Download or read book Applications of Statistical Modeling in Iterative CT Image Reconstruction written by David Simon Perlmutter and published by . This book was released on 2015 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally, x-ray CT images are produced by an algorithm called filtered back projection, or FBP. FBP is an analytical solution to the idealized CT image reconstruction problem, the inverse problem of turning raw x-ray measurements into a full 3-dimensional (3D) image, and is derived assuming a continuous set of noiseless measurements. However real CT data are noisy and biased, especially so if the scans are performed at low x-ray dose, and advanced statistical estimation techniques have been shown to produce higher quality images than FBP. This work presents two applications of statistical modeling in CT image reconstruction. The first application discusses the statistics of CT data noise, and compares the performance of several common models for estimation in a simplified 1D experiment. The second application concerns modeling temporal CT data, in which the measured data typically contain redundancies. It proposes an estimation method that exploits these redundancies to address two key challenges in CT image reconstruction: reducing noise and lowering computation time. We demonstrate this noise reduction analytically and through experimental simulations. In addition, a third study validates the use of the statistical models used in this work by comparing them to measured data from a clinical CT scanner. Overall, these methods contribute to the methodology of statistical CT image reconstruction to enable ultra-low dose x-ray CT imaging.
Download or read book Statistical Modeling and Path based Iterative Reconstruction for X ray Computed Tomography written by Meng Wu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: X-ray computed tomography (CT) and tomosynthesis systems have proven to be indispensable components in medical diagnosis and treatment. My research is to develop advanced image reconstruction and processing algorithms for the CT and tomosynthesis systems. Streak artifacts caused by metal objects such as dental fillings, surgical instruments, and orthopedic hardware may obscure important diagnostic information in X-ray computed tomography (CT) images. To improve the image quality, we proposed to complete the missing kilovoltage (kV) projection data with selectively acquired megavoltage (MV) data that do not suffer from photon starvation. We developed two statistical image reconstruction methods, dual-energy penalized weighted least squares and polychromatic maximum likelihood, for combining kV and selective MV data. Cramer-Rao Lower Bound for Compound Poisson was studied to revise the statistical model and minimize radiation dose. Numerical simulations and phantom studies have shown that the combined kV/MV imaging systems enable a better delineation of structures of interest in CT images for patients with metal objects. The x-ray tube on the CT system produces a wide x-ray spectrum. Polychromatic statistical CT reconstruction is desired for more accurate quantitative measurement of the chemical composition and density of the tissue. Polychromatic statistical reconstruction algorithms usually have very high computational demands due to complicated optimization frameworks and the large number of spectrum bins. We proposed a spectrum information compression method and a new optimization framework to significantly reduce the computational cost in reconstructions. The new algorithm applies to multi-material beam hardening correction, adaptive exposure control, and spectral imaging. Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The MBIR approach is often modeled as a convex optimization problem including a data fitting function and a penalty function. The tuning parameter value that regulates the strength of the penalty function is critical for achieving good reconstruction results but is difficult to choose. We have developed two path seeking algorithms that are capable of generating a path of MBIR images with different strengths of the penalty function. The errors of the proposed path seeking algorithms are reasonably small throughout the entire reconstruction path. With the efficient path seeking algorithm, we suggested a path-based iterative reconstruction (PBIR) to obtain complete information from the scanned data and reconstruction model. Additionally, we have developed a convolution-based blur-and-add model for digital tomosynthesis systems that can be used in efficient system analysis, task-dependent optimization, and filter design. We also proposed a computationally practical algorithm to simulate and subtract out-of-plane artifacts in tomosynthesis images using patient-specific prior CT volumes.
