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Bio Imaging and Signal Processing

Blind polychromatic X-Ray CT reconstruction from Poisson measurements


X-ray sources are polychromatic. Ignoring this fact when performing reconstruction leads to artifacts, such as cupping and streaking, in reconstructed images. We first propose a new model parameterization that allows for blind correction of these artifacts and then develop reconstruction algorithms based on this parameterization.

Here, blind correction means that we do not know
- incident spectrum (which is an X-ray machine characteristic) and
- mass attenuation (inspected material).

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Authors:
Renliang Gu, Aleksandar Dogandzic
Submitted On:
14 March 2016 - 6:04am
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poster.pdf

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[1] Renliang Gu, Aleksandar Dogandzic, "Blind polychromatic X-Ray CT reconstruction from Poisson measurements", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/668. Accessed: Nov. 25, 2017.
@article{668-16,
url = {http://sigport.org/668},
author = {Renliang Gu; Aleksandar Dogandzic },
publisher = {IEEE SigPort},
title = {Blind polychromatic X-Ray CT reconstruction from Poisson measurements},
year = {2016} }
TY - EJOUR
T1 - Blind polychromatic X-Ray CT reconstruction from Poisson measurements
AU - Renliang Gu; Aleksandar Dogandzic
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/668
ER -
Renliang Gu, Aleksandar Dogandzic. (2016). Blind polychromatic X-Ray CT reconstruction from Poisson measurements. IEEE SigPort. http://sigport.org/668
Renliang Gu, Aleksandar Dogandzic, 2016. Blind polychromatic X-Ray CT reconstruction from Poisson measurements. Available at: http://sigport.org/668.
Renliang Gu, Aleksandar Dogandzic. (2016). "Blind polychromatic X-Ray CT reconstruction from Poisson measurements." Web.
1. Renliang Gu, Aleksandar Dogandzic. Blind polychromatic X-Ray CT reconstruction from Poisson measurements [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/668

PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY


In this dissertation, we propose the first, to the best of our knowledge, PCA based algorithm to noninvasively recognize and classify different temporal stages of brain tumors given a large time series of MRI images. We propose an algorithm that addresses the challenging task of classifying stage of tumor over period of time while the tumor is being treated with VB-111 virotherapy. Our approach treats stage tumor recognition as a two-dimensional recognition problem. Detecting the stage of the tumor is a crucial prognosis factor for predicting the progression of cancer and patient survival.

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Submitted On:
16 November 2016 - 9:38am
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[1] , "PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/570. Accessed: Nov. 25, 2017.
@article{570-15,
url = {http://sigport.org/570},
author = { },
publisher = {IEEE SigPort},
title = {PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY},
year = {2015} }
TY - EJOUR
T1 - PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/570
ER -
. (2015). PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY. IEEE SigPort. http://sigport.org/570
, 2015. PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY. Available at: http://sigport.org/570.
. (2015). "PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY." Web.
1. . PCA BASED ALGORITHM FOR LONGITUDINAL BRAIN TUMOR STAGE CLASSIFICATION AND DYNAMICALMODELING OF TUMOR DECAY IN RESPONSE TO VB-111 VIROTHERAPY [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/570

A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG


Phase synchrony measures computed on electrophysiological signals play an important role in the assessment of cognitive and sensory processes. However, due to the effects of volume conduction false synchronization values may arise between time series. Measures such as the imaginary part of coherence (ImC), phase-lag index (PLI) and an enhanced version of it, the weighted PLI (WPLI) have been proposed in order to attenuate the effects of volume conduction.

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23 February 2016 - 1:44pm
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A Time-Frequency Based Bivariate Synchrony Measure for ReducingVolumeConduction.pdf

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[1] , "A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/558. Accessed: Nov. 25, 2017.
@article{558-15,
url = {http://sigport.org/558},
author = { },
publisher = {IEEE SigPort},
title = {A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG},
year = {2015} }
TY - EJOUR
T1 - A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/558
ER -
. (2015). A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG. IEEE SigPort. http://sigport.org/558
, 2015. A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG. Available at: http://sigport.org/558.
. (2015). "A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG." Web.
1. . A Time-Frequency Based Bivariate Synchrony Measure for Reducing Volume Conduction Effects in EEG [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/558

Brain Functional Connectivity Analysis Using Mutual Information

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Authors:
Zhe Wang, Ahmed Alahmadi, David C. Zhu
Submitted On:
23 February 2016 - 1:44pm
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presentation.pdf

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[1] Zhe Wang, Ahmed Alahmadi, David C. Zhu, "Brain Functional Connectivity Analysis Using Mutual Information", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/388. Accessed: Nov. 25, 2017.
@article{388-15,
url = {http://sigport.org/388},
author = {Zhe Wang; Ahmed Alahmadi; David C. Zhu },
publisher = {IEEE SigPort},
title = {Brain Functional Connectivity Analysis Using Mutual Information},
year = {2015} }
TY - EJOUR
T1 - Brain Functional Connectivity Analysis Using Mutual Information
AU - Zhe Wang; Ahmed Alahmadi; David C. Zhu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/388
ER -
Zhe Wang, Ahmed Alahmadi, David C. Zhu. (2015). Brain Functional Connectivity Analysis Using Mutual Information. IEEE SigPort. http://sigport.org/388
Zhe Wang, Ahmed Alahmadi, David C. Zhu, 2015. Brain Functional Connectivity Analysis Using Mutual Information. Available at: http://sigport.org/388.
Zhe Wang, Ahmed Alahmadi, David C. Zhu. (2015). "Brain Functional Connectivity Analysis Using Mutual Information." Web.
1. Zhe Wang, Ahmed Alahmadi, David C. Zhu. Brain Functional Connectivity Analysis Using Mutual Information [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/388

IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS

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Authors:
Yehoshua Y. Zeevi
Submitted On:
23 February 2016 - 1:44pm
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PaperPresentation.pdf

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[1] Yehoshua Y. Zeevi, "IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/353. Accessed: Nov. 25, 2017.
@article{353-15,
url = {http://sigport.org/353},
author = {Yehoshua Y. Zeevi },
publisher = {IEEE SigPort},
title = {IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS},
year = {2015} }
TY - EJOUR
T1 - IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS
AU - Yehoshua Y. Zeevi
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/353
ER -
Yehoshua Y. Zeevi. (2015). IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS. IEEE SigPort. http://sigport.org/353
Yehoshua Y. Zeevi, 2015. IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS. Available at: http://sigport.org/353.
Yehoshua Y. Zeevi. (2015). "IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS." Web.
1. Yehoshua Y. Zeevi. IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/353

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