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Non-Rigid Volume Registration For DCE-MRI Using Deep Learning

Ferm, Jonatan LU (2021) In Master's Theses in Mathematical Sciences FMAM05 20202
Mathematics (Faculty of Engineering)
Abstract
In dynamically contrast enhanced magnetic resonance imaging (DCE-MRI) mul-tiple images are generated for a single scan. Before further analysis can be done,the images needs to be registered. Traditional registration techniques can yieldimpressive results, but are often very computationally expensive.In this thesis a neural network model using spatial transformer networks isproduced. This model aims to solve the registration problem, while not beingtoo computationally expensive.Several versions of the model are trained, on a limited data set of DCE-MRI scans of rabbit livers. The registration accuracy of these versions are thencompared to each other, and to two baseline methods: ANTs and Elastix.The model is shown – with a similarity metric... (More)
In dynamically contrast enhanced magnetic resonance imaging (DCE-MRI) mul-tiple images are generated for a single scan. Before further analysis can be done,the images needs to be registered. Traditional registration techniques can yieldimpressive results, but are often very computationally expensive.In this thesis a neural network model using spatial transformer networks isproduced. This model aims to solve the registration problem, while not beingtoo computationally expensive.Several versions of the model are trained, on a limited data set of DCE-MRI scans of rabbit livers. The registration accuracy of these versions are thencompared to each other, and to two baseline methods: ANTs and Elastix.The model is shown – with a similarity metric and visual inspection – to reg-ister the volumes less accurately than ANTs, but more accurately than Elastix.Notably this is done with orders of magnitude less computational expense aftertraining (Less)
Please use this url to cite or link to this publication:
author
Ferm, Jonatan LU
supervisor
organization
course
FMAM05 20202
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3447-2021
ISSN
1404-6342
other publication id
2021:E28
language
English
id
9062388
date added to LUP
2021-08-18 17:13:34
date last changed
2021-08-18 17:13:34
@misc{9062388,
  abstract     = {{In dynamically contrast enhanced magnetic resonance imaging (DCE-MRI) mul-tiple images are generated for a single scan. Before further analysis can be done,the images needs to be registered. Traditional registration techniques can yieldimpressive results, but are often very computationally expensive.In this thesis a neural network model using spatial transformer networks isproduced. This model aims to solve the registration problem, while not beingtoo computationally expensive.Several versions of the model are trained, on a limited data set of DCE-MRI scans of rabbit livers. The registration accuracy of these versions are thencompared to each other, and to two baseline methods: ANTs and Elastix.The model is shown – with a similarity metric and visual inspection – to reg-ister the volumes less accurately than ANTs, but more accurately than Elastix.Notably this is done with orders of magnitude less computational expense aftertraining}},
  author       = {{Ferm, Jonatan}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Non-Rigid Volume Registration For DCE-MRI Using Deep Learning}},
  year         = {{2021}},
}