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  • 1
    Keywords: Biomedical Engineering ; Signal processing ; Image processing ; Speech processing systems ; Radiology ; Biomedical Engineering/Biotechnology ; Biomedical Engineering and Bioengineering ; Signal, Image and Speech Processing ; Imaging / Radiology ; Springer eBooks
    Description / Table of Contents: What is Medical Imaging? -- Part I: From Signals… -- Basic Concepts -- Transmission: X-Rays -- Reflection : Ultrasound -- Emission: SPECT/PET -- Resonance: NMR -- Part II: …To Images -- A Revision of Frequency Analysis -- Basic Concepts -- Timing-Based Reconstruction -- Back-Projection Reconstruction: X-Ray and PET/SPECT -- Fourier Reconstruction: MRI -- Part III: Functional and Physiological Imaging -- Contrast Agents -- Tracer Kinetics -- Examples of Tracer Kinetic Methods -- Other Physiological and Functional MRI Methods
    Abstract: This introduction to medical imaging introduces all of the major medical imaging techniques in wide use in both medical practice and medical research, including Computed Tomography, Ultrasound, Positron Emission Tomography, Single Photon Emission Tomography and Magnetic Resonance Imaging. Principles of Medical Imaging for Engineers introduces fundamental concepts related to why we image and what we are seeking to achieve to get good images, such as the meaning of ‘contrast’ in the context of medical imaging. This introductory text separates the principles by which ‘signals’ are generated and the subsequent ‘reconstruction’ processes, to help illustrate that these are separate concepts and also highlight areas in which apparently different medical imaging methods share common theoretical principles. Exercises are provided in every chapter, so the student reader can test their knowledge and check against worked solutions and examples. The text considers firstly the underlying physical principles by which information about tissues within the body can be extracted in the form of signals, considering the major principles used: transmission, reflection, emission and resonance. Then, it goes on to explain how these signals can be converted into images, i.e., full 3D volumes, where appropriate showing how common methods of ‘reconstruction’ are shared by some imaging methods despite relying on different physics to generate the ‘signals’. Finally, it examines how medical imaging can be used to generate more than just pictures, but genuine quantitative measurements, and increasingly measurements of physiological processes, at every point within the 3D volume by methods such as the use of tracers and advanced dynamic acquisitions. Principles of Medical Imaging for Engineers will be of use to engineering and physical science students and graduate students with an interest in biomedical engineering, and to their lecturers
    Pages: XIV, 169 p. : online resource.
    Edition: 1st ed. 2019.
    ISBN: 9783030305116
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  • 2
    ISSN: 1573-8744
    Keywords: constrained optimization ; cubic splines ; deconvolution ; Fourier transforms ; genetic algorithms ; maximum entropy ; system identification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract We present results for the comparison of six deconvolution techniques. The methods we consider are based on Fourier transforms, system identification, constrained optimization, the use of cubic spline basis functions, maximum entropy, and a genetic algorithm. We compare the performance of these techniques by applying them to simulated noisy data, in order to extract an input function when the unit impulse response is known. The simulated data are generated by convolving the known impulse response with each of five different input functions, and then adding noise of constant coefficient of variation. Each algorithm was tested on 500 data sets, and we define error measures in order to compare the performance of the different methods.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1573-8744
    Keywords: antibodies ; compartmental models ; identifiability ; nonlinear models ; tumor targeting
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A mathematical model has been developed to optimize tumor targeting with labeled antibodies. The model is compartmental and nonlinear, incorporating saturable binding. Published parameter values have been used in the model, and the resulting stiff differential equations have been solved using FACSIMILE, a computer package that can simulate very stiff differential systems. Results show that successful tumor targeting depends on an optimal combination of antibody dose, affinity, and molecular size. The model has allowed an assessment to be made of the complicated and interrelated dynamic relationships that these factors have on tumor targeting. It has also offered an explanation for previously unsatisfactory results from tumor targeting with labeled antibodies. The structural identifiability of the model parameters is also analyzed and it is shown that, with the prior knowledge of some parameters which is likely in practice, the remaining model parameters are uniquely identifiable.
    Type of Medium: Electronic Resource
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