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Bayesian Deep Learning Methods in Magnetic Resonance Imaging (MRI) and Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MRI).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Deep Learning Methods in Magnetic Resonance Imaging (MRI) and Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MRI)./
Author:	Leynes, Andrew Palmera.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	160 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968811
ISBN:	9798209908753

Bayesian Deep Learning Methods in Magnetic Resonance Imaging (MRI) and Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MRI).
Leynes, Andrew Palmera.

Bayesian Deep Learning Methods in Magnetic Resonance Imaging (MRI) and Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MRI). - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 160 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of California, San Francisco, 2022.

This item must not be sold to any third party vendors.

Medical image reconstruction is the process of reproducing an image of an object from the measurements produced from a scanner through some physical process. Deep image reconstruction, which is utilizing deep learning in image reconstruction, has surpassed what is possible compared to classical approaches. However, one requirement of deep learning is a large dataset that covers as many cases as possible and covers different demographics, disease conditions, hardware configurations, and acquisition schemes. Thus, a major limiting factor in deep learning in medical image reconstruction is the training data available. In this dissertation, I address the challenge of imperfect and small data in deep learning. This is addressed through the use of Bayesian deep learning. Bayesian deep learning allows a practitioner to estimate uncertainty to quantify possible sources of error and reduce the tendency of models to overfit. This is used in two application domains: simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) attenuation correction and in sub-Nyquist accelerated MRI. The use of uncertainty in PET/MRI attenuation correction has allowed the correction of intrinsic dataset errors due to bowel air movement, lack of bone signal, misregistration, and metal implant artifacts. The use of uncertainty in sub-Nyquist accelerated MRI has reduced the overfitting behavior of reconstruction models and allowed for higher possible acceleration factors.

ISBN: 9798209908753Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Bayesian deep learning

Bayesian Deep Learning Methods in Magnetic Resonance Imaging (MRI) and Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging (PET/MRI).
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300 $a 160 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Larson, Peder.
502 $a Thesis (Ph.D.)--University of California, San Francisco, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Medical image reconstruction is the process of reproducing an image of an object from the measurements produced from a scanner through some physical process. Deep image reconstruction, which is utilizing deep learning in image reconstruction, has surpassed what is possible compared to classical approaches. However, one requirement of deep learning is a large dataset that covers as many cases as possible and covers different demographics, disease conditions, hardware configurations, and acquisition schemes. Thus, a major limiting factor in deep learning in medical image reconstruction is the training data available. In this dissertation, I address the challenge of imperfect and small data in deep learning. This is addressed through the use of Bayesian deep learning. Bayesian deep learning allows a practitioner to estimate uncertainty to quantify possible sources of error and reduce the tendency of models to overfit. This is used in two application domains: simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) attenuation correction and in sub-Nyquist accelerated MRI. The use of uncertainty in PET/MRI attenuation correction has allowed the correction of intrinsic dataset errors due to bowel air movement, lack of bone signal, misregistration, and metal implant artifacts. The use of uncertainty in sub-Nyquist accelerated MRI has reduced the overfitting behavior of reconstruction models and allowed for higher possible acceleration factors.
590 $a School code: 0034.
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650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
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653 $a Deep learning
653 $a Image reconstruction
653 $a MRI
653 $a PET/MRI
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