Multi-resolution inversion algorithm for the attenuated radon transform

Paolo Emilio Barbano; Athanasios S. Fokas

doi:10.1109/mlsp.2011.6064632

Multi-resolution inversion algorithm for the attenuated radon transform

Paolo Emilio Barbano, Athanasios S. Fokas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present a FAST implementation of the Inverse Attenuated Radon Transform which incorporates accurate collimator response, as well as artifact rejection due to statistical noise and data corruption. This new reconstruction procedure is performed by combining a memory-efficient implementation of the analytical inversion formula (AIF [1], [2]) with a wavelet-based version of a recently discovered regularization technique [3]. The paper introduces all the main aspects of the new AIF, as well numerical experiments on real and simulated data. Those display a substantial improvement in reconstruction quality when compared to linear or iterative algorithms. © 2011 IEEE.

Original language	English (US)
Title of host publication	2011 IEEE International Workshop on Machine Learning for Signal Processing
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	9781457716218
DOIs	https://doi.org/10.1109/mlsp.2011.6064632
State	Published - Sep 2011
Externally published	Yes

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10.1109/mlsp.2011.6064632

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@inproceedings{7fb0e37636e1419699d7449a4eaf943e,

title = "Multi-resolution inversion algorithm for the attenuated radon transform",

abstract = "We present a FAST implementation of the Inverse Attenuated Radon Transform which incorporates accurate collimator response, as well as artifact rejection due to statistical noise and data corruption. This new reconstruction procedure is performed by combining a memory-efficient implementation of the analytical inversion formula (AIF [1], [2]) with a wavelet-based version of a recently discovered regularization technique [3]. The paper introduces all the main aspects of the new AIF, as well numerical experiments on real and simulated data. Those display a substantial improvement in reconstruction quality when compared to linear or iterative algorithms. {\textcopyright} 2011 IEEE.",

author = "Barbano, {Paolo Emilio} and Fokas, {Athanasios S.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by KAUST and EPSRC. PEB was also sponsored by the Chinese Academy of Sciences. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2011",

month = sep,

doi = "10.1109/mlsp.2011.6064632",

language = "English (US)",

isbn = "9781457716218",

booktitle = "2011 IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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T1 - Multi-resolution inversion algorithm for the attenuated radon transform

AU - Barbano, Paolo Emilio

AU - Fokas, Athanasios S.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by KAUST and EPSRC. PEB was also sponsored by the Chinese Academy of Sciences. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2011/9

Y1 - 2011/9

N2 - We present a FAST implementation of the Inverse Attenuated Radon Transform which incorporates accurate collimator response, as well as artifact rejection due to statistical noise and data corruption. This new reconstruction procedure is performed by combining a memory-efficient implementation of the analytical inversion formula (AIF [1], [2]) with a wavelet-based version of a recently discovered regularization technique [3]. The paper introduces all the main aspects of the new AIF, as well numerical experiments on real and simulated data. Those display a substantial improvement in reconstruction quality when compared to linear or iterative algorithms. © 2011 IEEE.

AB - We present a FAST implementation of the Inverse Attenuated Radon Transform which incorporates accurate collimator response, as well as artifact rejection due to statistical noise and data corruption. This new reconstruction procedure is performed by combining a memory-efficient implementation of the analytical inversion formula (AIF [1], [2]) with a wavelet-based version of a recently discovered regularization technique [3]. The paper introduces all the main aspects of the new AIF, as well numerical experiments on real and simulated data. Those display a substantial improvement in reconstruction quality when compared to linear or iterative algorithms. © 2011 IEEE.

UR - http://hdl.handle.net/10754/598900

UR - http://ieeexplore.ieee.org/document/6064632/

UR - http://www.scopus.com/inward/record.url?scp=82455212635&partnerID=8YFLogxK

U2 - 10.1109/mlsp.2011.6064632

DO - 10.1109/mlsp.2011.6064632

M3 - Conference contribution

SN - 9781457716218

BT - 2011 IEEE International Workshop on Machine Learning for Signal Processing

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Multi-resolution inversion algorithm for the attenuated radon transform

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