1,706 research outputs found

    Contributions to Statistical Image Analysis for High Content Screening.

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    Images of cells incubated with fluorescent small molecule probes can be used to infer where the compounds distribute within cells. Identifying the spatial pattern of compound localization within each cell is very important problem for which adequate statistical methods do not yet exist. First, we asked whether a classifier for subcellular localization categories can be developed based on a training set of manually classified cells. Due to challenges of the images such as uneven field illumination, low resolution, high noise, variation in intensity and contrast, and cell to cell variability in probe distributions, we constructed texture features for contrast quantiles conditioning on intensities, and classifying on artificial cells with same marginal distribution but different conditional distribution supported that this conditioning approach is beneficial to distinguish different localization distributions. Using these conditional features, we obtained satisfactory performance in image classification, and performed to dimension reduction and data visualization. As high content images are subject to several major forms of artifacts, we are interested in the implications of measurement errors and artifacts on our ability to draw scientifically meaningful conclusions from high content images. Specifically, we considered three forms of artifacts: saturation, blurring and additive noise. For each type of artifacts, we artificially introduced larger amount, and aimed to understand the bias by `Simulation Extrapolation' (SIMEX) method, applied to the measurement errors for pairwise centroid distances, the degree of eccentricity in the class-specific distributions, and the angles between the dominant axes of variability for different categories. Finally, we briefly considered the analysis of time-point images. Small molecule studies will be more focused. Specifically, we consider the evolving patterns of subcellular staining from the moment that a compound is introduced into the cell culture medium, to the point that steady state distribution is reached. We construct the degree to which the subcellular staining pattern is concentrated in or near the nucleus as the features of timecourse data set, and aim to determine whether different compounds accumulate in different regions at different times, as characterized in terms of their position in the cell relative to the nucleus.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91460/1/liufy_1.pd

    3D Imaging for Planning of Minimally Invasive Surgical Procedures

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    Novel minimally invasive surgeries are used for treating cardiovascular diseases and are performed under 2D fluoroscopic guidance with a C-arm system. 3D multidetector row computed tomography (MDCT) images are routinely used for preprocedural planning and postprocedural follow-up. For preprocedural planning, the ability to integrate the MDCT with fluoroscopic images for intraprocedural guidance is of clinical interest. Registration may be facilitated by rotating the C-arm to acquire 3D C-arm CT images. This dissertation describes the development of optimal scan and contrast parameters for C-arm CT in 6 swine. A 5-s ungated C-arm CT acquisition during rapid ventricular pacing with aortic root injection using minimal contrast (36 mL), producing high attenuation (1226), few artifacts (2.0), and measurements similar to those from MDCT (p\u3e0.05) was determined optimal. 3D MDCT and C-arm CT images were registered to overlay the aortic structures from MDCT onto fluoroscopic images for guidance in placing the prosthesis. This work also describes the development of a methodology to develop power equation (R2\u3e0.998) for estimating dose with C-arm CT based on applied tube voltage. Application in 10 patients yielded 5.48┬▒177 2.02 mGy indicating minimal radiation burden. For postprocedural follow-up, combinations of non-contrast, arterial, venous single energy CT (SECT) scans are used to monitor patients at multiple time intervals resulting in high cumulative radiation dose. Employing a single dual-energy CT (DECT) scan to replace two SECT scans can reduce dose. This work focuses on evaluating the feasibility of DECT imaging in the arterial phase. The replacement of non-contrast and arterial SECT acquisitions with one arterial DECT acquisition in 30 patients allowed generation of virtual non-contrast (VNC) images with 31 dose savings. Aortic luminal attenuation in VNC (32┬▒177 2 HU) was similar to true non-contrast images (35┬▒177 4 HU) indicating presence of unattenuated blood. To improve discrimination between c

    3D Imaging for Planning of Minimally Invasive Surgical Procedures

    Get PDF
    Novel minimally invasive surgeries are used for treating cardiovascular diseases and are performed under 2D fluoroscopic guidance with a C-arm system. 3D multidetector row computed tomography (MDCT) images are routinely used for preprocedural planning and postprocedural follow-up. For preprocedural planning, the ability to integrate the MDCT with fluoroscopic images for intraprocedural guidance is of clinical interest. Registration may be facilitated by rotating the C-arm to acquire 3D C-arm CT images. This dissertation describes the development of optimal scan and contrast parameters for C-arm CT in 6 swine. A 5-s ungated C-arm CT acquisition during rapid ventricular pacing with aortic root injection using minimal contrast (36 mL), producing high attenuation (1226), few artifacts (2.0), and measurements similar to those from MDCT (p\u3e0.05) was determined optimal. 3D MDCT and C-arm CT images were registered to overlay the aortic structures from MDCT onto fluoroscopic images for guidance in placing the prosthesis. This work also describes the development of a methodology to develop power equation (R2\u3e0.998) for estimating dose with C-arm CT based on applied tube voltage. Application in 10 patients yielded 5.48┬▒177 2.02 mGy indicating minimal radiation burden. For postprocedural follow-up, combinations of non-contrast, arterial, venous single energy CT (SECT) scans are used to monitor patients at multiple time intervals resulting in high cumulative radiation dose. Employing a single dual-energy CT (DECT) scan to replace two SECT scans can reduce dose. This work focuses on evaluating the feasibility of DECT imaging in the arterial phase. The replacement of non-contrast and arterial SECT acquisitions with one arterial DECT acquisition in 30 patients allowed generation of virtual non-contrast (VNC) images with 31 dose savings. Aortic luminal attenuation in VNC (32┬▒177 2 HU) was similar to true non-contrast images (35┬▒177 4 HU) indicating presence of unattenuated blood. To improve discrimination between c

    Separable Inverse Problems, Blind Deconvolution, and Stray Light Correction for Extreme Ultraviolet Solar Images.

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    The determination of the inputs to a system given noisy output data is known as an inverse problem. When the system is a linear transformation involving unknown side parameters, the problem is called separable. A quintessential separable inverse problem is blind deconvolution: given a blurry image one must determine the sharp image and point spread function (PSF) that were convolved together to form it. This thesis describes a novel optimization approach for general separable inverse problems, a new blind deconvolution method for images corrupted by camera shake, and the first stray light correction for extreme ultraviolet (EUV) solar images from the EUVI/STEREO instruments. We present a generalization of variable elimination methods for separable inverse problems beyond least squares. Existing variable elimination methods require an explicit formula for the optimal value of the linear variables, so they cannot be used in problems with Poisson likelihoods, bound constraints, or other important departures from least squares. To address this limitation, we propose a generalization of variable elimination in which standard optimization methods are modified to behave as though a variable has been eliminated. Computational experiments indicate that this approach can have significant speed and robustness advantages. A new incremental sparse approximation method is proposed for blind deconvolution of images corrupted by camera shake. Unlike current state-of-the-art variational Bayes methods, it is based on simple alternating projected gradient optimization. In experiments on a standard test set, our method is faster than the state-of-the-art and competitive in deblurring performance. Stray light PSFs are determined for the two EUVI instruments, EUVI-A and B, aboard the STEREO mission. The PSFs are modeled using semi-empirical parametric formulas, and their parameters are determined by semiblind deconvolution of EUVI images. The EUVI-B PSFs were determined from lunar transit data, exploiting the fact that the Moon is not a significant EUV source. The EUVI-A PSFs were determined by analysis of simultaneous A/B observations from December 2006, when the instruments had nearly identical lines of sight to the Sun. We provide the first estimates of systematic error in EUV deconvolved images.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99797/1/shearerp_1.pd

    IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

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    In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions
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