Reconstruction methods for sparse-data X-ray tomography/ (Record no. 36576)

MARC details
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001 - CONTROL NUMBER
control field 37915
003 - CONTROL NUMBER IDENTIFIER
control field P5A
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221213140604.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180118s2017 bl por d
035 ## - SYSTEM CONTROL NUMBER
System control number ocm51338542
040 ## - CATALOGING SOURCE
Original cataloging agency P5A
Transcribing agency P5A
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number cs
090 ## - IMPA CODE FOR CLASSIFICATION SHELVES
IMPA CODE FOR CLASSIFICATION SHELVES Congressos e Seminários.
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Siltanen, Samuli
Affiliation (University of Helsinki, Finland)
9 (RLIN) 9380
245 10 - TITLE STATEMENT
Title Reconstruction methods for sparse-data X-ray tomography/
Statement of responsibility, etc. Samuli Siltanen.
246 1# - VARYING FORM OF TITLE
Title proper/short title Minicurso: Reconstruction methods for sparse-data X-ray tomography
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Rio de Janeiro:
Name of publisher, distributor, etc. IMPA,
Date of publication, distribution, etc. 2017.
300 ## - PHYSICAL DESCRIPTION
Extent video online
500 ## - GENERAL NOTE
General note Minicurso - 3 aulas
505 2# - FORMATTED CONTENTS NOTE
Formatted contents note X-ray tomography is an imaging method where an unknown physical body is photographed from many directions using X-rays. The X-rays passing through the object lose their intensity exponentially in proportion to the density of the material along the ray according to the Beer-Lambert law. After a calibration step one arrives at the following mathematical problem: can one recover a non-negative, compactly supported function from the knowledge of integrals of that function along lines? Johann Radon showed in his seminal 1917 article how to do that in dimension two when all possible line integrals are known. Radon’s geometric reconstruction formula serves as the foundation of today’s Computerized Tomography (CT) scanners in hospitals in the form of the Filtered Back-Projection (FBP) algorithm. FBP is based on inverting the so-called Radon transform. Recently, there is growing interest in X-ray tomography imaging based on limited data. The main reason for this is the need to limit the harmful radiation dose to the patient. Mathematically, the problem of recovering a function from an incomplete set of line integrals is an example of a linear ill-posed inverse problem. Illposedness means that the reconstruction problem is extremely sensitive to measurement noise and modelling errors. In such situations the FBP algorithm is not optimal. This course discusses variational regularisation methods for limited-data X-ray tomography, including classical Tikhonov regularisation and modern sparsitypromoting algorithms such as Total Variation regularization. The core idea behind these methods is complementing the insufficient measurement data by additional information about the unknown function. The methods presented in the course are applicable to any linear illposed inverse problems. Also, they can be extended to nonlinear cases.
650 04 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Matematica.
Source of heading or term larpcal
9 (RLIN) 19899
697 ## - LOCAL SUBJECT
Local Subject Congressos e Seminários.
Linkage 23755
856 4# - ELECTRONIC LOCATION AND ACCESS
Public note AULA 1
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856 4# - ELECTRONIC LOCATION AND ACCESS
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856 4# - ELECTRONIC LOCATION AND ACCESS
Public note AULA 3
Uniform Resource Identifier <a href="https://www.youtube.com/watch?v=69HA1t0SsQg&index=3&list=PLo4jXE-LdDTQmU66tIMLtjdIKjwV_XjiT">https://www.youtube.com/watch?v=69HA1t0SsQg&index=3&list=PLo4jXE-LdDTQmU66tIMLtjdIKjwV_XjiT</a>
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