Download or read book System and Image Modeling in Statistical Iterative Reconstruction for Multi slice CT written by Jiao Wang and published by . This book was released on 2012 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Iterative Reconstruction and Dose Reduction in Multi Slice Computed Tomography written by Katharina Hahn and published by . This book was released on 2022-02-14 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computed tomography is one of the most important imaging methods in medical technology. Although computed tomography examinations only make up a small proportion of X-ray examinations, they do make a great contribution to civilizing radiation exposure of the population. By using statistical iterative reconstruction methods, it is possible to reduce the mean radiation dose per examination. While statistical iterative reconstruction methods enable the modeling of physical imaging properties, the user can decide freely and independently about the choice of numerous free parameters. However, every parameterization decision has an influence on the final image quality. In this work, inter alia the definition of the modeling of the forward projection is examined as well as the influence of statistical weights and data redundancies in interaction with various iterative reconstruction techniques. Several extensive studies were put together, which challenge these different combinations in every respect and push the models to their limits. Image quality was assessed using the following quantitative metrics: basic metrics and task-based metrics. The investigation shows that the definition of iterative reconstruction parameters is not always trivial and must always be understood comprehensively to obtain an optimal image quality. Finally, a novel reconstruction algorithm, called FINESSE, is presented, which improves some of the weaknesses of other reconstruction techniques.
Download or read book Statistical Image Reconstruction for Quantitative Computed Tomography written by Joshua D. Evans and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical iterative reconstruction (SIR) algorithms for x-ray computed tomography (CT) have the potential to reconstruct images with less noise and systematic error than the conventional filtered backprojection (FBP) algorithm. More accurate reconstruction algorithms are important for reducing imaging dose and for a wide range of quantitative CT applications. The work presented herein investigates some potential advantages of one such statistically motivated algorithm called Alternating Minimization (AM). A simulation study is used to compare the tradeoff between noise and resolution in images reconstructed with the AM and FBP algorithms. The AM algorithm is employed with an edge-preserving penalty function, which is shown to result in images with contrast-dependent resolution. The AM algorithm always reconstructed images with less image noise than the FBP algorithm. Compared to previous studies in the literature, this is the first work to clearly illustrate that the reported noise advantage when using edge-preserving penalty functions can be highly dependent on the contrast of the object used for quantifying resolution. A polyenergetic version of the AM algorithm, which incorporates knowledge of the scanner's x-ray spectrum, is then commissioned from data acquired on a commercially available CT scanner. Homogeneous cylinders are used to assess the absolute accuracy of the polyenergetic AM algorithm and to compare systematic errors to conventional FBP reconstruction. Methods to estimate the x-ray spectrum, model the bowtie filter and measure scattered radiation are outlined which support AM reconstruction to within 0.5% of the expected ground truth. The polyenergetic AM algorithm reconstructs the cylinders with less systematic error than FBP, in terms of better image uniformity and less object-size dependence. Finally, the accuracy of a post-processing dual-energy CT (pDECT) method to non-invasively measure a material's photon cross-section information is investigated. Data is acquired on a commercial scanner for materials of known composition. Since the pDECT method has been shown to be highly sensitive to reconstructed image errors, both FBP and polyenergetic AM reconstruction are employed. Linear attenuation coefficients are estimated with residual errors of around 1% for energies of 30 keV to 1 MeV with errors rising to 3%-6% at lower energies down to 10 keV. In the ideal phantom geometry used here, the main advantage of AM reconstruction is less random cross-section uncertainty due to the improved noise performance.
Download or read book Quantifying Differences in CT Image Quality Between a Model based Iterative Reconstruction Algorithm an Adaptive Statistical Iterative Reconstruction Algorithm and Filtered Backprojection written by Hayley M. Whitson and published by . This book was released on 2017 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 3D Image Reconstruction for CT and PET written by Daniele Panetta and published by CRC Press. This book was released on 2020-10-11 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.
