Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data

Kerstin Hammernik; Thomas Pock; Robert Nuster

doi:10.1117/12.2254863

Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data

Kerstin Hammernik, Thomas Pock, Robert Nuster

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

In this work, we explore different reconstruction algorithms for photoacoustic image reconstruction of spatially resolved projection data. While the commonly used back-projected reconstruction is efficient and fast to compute, it cannot deal with noise that arises during measurements. Therefore, we formulate photoacoustic image reconstruction in a variational framework where we add prior knowledge in terms of Total Generalized Variation. Using this prior knowledge, we can reduce measurement noise and improve the visibility of vessel structures.

Original language	English
Title of host publication	Proceedings Volume 10064, Photons Plus Ultrasound
Subtitle of host publication	Imaging and Sensing 2017
Volume	10064
DOIs	https://doi.org/10.1117/12.2254863
Publication status	Published - 2017
Event	Photons Plus Ultrasound: Imaging and Sensing 2017: SPIE BiOS 2017 - San Francisco, United States Duration: 28 Jan 2017 → 2 Feb 2017

Conference

Conference	Photons Plus Ultrasound: Imaging and Sensing 2017
Country/Territory	United States
City	San Francisco
Period	28/01/17 → 2/02/17

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10.1117/12.2254863

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@inproceedings{3eb0ef48a6344ea9a6a44a5652d5292d,

title = "Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data",

abstract = "In this work, we explore different reconstruction algorithms for photoacoustic image reconstruction of spatially resolved projection data. While the commonly used back-projected reconstruction is efficient and fast to compute, it cannot deal with noise that arises during measurements. Therefore, we formulate photoacoustic image reconstruction in a variational framework where we add prior knowledge in terms of Total Generalized Variation. Using this prior knowledge, we can reduce measurement noise and improve the visibility of vessel structures.",

author = "Kerstin Hammernik and Thomas Pock and Robert Nuster",

year = "2017",

doi = "10.1117/12.2254863",

language = "English",

volume = "10064",

booktitle = "Proceedings Volume 10064, Photons Plus Ultrasound",

note = "Photons Plus Ultrasound: Imaging and Sensing 2017 : SPIE BiOS 2017 ; Conference date: 28-01-2017 Through 02-02-2017",

}

TY - GEN

T1 - Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data

AU - Hammernik, Kerstin

AU - Pock, Thomas

AU - Nuster, Robert

PY - 2017

Y1 - 2017

N2 - In this work, we explore different reconstruction algorithms for photoacoustic image reconstruction of spatially resolved projection data. While the commonly used back-projected reconstruction is efficient and fast to compute, it cannot deal with noise that arises during measurements. Therefore, we formulate photoacoustic image reconstruction in a variational framework where we add prior knowledge in terms of Total Generalized Variation. Using this prior knowledge, we can reduce measurement noise and improve the visibility of vessel structures.

AB - In this work, we explore different reconstruction algorithms for photoacoustic image reconstruction of spatially resolved projection data. While the commonly used back-projected reconstruction is efficient and fast to compute, it cannot deal with noise that arises during measurements. Therefore, we formulate photoacoustic image reconstruction in a variational framework where we add prior knowledge in terms of Total Generalized Variation. Using this prior knowledge, we can reduce measurement noise and improve the visibility of vessel structures.

U2 - 10.1117/12.2254863

DO - 10.1117/12.2254863

M3 - Conference paper

VL - 10064

BT - Proceedings Volume 10064, Photons Plus Ultrasound

T2 - Photons Plus Ultrasound: Imaging and Sensing 2017

Y2 - 28 January 2017 through 2 February 2017

ER -

Variational Photoacoustic Image Reconstruction with Spatially Resolved Projection Data

Abstract

Conference

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