, the possibility of further reducing the radiation dose using image postprocessing tools was evaluated. A Total Variation Minimization filter was applied to the images reconstructed with the TIGRE toolbox algorithm that provided the best image quality. These were then compared to the images of the commercial unit used for the image acquisitions. With the use of image quality parameters, it was found that the Maximum Likelihood Expectation Maximization algorithm performance was the best of the three for lower radiation doses, especially with the filter. In sum, the result showed the potential of the algorithm in obtaining images with quality for low doses

A Stationary Digital Breast Tomosynthesis System: Design Simulation, Characterization and Image Reconstruction

Conventional screen-film and/or digital mammography, despite being the most popular breast imaging modalities, suffer from certain limitations, most important of which is tissue overlap and false diagnoses arising thereof. A new three-dimensional alternative for breast cancer screening and diagnosis is tomosynthesis in which a limited number of low-dose two-dimensional projection images of a patient are used to reconstruct the three-dimensional tissue information. The tomosynthesis systems currently under development all incorporate an x-ray source that moves over a certain angle to acquire images. This tube motion is a major limitation because it degrades image quality, increases the scan time and causes prolonged patient discomfort. The availability of independently controllable carbon nanotube cathodes enabled us to explore the possibility of setting up a stationary multi-beam imaging system. In this dissertation we have proposed a stationary digital breast tomosynthesis scanner using spatially distributed carbon nanotube based field emission x-ray sources. We have presented details about the design, set-up, characterization and image reconstruction of the completely stationary digital breast tomosynthesis system. This system has the potential to reduce the total scan time and improve the image quality in breast imaging. Extensive design simulation results have been used to decide on the final system set-up. The fully assembled actual experimental system is capable of acquiring all the images in as little as eight seconds and yield superior image quality as well. The system has been completely characterized in terms of focal spot size, system resolution and geometric calibration. Certain important results have been obtained during the process that we hope will set the standard for the characterization of the future systems. A novel iterative reconstruction algorithm has been tried on the projection images obtained from the tomosynthesis system. Our algorithm has demonstrated image quality that is on par with the other tomosynthesis systems under development

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images

Geometrical Calibration and Filter Optimization for Cone-Beam Computed Tomography

This thesis will discuss the requirements of a software library for tomography and will derive a framework which can be used to realize various applications in cone-beam computed tomography (CBCT). The presented framework is self-contained and is realized using the MATLAB environment in combination with native low-level technologies (C/C++ and CUDA) to improve its computational performance, while providing accessibility and extendability through to use of a scripting language environment. On top of this framework, the realization of Katsevich’s algorithm on multicore hardware will be explained and the resulting implementation will be compared to the Feldkamp, Davis and Kress (FDK) algorithm. It will also be shown that this helical reconstruction method has the potential to reduce the measurement uncertainty. However, misalignment artifacts appear more severe in the helical reconstructions from real data than in the circular ones. Especially for helical CBCT (H-CBCT), this fact suggests that a precise calibration of the computed tomography (CT) system is inevitable. As a consequence, a self-calibration method will be designed that is able to estimate the misalignment parameters from the cone-beam projection data without the need of any additional measurements. The presented method employs a multi-resolution 2D-3D registration technique and a novel volume update scheme in combination with a stochastic reprojection strategy to achieve a reasonable runtime performance. The presented results will show that this method reaches sub-voxel accuracy and can compete with current state-of-the-art online- and offline-calibration approaches. Additionally, for the construction of filters in the area of limited-angle tomography a general scheme which uses the Approximate Inverse (AI) to compute an optimized set of 2D angle-dependent projection filters will be derived. Optimal sets of filters are then precomputed for two angular range setups and will be reused to perform various evaluations on multiple datasets with a filtered backprojection (FBP)-type method. This approach will be compared to the standard FDK algorithm and to the simultaneous iterative reconstruction technique (SIRT). The results of the study show that the introduced filter optimization produces results comparable to those of SIRT with respect to the reduction of reconstruction artifacts, whereby its runtime is comparable to that of the FDK algorithm

High-Resolution Quantitative Cone-Beam Computed Tomography: Systems, Modeling, and Analysis for Improved Musculoskeletal Imaging

This dissertation applies accurate models of imaging physics, new high-resolution imaging hardware, and novel image analysis techniques to benefit quantitative applications of x-ray CT in in vivo assessment of bone health. We pursue three Aims: 1. Characterization of macroscopic joint space morphology, 2. Estimation of bone mineral density (BMD), and 3. Visualization of bone microstructure. This work contributes to the development of extremity cone-beam CT (CBCT), a compact system for musculoskeletal (MSK) imaging. Joint space morphology is characterized by a model which draws an analogy between the bones of a joint and the plates of a capacitor. Virtual electric field lines connecting the two surfaces of the joint are computed as a surrogate measure of joint space width, creating a rich, non-degenerate, adaptive map of the joint space. We showed that by using such maps, a classifier can outperform radiologist measurements at identifying osteoarthritic patients in a set of CBCT scans. Quantitative BMD accuracy is achieved by combining a polyenergetic model-based iterative reconstruction (MBIR) method with fast Monte Carlo (MC) scatter estimation. On a benchtop system emulating extremity CBCT, we validated BMD accuracy and reproducibility via a series of phantom studies involving inserts of known mineral concentrations and a cadaver specimen. High-resolution imaging is achieved using a complementary metal-oxide semiconductor (CMOS)-based x-ray detector featuring small pixel size and low readout noise. A cascaded systems model was used to performed task-based optimization to determine optimal detector scintillator thickness in nominal extremity CBCT imaging conditions. We validated the performance of a prototype scanner incorporating our optimization result. Strong correlation was found between bone microstructure metrics obtained from the prototype scanner and µCT gold standard for trabecular bone samples from a cadaver ulna. Additionally, we devised a multiresolution reconstruction scheme allowing fast MBIR to be applied to large, high-resolution projection data. To model the full scanned volume in the reconstruction forward model, regions outside a finely sampled region-of-interest (ROI) are downsampled, reducing runtime and cutting memory requirements while maintaining image quality in the ROI

