Image Reconstruction Applications in Medical Sciences
This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applicati...
Saved in:
| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Germany
De Gruyter
2017
Walter de Gruyter GmbH |
| Edition | 1 |
| Series | De Gruyter Textbook |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783110500592 3110500590 9783110500486 3110500485 |
| DOI | 10.1515/9783110500592 |
Cover
| Abstract | This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applications in X-ray CT, SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging) are discussed. Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich’s cone-beam filtered backprojection algorithm, and reconstruction with highly under-sampled data are included. The last chapter of the book is devoted to the techniques of using a fast analytical algorithm to reconstruct an image that is equivalent to an iterative reconstruction. These techniques are the author’s most recent research results. This book is intended for students, engineers, and researchers who are interested in medical image reconstruction. Written in a non-mathematical way, this book provides an easy access to modern mathematical methods in medical imaging. Table of Content:Chapter 1 Basic Principles of Tomography1.1 Tomography1.2 Projection1.3 Image Reconstruction1.4 Backprojection1.5 Mathematical ExpressionsProblemsReferencesChapter 2 Parallel-Beam Image Reconstruction2.1 Fourier Transform2.2 Central Slice Theorem2.3 Reconstruction Algorithms2.4 A Computer Simulation2.5 ROI Reconstruction with Truncated Projections2.6 Mathematical Expressions (The Fourier Transform and Convolution , The Hilbert Transform and the Finite Hilbert Transform , Proof of the Central Slice Theorem, Derivation of the Filtered Backprojection Algorithm , Expression of the Convolution Backprojection Algorithm, Expression of the Radon Inversion Formula ,Derivation of the Backprojection-then-Filtering AlgorithmProblemsReferencesChapter 3 Fan-Beam Image Reconstruction3.1 Fan-Beam Geometry and Point Spread Function3.2 Parallel-Beam to Fan-Beam Algorithm Conversion3.3 Short Scan3.4 Mathematical Expressions (Derivation of a Filtered Backprojection Fan-Beam Algorithm, A Fan-Beam Algorithm Using the Derivative and the Hilbert Transform)ProblemsReferencesChapter 4 Transmission and Emission Tomography4.1 X-Ray Computed Tomography4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography4.3 Attenuation Correction for Emission Tomography4.4 Mathematical ExpressionsProblemsReferencesChapter 5 3D Image Reconstruction5.1 Parallel Line-Integral Data5.2 Parallel Plane-Integral Data5.3 Cone-Beam Data (Feldkamp's Algorithm, Grangeat's Algorithm, Katsevich's Algorithm)5.4 Mathematical Expressions (Backprojection-then-Filtering for Parallel Line-Integral Data, Filtered Backprojection Algorithm for Parallel Line-Integral Data, 3D Radon Inversion Formula, 3D Backprojection-then-Filtering Algorithm for Radon Data, Feldkamp's Algorithm, Tuy's Relationship, Grangeat's Relationship, Katsevich’s Algorithm)ProblemsReferencesChapter 6 Iterative Reconstruction6.1 Solving a System of Linear Equations6.2 Algebraic Reconstruction Technique6.3 Gradient Descent Algorithms6.4 Maximum-Likelihood Expectation-Maximization Algorithms6.5 Ordered-Subset Expectation-Maximization Algorithm6.6 Noise Handling (Analytical Methods, Iterative Methods, Iterative Methods)6.7 Noise Modeling as a Likelihood Function6.8 Including Prior Knowledge6.9 Mathematical Expressions (ART, Conjugate Gradient Algorithm, ML-EM, OS-EM, Green’s One-Step Late Algorithm, Matched and Unmatched Projector/Backprojector Pairs )6.10 Reconstruction Using Highly Undersampled Data with l0 MinimizationProblemsReferencesChapter 7 MRI Reconstruction7.1 The 'M'7.2 The 'R'7.3 The |