Download or read book Medical Image Reconstruction written by Gengsheng Lawrence Zeng and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-07-04 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces the essential concepts of tomography in the field of medical imaging. The medical imaging modalities include x-ray CT (computed tomography), PET (positron emission tomography), SPECT (single photon emission tomography) and MRI. In these modalities, the measurements are not in the image domain and the conversion from the measurements to the images is referred to as the image reconstruction. The work covers various image reconstruction methods, ranging from the classic analytical inversion methods to the optimization-based iterative image reconstruction methods. As machine learning methods have lately exhibited astonishing potentials in various areas including medical imaging the author devotes one chapter to applications of machine learning in image reconstruction. Based on college level in mathematics, physics, and engineering the textbook supports students in understanding the concepts. It is an essential reference for graduate students and engineers with electrical engineering and biomedical background due to its didactical structure and the balanced combination of methodologies and applications,
Download or read book Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half ring PET Insert System written by Daniel Brian Keesing and published by . This book was released on 2009 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: X-ray computed tomography (CT) and positron emission tomography (PET) have become widely used imaging modalities for screening, diagnosis, and image-guided treatment planning. Along with the increased clinical use are increased demands for high image quality with reduced ionizing radiation dose to the patient. Despite their significantly high computational cost, statistical iterative reconstruction algorithms are known to reconstruct high-quality images from noisy tomographic datasets. The overall goal of this work is to design statistical reconstruction software for clinical x-ray CT scanners, and for a novel PET system that utilizes high-resolution detectors within the field of view of a whole-body PET scanner. The complex choices involved in the development and implementation of image reconstruction algorithms are fundamentally linked to the ways in which the data is acquired, and they require detailed knowledge of the various sources of signal degradation. Both of the imaging modalities investigated in this work have their own set of challenges. However, by utilizing an underlying statistical model for the measured data, we are able to use a common framework for this class of tomographic problems. We first present the details of a new fully 3D regularized statistical reconstruction algorithm for multislice helical CT. To reduce the computation time, the algorithm was carefully parallelized by identifying and taking advantage of the specific symmetry found in helical CT. Some basic image quality measures were evaluated using measured phantom and clinical datasets, and they indicate that our algorithm achieves comparable or superior performance over the fast analytical methods considered in this work. Next, we present our fully 3D reconstruction efforts for a high-resolution half-ring PET insert. We found that this unusual geometry requires extensive redevelopment of existing reconstruction methods in PET. We redesigned the major components of the data modeling process and incorporated them into our reconstruction algorithms. The algorithms were tested using simulated Monte Carlo data and phantom data acquired by a PET insert prototype system. Overall, we have developed new, computationally efficient methods to perform fully 3D statistical reconstructions on clinically-sized datasets.
Download or read book Fundamentals of Computerized Tomography written by Gabor T. Herman and published by Springer Science & Business Media. This book was released on 2009-07-14 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This revised and updated second edition – now with two new chapters - is the only book to give a comprehensive overview of computer algorithms for image reconstruction. It covers the fundamentals of computerized tomography, including all the computational and mathematical procedures underlying data collection, image reconstruction and image display. Among the new topics covered are: spiral CT, fully 3D positron emission tomography, the linogram mode of backprojection, and state of the art 3D imaging results. It also includes two new chapters on comparative statistical evaluation of the 2D reconstruction algorithms and alternative approaches to image reconstruction.
