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 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 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 Multi GPU Acceleration of Iterative X ray CT Image Reconstruction written by Ayan Mitra and published by . This book was released on 2018 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time.We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O'Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets.

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 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 Computed Tomography written by Jiang Hsieh and published by Society of Photo Optical. This book was released on 2009-01-01 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: X-ray computed tomography (CT) continues to experience rapid growth, both in basic technology and new clinical applications. Seven years after its first edition, Computed Tomography: Principles, Design, Artifacts, and Recent Advancements, Second Edition, provides an overview of the evolution of CT, the mathematical and physical aspects of the technology, and the fundamentals of image reconstruction algorithms. Image display is examined from traditional methods used through the most recent advancements. Key performance indices, theories behind the measurement methodologies, and different measurement phantoms in image quality are discussed. The CT scanner is broken down into components to provide the reader with an understanding of their function, their latest advances, and their impact on the CT system. General descriptions and different categories of artifacts, their causes, and their corrections are considered at length. Given the high visibility and public awareness of the impact of x-ray radiation, the second edition features a new chapter on x-ray dose and presents different dose reduction techniques ranging from patient handling, optimal data acquisition, image reconstruction, and post-process. Based on the advancements over the past five years, the second edition added new sections on cone beam reconstruction algorithms, nonconventional helical acquisition and reconstruction, new reconstruction approaches, and dual-energy CT. Finally, new to this edition is a set of problems for each chapter, providing opportunities to enhance reader comprehension and practice the application of covered material.

Download or read book Medical Imaging Systems written by Andreas Maier and published by Springer. This book was released on 2018-08-02 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book gives a complete and comprehensive introduction to the fields of medical imaging systems, as designed for a broad range of applications. The authors of the book first explain the foundations of system theory and image processing, before highlighting several modalities in a dedicated chapter. The initial focus is on modalities that are closely related to traditional camera systems such as endoscopy and microscopy. This is followed by more complex image formation processes: magnetic resonance imaging, X-ray projection imaging, computed tomography, X-ray phase-contrast imaging, nuclear imaging, ultrasound, and optical coherence tomography.

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 Multislice CT written by Konstantin Nikolaou and published by Springer. This book was released on 2019-08-06 with total page 1116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fourth edition of this well-received book offers a comprehensive update on recent developments and trends in the clinical and scientific applications of multislice computed tomography. Following an initial section on the most significant current technical aspects and issues, detailed information is provided on a comprehensive range of diagnostic applications. Imaging of the head and neck, the cardiovascular system, the abdomen, and the lungs is covered in depth, describing the application of multislice CT in a variety of tumors and other pathologies. Emerging fields such as pediatric imaging and CT-guided interventions are fully addressed, and emergency CT is also covered. Radiation exposure, dual-energy imaging, contrast enhancement, image postprocessing, CT perfusion imaging, and CT angiography all receive close attention. The new edition has been comprehensively revised and complemented by contributions from highly experienced and well-known authors who offer diverse perspectives, highlighting the possibilities offered by the most modern multidetector CT systems. This book will be particularly useful for general users of CT systems who wish to upgrade and enhance not only their machines but also their knowledge.

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 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 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 The Radon Transform and Medical Imaging written by Peter Kuchment and published by SIAM. This book was released on 2014-03-20 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book surveys the main mathematical ideas and techniques behind some well-established imaging modalities such as X-ray CT and emission tomography, as well as a variety of newly developing coupled-physics or hybrid techniques, including thermoacoustic tomography. The Radon Transform and Medical Imaging emphasizes mathematical techniques and ideas arising across the spectrum of medical imaging modalities and explains important concepts concerning inversion, stability, incomplete data effects, the role of interior information, and other issues critical to all medical imaging methods. For nonexperts, the author provides appendices that cover background information on notation, Fourier analysis, geometric rays, and linear operators. The vast bibliography, with over 825 entries, directs readers to a wide array of additional information sources on medical imaging for further study.