|---|---|
| AbstractList | This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applications in X-ray CT, SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging) are discussed. Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly under-sampled data are included. The last chapter of the book is devoted to the techniques of using a fast analytical algorithm to reconstruct an image that is equivalent to an iterative reconstruction. These techniques are the author's most recent research results. This book is intended for students, engineers, and researchers who are interested in medical image reconstruction. Written in a non-mathematical way, this book provides an easy access to modern mathematical methods in medical imaging. Table of Content:Chapter 1 Basic Principles of Tomography1.1 Tomography1.2 Projection1.3 Image Reconstruction1.4 Backprojection1.5 Mathematical ExpressionsProblemsReferencesChapter 2 Parallel-Beam Image Reconstruction2.1 Fourier Transform2.2 Central Slice Theorem2.3 Reconstruction Algorithms2.4 A Computer Simulation2.5 ROI Reconstruction with Truncated Projections2.6 Mathematical Expressions (The Fourier Transform and Convolution , The Hilbert Transform and the Finite Hilbert Transform , Proof of the Central Slice Theorem, Derivation of the Filtered Backprojection Algorithm , Expression of the Convolution Backprojection Algorithm, Expression of the Radon Inversion Formula ,Derivation of the Backprojection-then-Filtering AlgorithmProblemsReferencesChapter 3 Fan-Beam Image Reconstruction3.1 Fan-Beam Geometry and Point Spread Function3.2 Parallel-Beam to Fan-Beam Algorithm Conversion3.3 Short Scan3.4 Mathematical Expressions (Derivation of a Filtered Backprojection Fan-Beam Algorithm, A Fan-Beam Algorithm Using the Derivative and the Hilbert Transform)ProblemsReferencesChapter 4 Transmission and Emission Tomography4.1 X-Ray Computed Tomography4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography4.3 Attenuation Correction for Emission Tomography4.4 Mathematical ExpressionsProblemsReferencesChapter 5 3D Image Reconstruction5.1 Parallel Line-Integral Data5.2 Parallel Plane-Integral Data5.3 Cone-Beam Data (Feldkamp's Algorithm, Grangeat's Algorithm, Katsevich's Algorithm)5.4 Mathematical Expressions (Backprojection-then-Filtering for Parallel Line-Integral Data, Filtered Backprojection Algorithm for Parallel Line-Integral Data, 3D Radon Inversion Formula, 3D Backprojection-then-Filtering Algorithm for Radon Data, Feldkamp's Algorithm, Tuy's Relationship, Grangeat's Relationship, Katsevich's Algorithm)ProblemsReferencesChapter 6 Iterative Reconstruction6.1 Solving a System of Linear Equations6.2 Algebraic Reconstruction Technique6.3 Gradient Descent Algorithms6.4 Maximum-Likelihood Expectation-Maximization Algorithms6.5 Ordered-Subset Expectation-Maximization Algorithm6.6 Noise Handling (Analytical Methods, Iterative Methods, Iterative Methods)6.7 Noise Modeling as a Likelihood Function6.8 Including Prior Knowledge6.9 Mathematical Expressions (ART, Conjugate Gradient Algorithm, ML-EM, OS-EM, Green's One-Step Late Algorithm, Matched and Unmatched Projector/Backprojector Pairs )6.10 Reconstruction Using Highly Undersampled Data with l0 MinimizationProblemsReferencesChapter 7 MRI Reconstruction7.1 The 'M'7.2 The 'R'7.3 The 'I'; (To Obtain z-Information, x-Information, y-Information)7.4 Mathematical ExpressionsProblemsReferencesIndexing This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applications in X-ray CT, SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging) are discussed. Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich’s cone-beam filtered backprojection algorithm, and reconstruction with highly under-sampled data are included. The last chapter of the book is devoted to the techniques of using a fast analytical algorithm to reconstruct an image that is equivalent to an iterative reconstruction. These techniques are the author’s most recent research results. This book is intended for students, engineers, and researchers who are interested in medical image reconstruction. Written in a non-mathematical way, this book provides an easy access to modern mathematical methods in medical imaging. Table of Content:Chapter 1 Basic Principles of Tomography1.1 Tomography1.2 Projection1.3 Image Reconstruction1.4 Backprojection1.5 Mathematical ExpressionsProblemsReferencesChapter 2 Parallel-Beam Image Reconstruction2.1 Fourier Transform2.2 Central Slice Theorem2.3 Reconstruction Algorithms2.4 A Computer Simulation2.5 ROI Reconstruction with Truncated Projections2.6 Mathematical Expressions (The Fourier Transform and Convolution , The Hilbert Transform and the Finite Hilbert Transform , Proof of the Central Slice Theorem, Derivation of the Filtered Backprojection Algorithm , Expression of the Convolution Backprojection Algorithm, Expression of the Radon Inversion Formula ,Derivation of the Backprojection-then-Filtering AlgorithmProblemsReferencesChapter 3 Fan-Beam Image Reconstruction3.1 Fan-Beam Geometry and Point Spread Function3.2 Parallel-Beam to Fan-Beam Algorithm Conversion3.3 Short Scan3.4 Mathematical Expressions (Derivation of a Filtered Backprojection Fan-Beam Algorithm, A Fan-Beam Algorithm Using the Derivative and the Hilbert Transform)ProblemsReferencesChapter 4 Transmission and Emission Tomography4.1 X-Ray Computed Tomography4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography4.3 Attenuation Correction for Emission Tomography4.4 Mathematical ExpressionsProblemsReferencesChapter 5 3D Image Reconstruction5.1 Parallel Line-Integral Data5.2 Parallel Plane-Integral Data5.3 Cone-Beam Data (Feldkamp's Algorithm, Grangeat's Algorithm, Katsevich's Algorithm)5.4 Mathematical Expressions (Backprojection-then-Filtering for Parallel Line-Integral Data, Filtered Backprojection Algorithm for Parallel Line-Integral Data, 3D Radon Inversion Formula, 3D Backprojection-then-Filtering Algorithm for Radon Data, Feldkamp's Algorithm, Tuy's Relationship, Grangeat's Relationship, Katsevich’s Algorithm)ProblemsReferencesChapter 6 Iterative Reconstruction6.1 Solving a System of Linear Equations6.2 Algebraic Reconstruction Technique6.3 Gradient Descent Algorithms6.4 Maximum-Likelihood Expectation-Maximization Algorithms6.5 Ordered-Subset Expectation-Maximization Algorithm6.6 Noise Handling (Analytical Methods, Iterative Methods, Iterative Methods)6.7 Noise Modeling as a Likelihood Function6.8 Including Prior Knowledge6.9 Mathematical Expressions (ART, Conjugate Gradient Algorithm, ML-EM, OS-EM, Green’s One-Step Late Algorithm, Matched and Unmatched Projector/Backprojector Pairs )6.10 Reconstruction Using Highly Undersampled Data with l0 MinimizationProblemsReferencesChapter 7 MRI Reconstruction7.1 The 'M'7.2 The 'R'7.3 The |
| Author | Zeng, Gengsheng Lawrence |
| Author_xml | – sequence: 1 fullname: Zeng, Gengsheng Lawrence |
