Computational methods for image reconstruction

Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill‐posed and large‐scale and...

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Published in:	NMR in biomedicine Vol. 30; no. 4; pp. np - n/a
Main Authors:	Chung, Julianne, Ruthotto, Lars
Format:	Journal Article
Language:	English
Published:	England Wiley Subscription Services, Inc 01.04.2017
Subjects:	Algorithms Animals Brain - anatomy & histology Brain - physiology Brain Mapping - methods deconvolution Diffusion Magnetic Resonance Imaging - methods Humans ill‐posed Image Enhancement - methods Image Interpretation, Computer-Assisted - methods iterative methods linear inverse problems Models, Biological quantitative susceptibility mapping (QSM) regularization Reproducibility of Results Sensitivity and Specificity Tikhonov total variation deconvolution iterative methods linear inverse problems quantitative susceptibility mapping (QSM) ill-posed regularization total variation Tikhonov
ISSN:	0952-3480, 1099-1492, 1099-1492
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Abstract	Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill‐posed and large‐scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill‐posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state‐of‐the‐art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd. We review analytical tools and state‐of‐the‐art computational tools for solving image reconstruction problems. By comparing quantitative susceptibility mapping (QSM) with the classic image‐deblurring problem, we show that a severe challenge for QSM reconstruction is to distinguish between noise and signal contributions in the data; therefore regularization methods are crucial. We survey some regularization approaches and regularization parameter selection methods and discuss efficient numerical implementations for large‐scale QSM problems.
AbstractList	Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill-posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state-of-the-art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. We review analytical tools and state-of-the-art computational tools for solving image reconstruction problems. By comparing quantitative susceptibility mapping (QSM) with the classic image-deblurring problem, we show that a severe challenge for QSM reconstruction is to distinguish between noise and signal contributions in the data; therefore regularization methods are crucial. We survey some regularization approaches and regularization parameter selection methods and discuss efficient numerical implementations for large-scale QSM problems. Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill-posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state-of-the-art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd. Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill‐posed and large‐scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill‐posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state‐of‐the‐art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd. We review analytical tools and state‐of‐the‐art computational tools for solving image reconstruction problems. By comparing quantitative susceptibility mapping (QSM) with the classic image‐deblurring problem, we show that a severe challenge for QSM reconstruction is to distinguish between noise and signal contributions in the data; therefore regularization methods are crucial. We survey some regularization approaches and regularization parameter selection methods and discuss efficient numerical implementations for large‐scale QSM problems. Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill-posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state-of-the-art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd.Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill-posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state-of-the-art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd.
Author	Chung, Julianne Ruthotto, Lars
Author_xml	– sequence: 1 givenname: Julianne surname: Chung fullname: Chung, Julianne email: jmchung@vt.edu organization: Virginia Tech – sequence: 2 givenname: Lars surname: Ruthotto fullname: Ruthotto, Lars organization: Emory University
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Keywords	deconvolution iterative methods linear inverse problems quantitative susceptibility mapping (QSM) ill-posed regularization total variation Tikhonov
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Snippet	Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative...
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SubjectTerms	Algorithms Animals Brain - anatomy & histology Brain - physiology Brain Mapping - methods deconvolution Diffusion Magnetic Resonance Imaging - methods Humans ill‐posed Image Enhancement - methods Image Interpretation, Computer-Assisted - methods iterative methods linear inverse problems Models, Biological quantitative susceptibility mapping (QSM) regularization Reproducibility of Results Sensitivity and Specificity Tikhonov total variation
Title	Computational methods for image reconstruction
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fnbm.3545 https://www.ncbi.nlm.nih.gov/pubmed/27226213 https://www.proquest.com/docview/1878224661 https://www.proquest.com/docview/1826687178 https://www.proquest.com/docview/1881750540
Volume	30
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