Download or read book Visual grading evaluation of reconstruction methods and dose optimisation in abdominal Computed Tomography written by Bharti Kataria and published by Linköping University Electronic Press. This book was released on 2019-10-15 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its introduction in the 1970’s CT has emerged as a modality of choice because of its high sensitivity in producing accurate diagnostic images. A third of all Computed Tomography (CT) examinations are abdominal CTs which deliver one of the highest doses among common examinations. An increase in the number of CT examinations has raised concerns about the negative effects of ionising radiation as the dose is cumulative over the life span of the individual. Image quality in CT is closely related to the radiation dose, so that a certain dose with an associated small, but not negligible, risk is a prerequisite for high image quality. Typically, dose reduction in CT results in higher noise and a decrease in low contrast resolution which can be detrimental to the image quality produced. New technology presents a wide range of dose reduction strategies, the latest being iterative reconstruction (IR).The aim of this thesis was to evaluate two different classes of iterative reconstruction algorithms: statistical (SAFIRE) and model-based (ADMIRE) as well as to explore the diagnostic value of a low-dose abdominal CT for optimisation purposes. This thesis included a total of 140 human subjects in four image quality evaluation studies, three of which were prospective studies (Papers I, II and IV) and one retrospective study (Paper III). Visual grading experiments to determine the potential dose reductions, were performed with pairwise comparison of image quality in the same patient at different tube loads (dose) and reconstructed with Filtered back projection (FBP) and SAFIRE strength 1 in a low-dose abdominal CT (Paper I) and FBP and ADMIRE strengths 3 and 5 in a standard dose abdominal CT (Paper II). Paper IV evaluated the impact of slice thicknesses in CT images reconstructed with ADMIRE strengths 3 and 5 when comparing multiplanar reconstruction (MPR) formatted images in a standard dose abdominal CT. Paper III, on the other hand, was an absolute assessment of image quality and pathology between the three phases of a CT Urography (CTU) protocol to explore the diagnostic value of low-dose abdominal CT. The anonymised images were displayed in random order and image quality was assessed by a group of radiologists using image quality criteria from the “European guidelines of quality criteria for CT”. The responses from the reviewer assessment were analysed statistically with ordinal logistic regression i.e. Visual Grading Regression (VGR). Results in Paper I show that a small dose reduction (5-9 %) was possible using SAFIRE strength 1and indicated the need for further research to evaluate the dose reduction potential of higher strengths of the algorithm. In Paper II a 30% dose reduction was possible without change in ADMIRE algorithm strength as no improvement in image quality was observed between tube loads 98- and 140 mAs. When comparing tube loads 42 and 98 mAs, further dose reduction was possible with ADMIRE strength 3 (22-47%). However, for images reconstructed with ADMIRE strength 5, a dose reduction of 34-74% was possible for some, but not all image criteria. Image quality in low-contrast objects such as the liver parenchyma, was affected and a decline in diagnostic confidence was observed. Paper IV showed potential dose reductions are possible with increasing slice thickness from 1 mm to 2 mm (24-35%) and 1 mm to 3mm (25-41%). ADMIRE strength 3 continued to provide diagnostically acceptable images with possible dose reductions for all image criteria assessed. Despite objective evaluations showing a decrease in noise and an increase in contrast to noise ratio, ADMIRE strength 5 had diverse effects on the five image criteria, depending on slice thickness and further dose reductions were limited to certain image criteria. The findings do not support a general recommendation to replace ADMIRE3 with ADMIRE5 in clinical abdominal CT protocols. Paper III studied another aspect of optimisation and results show that visualisation of renal anatomy was as expected in favour of the post-contrast phases when compared to the native phase. Assessment of pathology showed no significant differences between the three phases. Significantly higher diagnostic certainty for renal anatomy was observed for the post-contrast phases when compared to the native phase. Significantly high certainty scores were also seen for the nephrographic phase for incidental findings. The conclusion is that a low-dose series seems to be sufficient as a first-line modality in certain patient groups. This thesis clinically evaluated the effect of IR in abdominal CT imaging and estimated potential dose reductions. The important conclusion from papers I, II and IV is that IR improves image quality in abdominal CT allowing for some dose reductions. However, the