Download or read book Modeling and Development of Iterative Reconstruction Algorithms in Emerging X ray Imaging Technologies written by Jiaofeng Xu and published by . This book was released on 2014 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many new promising X-ray-based biomedical imaging technologies have emerged over the last two decades. Five different novel X-ray based imaging technologies are discussed in this dissertation: differential phase-contrast tomography (DPCT), grating-based phase-contrast tomography (GB-PCT), spectral-CT (K-edge imaging), cone-beam computed tomography (CBCT), and in-line X-ray phase contrast (XPC) tomosynthesis. For each imaging modality, one or more specific problems prevent them being effectively or efficiently employed in clinical applications have been discussed. Firstly, to mitigate the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods in DPCT, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction. Secondly, to improve image quality in grating-based phase-contrast tomography, we incorporate 2nd order statistical properties of the object property sinograms, including correlations between them, into the formulation of an advanced multi-channel (MC) image reconstruction algorithm, which reconstructs three object properties simultaneously. We developed an advanced algorithm based on the proximal point algorithm and the augmented Lagrangian method to rapidly solve the MC reconstruction problem. Thirdly, to mitigate image artifacts that arise from reduced-view and/or noisy decomposed sinogram data in K-edge imaging, we exploited the inherent sparseness of typical K-edge objects and incorporated the statistical properties of the decomposed sinograms to formulate two penalized weighted least square problems with a total variation (TV) penalty and a weighted sum of a TV penalty and an l1-norm penalty with a wavelet sparsifying transform. We employed a fast iterative shrinkage/thresholding algorithm (FISTA) and splitting-based FISTA algorithm to solve these two PWLS problems. Fourthly, to enable advanced iterative algorithms to obtain better diagnostic images and accurate patient positioning information in image-guided radiation therapy for CBCT in a few minutes, two accelerated variants of the FISTA for PLS-based image reconstruction are proposed. The algorithm acceleration is obtained by replacing the original gradient-descent step by a sub-problem that is solved by use of the ordered subset concept (OS-SART). In addition, we also present efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units (GPUs). Finally, we employed our developed accelerated version of FISTA for dealing with the incomplete (and often noisy) data inherent to in-line XPC tomosynthesis which combines the concepts of tomosynthesis and in-line XPC imaging to utilize the advantages of both for biological imaging applications. We also investigate the depth resolution properties of XPC tomosynthesis and demonstrate that the z-resolution properties of XPC tomosynthesis is superior to that of conventional absorption-based tomosynthesis. To investigate all these proposed novel strategies and new algorithms in these different imaging modalities, we conducted computer simulation studies and real experimental data studies. The proposed reconstruction methods will facilitate the clinical or preclinical translation of these emerging imaging methods.
Download or read book Computed Tomography E Book written by Euclid Seeram and published by Elsevier Health Sciences. This book was released on 2022-06-16 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build the foundation necessary for the practice of CT scanning with Computed Tomography: Physical Principles, Patient Care, Clinical Applications, and Quality Control, 5th Edition. Written to meet the varied requirements of radiography students and practitioners, this two-color text provides comprehensive coverage of the physical principles of computed tomography and its clinical applications. The clear, straightforward approach is designed to improve your understanding of sectional anatomic images as they relate to computed tomography and facilitate communication between CT technologists and other medical personnel. Chapter outlines and chapter review questions help you focus your study time and master content. NEW! Three additional chapters reflect the latest industry CT standards in imaging: Radiation Awareness and Safety Campaigns in Computed Tomography, Patient Care Considerations, and Artificial Intelligence: An Overview of Applications in Health and Medical Imaging. UPDATED! More than 509 photos and line drawings visually clarify key concepts. UPDATED! The latest information keeps you up to date on advances in volume CT scanning; CT fluoroscopy; and multislice applications like 3-D imaging, CT angiography, and virtual reality imaging (endoscopy).
Download or read book Computed Tomography written by Euclid Seeram and published by Saunders. This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiologic technologists play an important role in the care and management of patients undergoing advanced imaging procedures. This new edition provides the up-to-date information and thorough coverage you need to understand the physical principles of computed tomography (CT) and safely produce high-quality images. You'll gain valuable knowledge about the practice of CT scanning, effective communication with other medical personnel, and sectional anatomic images as they relate to CT. Features a chapter devoted to quality control testing of CT scanners (both spiral CT and conventional scan-and-stop), helping you achieve and maintain high quality control standards. Provides the latest information on: advances in volume CT scanning; CT fluoroscopy; multi-slice spiral/helical CT; and multi-slice applications such as 3-D imaging, CT angiography, and virtual reality imaging (endoscopy)--all with excellent coverage of state-of-the-art principles, instrumentation, clinical applications and quality control. Two new chapters cover recent developments and important principles of multislice CT and PET/CT, giving you in-depth coverage of these quickly emerging aspects of CT.
