23 research outputs found

    Machine Learning for Biomedical Application

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    Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

    令和2年(2020年)福島県立医科大学業績集

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    Recent Advances in Signal Processing

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    The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

    Preclinical MRI of the kidney : methods and protocols

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    This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

    Motion correction of free-breathing magnetic resonance renography using model-driven registration

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    Introduction Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). Materials and methods MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). Results DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. Discussion MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods

    Functional pulmonary MRI with ultra-fast steady-state free precession

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    To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment. At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter. Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing). The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma. The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies

    The radiological investigation of musculoskeletal tumours : chairperson's introduction

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    Infective/inflammatory disorders

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