clinical utility of the highest strength of the algorithm is limited to certain criteria. The results can be used to optimise the clinical abdominal CT protocol. The conclusion from paper III may increase clinical awareness of the value of the low-dose abdominal protocol when choosing an imaging method for certain patient groups who are more sensitive to radiation. Datortomografi (DT) används i allt större omfattning vid bilddiagnostik och ger en viss stråldos till patienten. DT är en viktig, snabb och patientvänlig undersökningsteknik. En fördel med denna teknik är att bildmaterialet kan rekonstrueras i olika format för att åskådliggöra anatomin på bästasätt beroende på vilken frågeställning som ska besvaras. Joniserande strålning från dessa undersökningar anses öka risken för negativa effekter även om risken för den enskilde patient är mycket liten. Antalet datortomografiundersökningar ökar från år till år vilket kan leda till ökade stråldoser tillbefolkningen. Optimering av undersökningsteknik och val av undersökning för att minska negativa effekter av röntgenstrålning är därför nödvändig. Det övergripande målet med avhandlingen var att utvärdera bildkvalitetvid en DT-undersökning av buken (då dessa medför en av de högstastråldoserna bland de vanliga röntgenundersökningarna), att kvantifieramöjlig stråldosminskning med hjälp av iterativa rekonstruktionsalgoritmer och att utvärdera diagnostiska värdet av lågdosundersökningsteknik vid DT-buk. Av de fyra delstudierna var delarbeten I, II och IV prospektiva och delarbete III retrospektivt. För de prospektiva studierna, samlades bildmaterial in vid en kliniskberättigad undersökning av lågdos-DT av buken (delarbetet I), eller standarddos-DT av buken (delarbetet II och IV). Bilder rekonstruerades meden standard bildrekonstruktionsalgoritm, filtrerad återprojektion (FBP), och med styrka 1 av den iterativa algoritmen SAFIRE (delarbetet I). I delarbeten II och IV, gjordes bildrekonstruktioner med FBP och med styrka 3 och 5 av den iterativa algoritmen ADMIRE. Avidentifierade bildmaterialför varje patient visades parvis i slumpmässig ordning för ett antal granskare och bildkvaliteten bedömdes med hjälp av europeiska bildkriterier. I den retrospektiva studien, delarbete III, hämtades bildmaterialet från utförda DT-urografiundersökningar från bildarkivet. För varje undersökning visades bilder från varje fas i DT-urografiundersökningen separat i slumpmässig ordning. För samtliga delarbeten, hämtades bildkriteriernafrån ”European Guidelines of Quality Criteria for CT” och modifierades för att passa till varje studie. Granskarnas bedömning analyserades med ordinal logistisk regression så kallad visual grading regression (VGR). Resultat från delarbetet I visade att det fanns en signifikant inverkan av dos (p <0,001) och rekonstruktionsalgoritm (p <0,01) på samtliga bildkriterier, med en beräknad möjlig dosminskning på 5–9%. Delarbetet II visade att rekonstruktionsalgoritmen ADMIRE förbättrar bildkvaliteten i jämförelse med FBP. ADMIRE styrka 3 tillåter en dosminskning mellan 22–47% för samtliga bildkriterier medan ADMIRE styrka 5 tillåter en dosminskning mellan 34–74% för nästan alla bedömda bildkriterier utom återgivning av leverns parenkym. Ett mycket oväntat resultat var att bildkvalitén för 70% dosnivå bedömdes som högre eller likvärdig med 100% dosnivå, vilket innebar att stråldosen kan sänkas med 30% utan förändring i algoritm eller styrka. Resultaten av delarbete III visade att avbildning av njuranatomi var som förväntat för varje fas med fördel för kontrastuppladdningsfaserna jämfört med den nativa fasen. Detta var inte ett oväntat resultat eftersom DT-urografiprotokollet är utformat för att visualisera njuranatomi på bästa möjliga sätt. Vid bedömning av patologiska fynd, erhölls dock små och ickesignifikanta skillnader mellan faserna. Däremot noterades signifikant högre bedömningssäkerhet för patologi i njurarna för de kontrast förstärkta faserna jämfört med nativfasen, och endast för bifynd signifikant högre poäng för parenkymfasen. Delarbete IV visade att styrka 5 jämfört med styrka 3 av den iterativa rekonstruktionsalgoritmen, har olika effekter på bedömningen av bildkvalitetskriterierna. Ökning av MPR-snittjocklek från 1 mm till 2 mm eller 3mm, ger en förbättring i bildkvalité, vilket möjliggör en viss dosreduktion. Den kliniska användbarheten av ADMIRE styrka 5 är begränsad, medan ADMIRE styrka 3 levererar bättre bildkvalitet för samtliga undersökta bildkriterier vid datortomografiundersökning av buken. Den viktigaste slutsatsen av delarbeten I, II och IV är att iterativa rekonstruktionsalgoritmer förbättrar bildkvalitet jämfört med FBP för samma stråldos och en dosminskning är möjlig. Detta kan användas för att optimera det kliniska DT-bukundersöknings protokoll. Slutsatsen för delarbetet III var att en lågdos-DT-bukundersökning är ett av många dosreduceringsalternativ, som möjligen kan användas för att minska strålningsbördan hos vissa patientgrupper som är mer känsliga för röntgenstrålning.

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.