Online Regularization by Denoising with Applications to Phase Retrieval

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this paper, we address this limitation by introducing a novel online RED (On-RED) algorithm, which processes a small subset of the data at a time. We establish the theoretical convergence of On-RED in convex settings and empirically discuss its effectiveness in non-convex ones by illustrating its applicability to phase retrieval. Our results suggest that On-RED is an effective alternative to the traditional RED algorithms when dealing with large datasets.Comment: Accepted ICCVW 2019 (LCI

Improving Image Reconstruction for Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has been developed to reduce the issue of overlapping tissue in conventional 2-D mammography for breast cancer screening and diagnosis. In the DBT procedure, the patient’s breast is compressed with a paddle and a sequence of x-ray projections is taken within a small angular range. Tomographic reconstruction algorithms are then applied to these projections, generating tomosynthesized image slices of the breast, such that radiologists can read the breast slice by slice. Studies have shown that DBT can reduce both false-negative diagnoses of breast cancer and false-positive recalls compared to mammography alone. This dissertation focuses on improving image quality for DBT reconstruction. Chapter I briefly introduces the concept of DBT and the inspiration of my study. Chapter II covers the background of my research including the concept of image reconstruction, the geometry of our experimental DBT system and figures of merit for image quality. Chapter III introduces our study of the segmented separable footprint (SG) projector. By taking into account the finite size of detector element, the SG projector improves the accuracy of forward projections in iterative image reconstruction. Due to the more efficient access to memory, the SG projector is also faster than the traditional ray-tracing (RT) projector. We applied the SG projector to regular and subpixel reconstructions and demonstrated its effectiveness. Chapter IV introduces a new DBT reconstruction method with detector blur and correlated noise modeling, called the SQS-DBCN algorithm. The SQS-DBCN algorithm is able to significantly enhance microcalcifications (MC) in DBT while preserving the appearance of the soft tissue and mass margin. Comparisons between the SQS-DBCN algorithm and several modified versions of the SQS-DBCN algorithm indicate the importance of modeling different components of the system physics at the same time. Chapter V investigates truncated projection artifact (TPA) removal algorithms. Among the three algorithms we proposed, the pre-reconstruction-based projection view (PV) extrapolation method provides the best performance. Possible improvements of the other two TPA removal algorithms have been discussed. Chapter VI of this dissertation examines the effect of source blur on DBT reconstruction. Our analytical calculation demonstrates that the point spread function (PSF) of source blur is highly shift-variant. We used CatSim to simulate digital phantoms. Analysis on the reconstructed images demonstrates that a typical finite-sized focal spot (~ 0.3 mm) will not affect the image quality if the x-ray tube is stationary during the data acquisition. For DBT systems with continuous-motion data acquisition, the motion of the x-ray tube is the main cause of the effective source blur and will cause loss in the contrast of objects. Therefore modeling the source blur for these DBT systems could potentially improve the reconstructed image quality. The final chapter of this dissertation discusses a few future studies that are inspired by my PhD research.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144059/1/jiabei_1.pd

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging