Robust approaches to non - linear diffusion based compressed sensing in parallel MRI (Record no. 6825)

MARC details
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fixed length control field 02958nam a22002057a 4500
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control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122900.0
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fixed length control field 210203b xxu||||| |||| 00| 0 eng d
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Transcribing agency IIITMK
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Personal name Ajin Joy
9 (RLIN) 18497
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Title Robust approaches to non - linear diffusion based compressed sensing in parallel MRI
300 ## - PHYSICAL DESCRIPTION
Extent PHD THESIS 2019
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General note Magnetic resonance imaging (MRI) is one of the most popular non-invasive imaging techniques used to look inside the human body and visually represent the physiology of various organs and tissues. One of its particularly notable features is the lack of ionizing radiation involved. However, a relatively high scanning time puts it at a <br/>disadvantage. Therefore, a major component of the research in this field over the last four decades has been focused on improving the imaging speed while also trying to achieve better image quality. The demand for accelerated imaging is often met by restricting the amount of data collected from the scanner. Missing data would then be estimated offline to reconstruct an artifact-free image. In this thesis, a new approach to MRI reconstruction using robust non-linear (NL) diffusion-based compressed sensing (CS) is introduced and investigated in detail. <br/>The signal processing technique of CS is widely popular due to its ability to <br/>facilitate efficient acquisition and reconstruction of a sparse or compressible signal <br/>like that of MRI, from relatively few measurements. Among the numerous sparse <br/>approximation techniques available in CS, minimization of total variation (TV) has <br/>been the key approach to sharply preserve the edges during the reconstruction process. <br/>In the primary phase of this work, a Perona-Malik (PM) diffusion-based sparse approximation algorithm is developed as an alternative to TV to address its high sensitivity to regularization parameter. In the succeeding part, a mixed-order diffusion <br/>the algorithm is developed that can prevent the formation of both staircase and speckle effects during reconstruction. <br/>It is further observed that the direction of image gradient computation has a significant influence on the diffusion of both edges and artifacts. In the final part of the work, this critical aspect is addressed by developing a directionality guided diffusion reconstruction algorithm. This enables better preservation of the complex structural details in the image by adapting the direction of diffusion to local variations in the directionality of edges and employing a precise diffusion in the local regions of the image on a sub-pixel level.
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Degree type PHD THESIS
Name of granting institution SEPTEMBER 2019
Year degree granted INT
-- Prof. (Dr.) Joseph Suresh Paul, <br/>Research Supervisor, <br/>Indian Institute of Information Technology and Management-Kerala,
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Topical term or geographic name entry element MAGNETIC RESONANCE IMAGING
9 (RLIN) 18498
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Topical term or geographic name entry element K-SPACE METHODS
9 (RLIN) 18499
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Topical term or geographic name entry element NON LINEAR DIFFUSION
9 (RLIN) 18500
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Topical term or geographic name entry element COMPRESSED SENSING
9 (RLIN) 18501
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     IIITM-K IIITM-K   03/02/2021   TH - 8 03/02/2021 03/02/2021 Thesis