Image reconstruction and processing for stationary digital tomosynthesis systems

Digital tomosynthesis (DTS) is an emerging x-ray imaging technique for disease and cancer screening. DTS takes a small number of x-ray projections to generate pseudo-3D images, it has a lower radiation and a lower cost compared to the Computed Tomography (CT) and an improved diagnostic accuracy compared to the 2D radiography. Our research group has developed a carbon nanotube (CNT) based x-ray source. This technology enables packing multiple x-ray sources into one single x-ray source array. Based on this technology, our group built several stationary digital tomosynthesis (s-DTS) systems, which have a faster scanning time and no source motion blur. One critical step in both tomosynthesis and CT is image reconstruction, which generates a 3D image from the 2D measurement. For tomosynthesis, the conventional reconstruction method runs fast but fails in image quality. A better iterative method exists, however, it is too time-consuming to be used in clinics. The goal of this work is to develop fast iterative image reconstruction algorithm and other image processing techniques for the stationary digital tomosynthesis system, improving the image quality affected by the hardware limitation. Fast iterative reconstruction algorithm, named adapted fan volume reconstruction (AFVR), was developed for the s-DTS. AFVR is shown to be an order of magnitude faster than the current iterative reconstruction algorithms and produces better images over the classical filtered back projection (FBP) method. AFVR was implemented for the stationary digital breast tomosynthesis system (s-DBT), the stationary digital chest tomosynthesis system (s-DCT) and the stationary intraoral dental tomosynthesis system (s-IOT). Next, scatter correction technique for stationary digital tomosynthesis was investigated. A new algorithm for estimating scatter profile was developed, which has been shown to improve the image quality substantially. Finally, the quantitative imaging was investigated, where the s-DCT system was used to assess the coronary artery calcium score.Doctor of Philosoph

X-ray Phase-Contrast Tomography: Underlying Physics and Developments for Breast Imaging

X-ray phase-contrast tomography is a powerful tool to dramatically increase the visibility of features exhibiting a faint attenuation contrast within bulk samples, as is generally the case of light (low-Z) materials. For this reason, the application to clinical tasks aiming at imaging soft tissues, as e.g., breast imaging, has always been a driving force in the development of this field. In this context, the SYRMA-3D project, which constitutes the framework of the present work, aims to develop and implement the first breast computed tomography system relying on the propagation-based phase-contrast technique at the Elettra synchrotron facility (Trieste, Italy). This thesis finds itself in the \u2018last mile\u2019 towards the in-vivo implementation, and the obtained results add some of the missing pieces in the realization of the project. The first part of the work introduces a homogeneous mathematical framework describing propagation-based phase contrast from the sample-induced X-ray refraction, to detection, processing and tomographic reconstruction. The original results reported in the following chapters include the implementation of a pre-processing procedure dedicated for a novel photon-counting CdTe detector; a study, supported by a rigorous theoretical model, on signal and noise dependence on physical parameters such as propagation distance and detector pixel size; hardware and software developments for improving signal-to-noise ratio and reducing the scan time; and, finally, a clinically-oriented study based on comparisons with clinical mammographic and histological images. The last part of the thesis attempts to widen the experimental horizon: first, a quantitative image comparison of the synchrotron-based setup and a clinically available breast-CT scanner is presented and then a practical laboratory implementation is detailed, introducing a monochromatic propagation-based micro-tomography setup making use on a high-power rotating anode source

Image Quality, Modeling, and Design for High-Performance Cone-Beam CT of the Head

Diagnosis and treatment of neurological and otolaryngological diseases rely heavily on visualization of fine, subtle anatomical structures in the head. In particular, high-quality head imaging at the point of care mitigates patient risk associated with transport and decreases time to diagnosis for time-sensitive diseases. Cone-beam computed tomography (CBCT) systems have found widespread adoption in diagnostic and image-guided procedures. Such systems exhibit potential for adaptation as point-of-care systems due to relatively low cost, mechanical simplicity, and inherently high spatial resolution, but are generally challenged by low contrast imaging tasks (e.g., visualization of tumors or hemorrhages). This thesis details the development and design of a CBCT imaging system with performance sufficient for high-quality imaging of the head and suitable to deployment at the point of care. The performance of a commercially available head-and-neck CBCT scanner was assessed to determine the potential of such systems for high-quality head imaging. Results indicated low-contrast visualization was challenged by high detector noise and scatter. Photon counting x-ray detectors (PCDs) were identified as a potential technology that could improve the low-contrast visualization, and an imaging performance model was developed to quantify their imaging performance. The model revealed important implications for energy resolution, noise, and spatial resolution as a function of energy threshold and charge sharing rejection. A new CBCT system dedicated to detection of low-contrast contrast intracranial hemorrhage was designed with guidance from an imaging chain model to optimize the system configuration (geometry, detector, x-ray source, etc.). The results indicated flat panel detectors (FPDs) were favorable due to a large field of view, but benefited from detector readout gain adjustments. Dual-gain detector readout was compared with use of bowtie filter in high-gain readout mode to investigate potential improvements to noise performance in FPDs. Finally, technical assessment of the prototype CBCT head scanner (with design based on guidance from the image quality model) indicated performance suitable for translation to clinical studies in the neurosciences critical care unit