| BookMark | eNqNz0tLAzEUBeCID6y1O92LG3FRzc1rkmUtVQsFQcRtyCTpw06Tmkyt_nsHK0h14-reAx8HzhHaCzF4hE4BXwEHfq0KSQEwx5grsoM6W3n3Vz5ALSVxwQSXcIg6Ob9gjEEyJjFtoZPhwkz82aO3MeQ6rWw9i-EY7Y9NlX3n-7bR8-3gqX_fHT3cDfu9UddQJsl7lxYcO2Io5yWhnArlGKEgDSldSa2TXDilmDDOjQVIBY0GEFCUwnooLaNtdLkpNnnu13kaqzrrt8qXMc6z3prxP8uaqUT-2LWpap-cn6TVR_PohUn2T-_Fxi5TfF35XOuvSutDnUylBzd9JinmBTTyfCOtyaaahZlexBAnySynWXMoBBBFPwFVm3V- |
| ContentType | eBook |
| DBID | I4C |
| DEWEY | 616 |
| DOI | 10.1515/9783110500592 |
| DatabaseName | Casalini Torrossa eBooks Institutional Catalogue |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Applied Sciences |
| EISBN | 9783110500592 3110500590 3110498022 9783110498028 |
| Edition | 1 1st edition. |
| ExternalDocumentID | 9783110500592 9783110498028 EBC4830571 5176129 |
| GroupedDBID | -VX 20A 38. 5O. AABBV AAUSU AAZEP ABARN ABHWV ABONK ABQPQ ABYVA ACEOT ACISH ACLGV ADDXO ADOUC ADVEM ADVQQ ADZGP AEAED AEDVL AEJQT AEQJM AERYV AETUO AEYCP AFHFQ AFRFP AGIZT AGLJD AHUFE AIOLA AIUUY AIXPE AJFER ALMA_UNASSIGNED_HOLDINGS AOEKO APELF ARPAB ARSQP AZZ BBABE BFRBX BHFIF CZZ DLQEV DNKAV DUGUG EBSCA ECOWB I4C MYL OHILO PQQKQ QD8 XI1 AAHDW ABMRC ACBYE AHWGJ AVGCG YSPEL |
| ID | FETCH-LOGICAL-a3482x-3750d2a355b235369d42318a2bdb3cd856d9946addf6189150d11617b6ce1bc43 |
| ISBN | 9783110500592 3110500590 9783110500486 3110500485 |
| IngestDate | Fri Nov 08 05:15:22 EST 2024 Fri Nov 08 05:15:17 EST 2024 Thu Jun 19 20:31:06 EDT 2025 Wed Sep 10 01:54:46 EDT 2025 Wed Feb 19 03:02:54 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCallNum_Ident | RC |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a3482x-3750d2a355b235369d42318a2bdb3cd856d9946addf6189150d11617b6ce1bc43 |
| OCLC | 980746581 984643034 |
| PQID | EBC4830571 |
| PageCount | 240 |
| ParticipantIDs | askewsholts_vlebooks_9783110500592 askewsholts_vlebooks_9783110498028 walterdegruyter_marc_9783110500592 proquest_ebookcentral_EBC4830571 casalini_monographs_5176129 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 [2017] 2017-03-20 |
| PublicationDateYYYYMMDD | 2017-01-01 2017-03-20 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationPlace | Germany |
| PublicationPlace_xml | – name: Germany – name: Berlin/Boston – name: Berlin – name: Boston |
| PublicationSeriesTitle | De Gruyter Textbook |
| PublicationYear | 2017 |
| Publisher | De Gruyter Walter de Gruyter GmbH |
| Publisher_xml | – name: De Gruyter – name: Walter de Gruyter GmbH |
| RestrictionsOnAccess | restricted access |
| SSID | ssj0001844803 |
| Score | 2.0059721 |
| Snippet | This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging,... |
| SourceID | askewsholts walterdegruyter proquest casalini |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Diseases MEDICAL / Diagnostic Imaging / Radiography Medical sciences |
| Subtitle | Applications in Medical Sciences |
| TableOfContents | Intro -- Contents -- 1. Basic principles of tomography -- 1.1 Tomography -- 1.2 Projection -- 1.3 Image reconstruction -- 1.4 Backprojection -- 1.5 Mathematical expressions -- 1.5.1 Projection -- 1.5.2 Backprojection -- 1.5.3 The Dirac -function -- 1.6 Worked examples -- 1.7 Summary -- Problems -- Bibliography -- 2. Parallel-beam image reconstruction -- 2.1 Fourier transform -- 2.2 Central slice theorem -- 2.3 Reconstruction algorithms -- 2.3.1 Method 1 -- 2.3.2 Method 2 -- 2.3.3 Method 3 -- 2.3.4 Method 4 -- 2.3.5 Method 5 -- 2.3.6 Method 6 -- 2.4 A computer simulation -- 2.5 ROI reconstruction with truncated projections -- 2.6 Mathematical expressions -- 2.6.1 The Fourier transform and convolution -- 2.6.2 The Hilbert transform and the finite Hilbert transform -- 2.6.3 Proof of the central slice theorem -- 2.6.4 Derivation of the FBP algorithm -- 2.6.5 Expression of the convolution backprojection algorithm -- 2.6.6 Expression of the Radon inversion formula -- 2.6.7 Derivation of the backprojection-then-filtering algorithm -- 2.6.8 Expression of the derivative-backprojection-Hilbert transform algorithm -- 2.6.9 Derivation of the backprojection-derivative-Hilbert transform algorithm -- 2.7 Worked examples -- 2.8 Summary -- Problems -- Bibliography -- 3. Fan-beam image reconstruction -- 3.1 Fan-beam geometry and the point spread function -- 3.2 Parallel-beam to fan-beam algorithm conversion -- 3.3 Short scan -- 3.4 Mathematical expressions -- 3.4.1 Derivation of a filtered backprojection fan-beam algorithm -- 3.4.2 A fan-beam algorithm using the derivative and the Hilbert transform -- 3.4.3 Expression for the Parker weights -- 3.4.4 Errors caused by finite bandwidth implementation -- 3.5 Worked examples -- 3.6 Summary -- Problems -- Bibliography -- 4. Transmission and emission tomography -- 4.1 X-ray computed tomography 4.2 Positron emission tomography and single-photon emission computed tomography -- 4.3 Noise propagation in reconstruction -- 4.3.1 Noise variance of emission data -- 4.3.2 Noise variance of transmission data -- 4.3.3 Noise propagation in an FBP algorithm -- 4.4 Attenuation correction for emission tomography -- 4.4.1 PET -- 4.4.2 SPECT: Tretiak-Metz FBP algorithm for uniform attenuation -- 4.4.3 SPECT: Inouye's algorithm for uniform attenuation -- 4.5 Mathematical expressions -- 4.5.1 Expression for Tretiak-Metz FBP algorithm -- 4.5.2 Derivation for Inouye's algorithm -- 4.5.3 Rullgård's derivative-then-backprojection algorithm for uniform attenuation -- 4.5.4 Novikov-Natterer FBP algorithm for nonuniform attenuation SPECT -- 4.6 Worked examples -- 4.7 Summary -- Problems -- Bibliography -- 5. Three-dimensional image reconstruction -- 5.1 Parallel line-integral data -- 5.1.1 Backprojection-then-filtering -- 5.1.2 Filtered backprojection -- 5.2 Parallel plane-integral data -- 5.3 Cone-beam data -- 5.3.1 Feldkamp's algorithm -- 5.3.2 Grangeat's algorithm -- 5.3.3 Katsevich's algorithm -- 5.4 Mathematical expressions -- 5.4.1 Backprojection-then-filtering for parallel line-integral data -- 5.4.2 FBP algorithm for parallel line-integral data -- 5.4.3 Three-dimensional Radon inversion formula (FBP algorithm) -- 5.4.4 Three-dimensional backprojection-then-filtering algorithm for Radon data -- 5.4.5 Feldkamp's algorithm -- 5.4.6 Tuy's relationship -- 5.4.7 Grangeat's relationship -- 5.4.8 Katsevich's algorithm -- 5.5 Worked examples -- 5.6 Summary -- Problems -- Bibliography -- 6. Iterative reconstruction -- 6.1 Solving a system of linear equations -- 6.2 Algebraic reconstruction technique -- 6.3 Gradient descent algorithms -- 6.3.1 The gradient descent algorithm -- 6.3.2 The Landweber algorithm -- 6.3.3 The conjugate gradient algorithm 6.4 ML-EM algorithms -- 6.5 OS-EM algorithm -- 6.6 Noise handling -- 6.6.1 Analytical methods - windowing -- 6.6.2 Iterative methods - stopping early -- 6.6.3 Iterative methods - choosing pixels -- 6.6.4 Iterative methods - accurate modeling -- 6.7 Noise modeling as a likelihood function -- 6.8 Including prior knowledge (Bayesian) -- 6.9 Mathematical expressions -- 6.9.1 ART -- 6.9.2 The Landweber algorithm -- 6.9.3 CG algorithm -- 6.9.4 ML-EM -- 6.9.5 OS-EM -- 6.9.6 MAP (Green's one-step late algorithm) -- 6.9.7 Matched and unmatched projector/backprojector pairs -- 6.10 Reconstruction using highly undersampled data -- 6.11 Worked examples -- 6.12 Summary -- Problems -- Bibliography -- 7. MR Ireconstruction -- 7.1 The "M" -- 7.2 The "R" -- 7.3 The "I" -- 7.3.1 To obtain z-information: slice selection -- 7.3.2 To obtain x-information: frequency encoding -- 7.3.3 To obtain y-information: phase encoding -- 7.4 Mathematical expressions -- 7.5 Image reconstruction for MRI -- 7.5.1 Fourier reconstruction -- 7.5.2 Iterative reconstruction -- 7.6 Worked examples -- 7.7 Summary -- Problems -- Bibliography -- 8. Using FBP to perform iterative reconstruction -- 8.1 The Landweber algorithm: From recursive form to non-recursive form -- 8.2 The Landweber algorithm: From non-recursive form to closed form -- 8.3 The Landweber algorithm: From closed form to backprojection-then-filtering algorithm -- 8.3.1 Implementation of (ATA)-1 in the Fourier domain -- 8.3.2 Implementation of I - (I - !ATA)k in the Fourier domain -- 8.3.3 Landweber algorithm: Backprojection-then-filtering algorithm -- 8.3.4 Numerical examples of the window function -- 8.4 The Landweber algorithm: The weighted FBP algorithm -- 8.4.1 Landweber algorithm: FBP without noise weighting -- 8.4.2 Landweber algorithm: FBP with view-based noise weighting 8.4.3 Landweber algorithm: FBP with ray-based noise weighting -- 8.5 FBP algorithm with quadratic constraints -- 8.5.1 Example of minimum norm-constrained FBP -- 8.5.2 Example of reference image-constrained FBP -- 8.6 Convolution backprojection -- 8.7 Non-quadratic constraints -- 8.8 A viewpoint from calculus of variations -- 8.9 Summary -- Problems -- Bibliography 1. Basic principles of tomography -- Contents -- 3. Fan-beam image reconstruction -- 4. Transmission and emission tomography -- Preface -- 7. MRI reconstruction -- 8. Using FBP to perform iterative reconstruction -- Frontmatter -- 2. Parallel-beam image reconstruction -- Index 5. Three-dimensional image reconstruction -- 6. Iterative reconstruction -- |
| Title | Image Reconstruction |
| URI | http://digital.casalini.it/9783110500592 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=4830571 https://www.degruyter.com/isbn/9783110500592 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783110498028 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783110500592 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dT9swED9BkSb6wBgfonRM0cRrRtzYjj1pL4MyNKl7KhPaS2TH5kNAmZYytv31Oztu0lRIsL2kbera7p3t-51z9zPAvttUoNoUMc-UjSnRMlbG8hixcSaKVFLjty5GX_jJKf18xs7aDMH3U_2u-PNoXsn_aBXvoV5dluw_aLauFG_ge9QvXlHDeF0Av_XHEHh-60JtnOvYEMD6Lb7v7fjw2WOYMINr_PzNVlP8E76Wl9YlOqkHn_dXrTKO_bj8cIQj6Mf9b0ejOMY1vO5D2CRAw5Ok8SBpIj3q8i33MUXbzzzp3qOLKfO8E005Vp1at8BP3fp-GZazDH3gFTSqw1Gz2SXQC0zcmRp1k6xiP2q6EAhQsdGDVpVd6KryGld8tAbT0sEHVSqXNdryCdYefHSBsRfV_5wDCeN1WLEuc-QVLNnJBrwM-D6ayX4DXoxCIMMmcK_BqK3B99G8_qKrSRT0V9exBV-Ph-PDkzgcYRErxxr0C9dvlpiBQlSnBylLuTSIX4lQA210WhjBuJGScrQy55wIifDcEOdyal5YoguabkNncjexOxARwTVx_IuJSqiiqZJSoffMXGitKoTuwds5QeU_b_zj9jIP0qRSIJZ8olAl8h70Z0LOccpU3OllzkiGeFj2IJrJPfe_DmHE-fDjIRVoPzKCrSzoI3fMLO1Wdp_TlT6sNuP5NXRQI3YPYeBUvwmD7C_x2lTn |
| linkProvider | ProQuest Ebooks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Image+Reconstruction%3A+Applications+in+Medical+Sciences&rft.au=Zeng%2C+Gengsheng+Lawrence&rft.series=De+Gruyter+Textbook&rft.date=2017-03-20&rft.pub=De+Gruyter&rft.isbn=9783110500486&rft_id=info:doi/10.1515%2F9783110500592&rft.externalDocID=9783110500592 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.degruyter.com%2Fdocument%2Fcover%2Fisbn%2F9783110500592%2Foriginal http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97831104%2F9783110498028.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97831105%2F9783110500592.jpg |