Download or read book Computational Intelligence Theories Applications and Future Directions Volume II written by Nishchal K. Verma and published by Springer. This book was released on 2018-09-01 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike.
Download or read book Iterative Reconstruction Methods of CT Images Using a Statistical Framework written by Diana (Diana Carolina) Delgado and published by . This book was released on 2011 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography (CT) is currently the most commonly used medical imaging technology because it is easy to use, detectors and scanners are constantly improving, and more importantly, patients receive less radiation compared to other imaging technologies. This thesis focuses on improving CT reconstruction algorithms by incorporating prior knowledge of the tissues being scanned. A Gaussian Mixture Prior, and Gibbs sampling is introduced into the reconstruction framework and solved using Maximum-a-posterior (MAP). As a comparison, the images were also reconstructed using unregularized and regularized Maximum Likelihood (ML).
Download or read book Statistical Reconstruction Algorithms for Polyenergetic X ray Computed Tomography written by Idris A. Elbakri and published by . This book was released on 2003 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Next Generation CT Image Reconstruction Via Synergy of Human Wisdom and Machine Intelligence written by Chengzhu Zhang (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computed tomography (CT) is a widely used non-destructive imaging technique for medical diagnosis, interventional procedures, and treatment planning. CT reconstruction involves accurately recovering linear attenuation coefficients in the form of image pixels from experimentally measured CT data in the form of line integrals. Provided that the acquired data satisfy the data sufficiency condition and other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have devised many solutions, including deterministic and statistical iterative approaches to reconstruct the CT image accurately. However, if these conditions are violated, accurate and robust image reconstruction from ill-posed CT data remains an intellectual challenge. Deep learning methods offer powerful regression capabilities to perform imaging processing tasks such as noise mitigation and artifact reduction. However, these methods face fundamental issues in medical imaging applications, such as accuracy and performance degradation when applied to individual patients or different patient cohorts. When the problem becomes overly ill-posed due to aggressive view angle undersampling and data truncation, the image artifacts in the conventional reconstructed images become so severe that crucial patient information is obscured from the deep neural network. Consequently, the deep learning methods may miss or "daydream" information, potentially leading to disastrous outcomes. This thesis project proposed several novel CT image reconstruction frameworks that synergistically combine analytical, iterative, and deep learning approaches to tackle three long-standing difficult CT reconstruction problems. The first study proposed a quality-assured deep learning reconstruction framework called "DL-PICCS", which combined a deep learning strategy with prior image constrained compressed sensing to tackle sparse-view reconstruction problems. The images post-processed by a deep neural network were used as the prior compressed sensing image. In contrast, the measured sinogram data were used to correct falsely reconstructed image details and avoid over-smoothness. The same method was also leveraged to defend against adversarial perturbations intentionally crafted and added to the network input to make the deep neural network unstable. The second study proposed a new reconstruction framework called "Deep-Interior" that leveraged weighted backprojection and a deep neural network to address severe data truncation for both short-scan and super-short-scan data acquisition schemes. The weighted backprojection was derived as a nice feature space, a blurred version of the original CT image with a shift-invariant blurring kernel. The deep learning model learns a generalizable deconvolution scheme that can be applied to arbitrary regions within the patient's body. The third study leveraged the power of analytical reconstruction and statistical analysis to estimate patient-specific and local noise power spectra from single CT data acquisitions. The statistical properties of the new estimator were rigorously derived to demonstrate its superiority over the conventional method using repeated samples. Completing this thesis project offers promising software advancements that can accelerate the arrival of next-generation novel CT imaging techniques with significantly reduced radiation dose, lower equipment costs, and improved patient care quality.
