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Methods to Automatically Transform MR Images of the Brain into the AC-PC Coordinate System

Larsson, Malin LU (2024) In Master's Theses in Mathematical Sciences FMAM05 20241
Mathematics (Faculty of Engineering)
Abstract
One way to reduce the growing workload for radiologists is to automate the trans-
formation of 3D MR images of the brain into the AC-PC coordinate system. In this
thesis, both non-deformable and deformable image registration methods were used
to register images to a template in the desired space, the AC-PC coordinate system.
First, three non-deformable transformations were used together with three different
templates and two similarity metrics. To transform an image to the AC-PC coordin-
ate system, only the rotation matrix and translation vector are needed. Thus, the
transformation matrix and the landmarks (AC, PC and MS) were used to extract the
rotation matrix and translation vector from non-deformable and deformable image re-
... (More)
One way to reduce the growing workload for radiologists is to automate the trans-
formation of 3D MR images of the brain into the AC-PC coordinate system. In this
thesis, both non-deformable and deformable image registration methods were used
to register images to a template in the desired space, the AC-PC coordinate system.
First, three non-deformable transformations were used together with three different
templates and two similarity metrics. To transform an image to the AC-PC coordin-
ate system, only the rotation matrix and translation vector are needed. Thus, the
transformation matrix and the landmarks (AC, PC and MS) were used to extract the
rotation matrix and translation vector from non-deformable and deformable image re-
gistration, respectively. The combinations of transformation, template and similarity
metric were evaluated with four different metrics: distance to AC, angle in sagittal,
coronal, and axial plane. The non-deformable transformations were rigid, similarity
and affine transformation, while the templates were Colin27 T1- and T2-weight, and
MNI305. The two similarity metrics used were Mattes mutual information, which is a
modification to mutual information, and cross correlation. The best combination in the
non-deformable case was the affine transform with Colin27 with T1-weight and Mattes
mutual information. The average angular errors for this combination were 3.45°, 0.87°
and 1.18° for the angles in the sagittal, coronal, and axial plane, respectively. The
average distance error to AC was 3.11mm. The second part was the development of a
deformable transformation which was semi-based on the first part. This because it used
the best template and the best similarity metric from the first part. The deformable
transformation was symmetric normalization (SyN) with Colin27 with T1-weight and
Mattes mutual information. The average angular errors for the deformable method
were 1.84°, 1.24° and 1.12° for the angles in the sagittal, coronal, and axial plane,
respectively. The average distance error to AC was 1.47mm. The average time for a
non-deformable registration was 18 seconds, and 320 seconds for a deformable regis-
tration. Except for the time, the deformable method was on par with state-of-the-art
machine learning methods from the literature. The non-deformable method performed
worse than the methods from the literature, except when comparing the time for one
registration. (Less)
Please use this url to cite or link to this publication:
author
Larsson, Malin LU
supervisor
organization
alternative title
Metoder för att automatiskt transformera MR bilder av hjärnan till AC-PC koordinatsystemet
course
FMAM05 20241
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3533-2024
ISSN
1404-6342
other publication id
2024:E20
language
English
id
9160025
date added to LUP
2024-06-28 16:00:17
date last changed
2024-06-28 16:00:17
@misc{9160025,
  abstract     = {{One way to reduce the growing workload for radiologists is to automate the trans-
formation of 3D MR images of the brain into the AC-PC coordinate system. In this
thesis, both non-deformable and deformable image registration methods were used
to register images to a template in the desired space, the AC-PC coordinate system.
First, three non-deformable transformations were used together with three different
templates and two similarity metrics. To transform an image to the AC-PC coordin-
ate system, only the rotation matrix and translation vector are needed. Thus, the
transformation matrix and the landmarks (AC, PC and MS) were used to extract the
rotation matrix and translation vector from non-deformable and deformable image re-
gistration, respectively. The combinations of transformation, template and similarity
metric were evaluated with four different metrics: distance to AC, angle in sagittal,
coronal, and axial plane. The non-deformable transformations were rigid, similarity
and affine transformation, while the templates were Colin27 T1- and T2-weight, and
MNI305. The two similarity metrics used were Mattes mutual information, which is a
modification to mutual information, and cross correlation. The best combination in the
non-deformable case was the affine transform with Colin27 with T1-weight and Mattes
mutual information. The average angular errors for this combination were 3.45°, 0.87°
and 1.18° for the angles in the sagittal, coronal, and axial plane, respectively. The
average distance error to AC was 3.11mm. The second part was the development of a
deformable transformation which was semi-based on the first part. This because it used
the best template and the best similarity metric from the first part. The deformable
transformation was symmetric normalization (SyN) with Colin27 with T1-weight and
Mattes mutual information. The average angular errors for the deformable method
were 1.84°, 1.24° and 1.12° for the angles in the sagittal, coronal, and axial plane,
respectively. The average distance error to AC was 1.47mm. The average time for a
non-deformable registration was 18 seconds, and 320 seconds for a deformable regis-
tration. Except for the time, the deformable method was on par with state-of-the-art
machine learning methods from the literature. The non-deformable method performed
worse than the methods from the literature, except when comparing the time for one
registration.}},
  author       = {{Larsson, Malin}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Methods to Automatically Transform MR Images of the Brain into the AC-PC Coordinate System}},
  year         = {{2024}},
}