Focus Mutual Information for medical image alignment in dentistry, orthodontics and craniofacial ...

193 Pages·2009·11.01 MB·English

Focus Mutual Information
for medical image alignment in
dentistry, orthodontics and craniofacial surgery
W. Jacquet
September 25, 2009
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Contents
1 Introduction 3
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . 8
2 Intrinsic image alignment methods 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Intrinsic image alignment methods . . . . . . . . . . . . . . . 12
2.3 Information theory and image alignment . . . . . . . . . . . . 13
2.3.1 Similarity measure image alignment . . . . . . . . . . . 13
2.3.2 Mutual information image alignment . . . . . . . . . . 14
2.3.3 Information theory applied to image alignment . . . . . 17
2.4 Statistical approach to image alignment . . . . . . . . . . . . . 21
2.4.1 Statistical alignment . . . . . . . . . . . . . . . . . . . 21
2.4.2 Regression model . . . . . . . . . . . . . . . . . . . . . 22
2.4.3 Error in variables model . . . . . . . . . . . . . . . . . 25
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Focus Mutual Information alignment 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Focus Mutual Information with respect to a density . . . . . . 28
3.3 Focus Mutual Information alignment . . . . . . . . . . . . . . 29
3.4 Approximation/estimation of FMI . . . . . . . . . . . . . . . . 31
3.4.1 Interpolation . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.2 Transformation . . . . . . . . . . . . . . . . . . . . . . 41
3.5 Focus distribution . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.2 Focus through rough indication of alignment structures 44
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3.5.3 Focus through indicative points incorporating a known
topology . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.4 Focus through feature points . . . . . . . . . . . . . . . 46
3.5.5 Focus through indication of longitudinal or volumetric
features . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5.6 Image based focus . . . . . . . . . . . . . . . . . . . . 48
3.5.7 Segmentation based focus . . . . . . . . . . . . . . . . 49
3.5.8 Reconstruction based focus . . . . . . . . . . . . . . . . 51
3.5.9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Optimization of FMI 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Local optimization strategies . . . . . . . . . . . . . . . . . . . 56
4.3 Global optimization strategies . . . . . . . . . . . . . . . . . . 61
4.3.1 Response surface optimization . . . . . . . . . . . . . . 63
4.3.2 Radial Basis Function global optimization . . . . . . . 64
4.3.3 Kriging global optimization . . . . . . . . . . . . . . . 67
4.3.4 Direct search optimization . . . . . . . . . . . . . . . . 70
4.3.5 One dimensional Direct algorithm . . . . . . . . . . . . 74
4.3.6 Multidimensional Direct algorithm . . . . . . . . . . . 78
4.4 Implementation specifics . . . . . . . . . . . . . . . . . . . . . 81
4.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . 84
5 Robustness of FMI 87
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2 Robustness of FMI versus MI . . . . . . . . . . . . . . . . . . 87
5.2.1 Material and methods . . . . . . . . . . . . . . . . . . 88
5.2.2 Robustness of FMI versus ROI . . . . . . . . . . . . . 91
5.2.3 Overall performance FMI versus ROI . . . . . . . . . . 91
5.2.4 Robustness to deviation in focus point indication . . . 91
5.3 Robustness with respect to changes in overlap . . . . . . . . . 92
5.3.1 Soft feature point alignment . . . . . . . . . . . . . . . 93
5.3.2 Numerical field of view experiment . . . . . . . . . . . 96
5.3.3 In vivo experiments . . . . . . . . . . . . . . . . . . . . 98
5.4 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . 104
CONTENTS v
6 Applications 107
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2 Dentistry:
2D-2D Intra-Oral X-rays . . . . . . . . . . . . . . . . . . . . . 107
6.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2.2 Material and methods . . . . . . . . . . . . . . . . . . 109
6.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.3 Orthodontics:
2D-2D cephalometric X-ray . . . . . . . . . . . . . . . . . . . 118
6.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 118
6.3.2 Material and methods . . . . . . . . . . . . . . . . . . 120
6.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 124
6.4 Craniofacial Surgery: 3D-3D CBCT . . . . . . . . . . . . . . . 126
6.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 126
6.4.2 Material and methods . . . . . . . . . . . . . . . . . . 127
6.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . 133
7 Prospects for further research 135
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.2 Theoretical and conceptual exploration . . . . . . . . . . . . . 135
7.2.1 Research questions . . . . . . . . . . . . . . . . . . . . 141
7.3 Robustness and comparison . . . . . . . . . . . . . . . . . . . 142
7.3.1 Steps to be taken . . . . . . . . . . . . . . . . . . . . . 143
7.3.2 Research questions . . . . . . . . . . . . . . . . . . . . 144
7.4 Clinical validation, applicability automation . . . . . . . . . . 145
7.4.1 Alignment of intra-oral images . . . . . . . . . . . . . . 146
7.4.2 Alignment of lateral cephalometric images . . . . . . . 149
7.4.3 Alignment of CBCT images . . . . . . . . . . . . . . . 156
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8 Contributions and conclusions 159
8.0.1 Chapter 2: Intrinsic image alignment methods . . . . . 159
8.0.2 Chapter 3: Focus Mutual Information alignment . . . . 160
8.0.3 Chapter 4: Optimization of FMI . . . . . . . . . . . . 161
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8.0.4 Chapter 5: Robustness . . . . . . . . . . . . . . . . . . 161
8.0.5 Chapter 6: Applications . . . . . . . . . . . . . . . . . 162
Glossary 166
Index 170
Samenvatting 185
Acknowledgments
Today’s research is more and more the result of collaboration between people
in a wide variety of research areas and cannot take place without openness,
trust, perseverance and mutual respect. A great many people were involved
in the process and work that led to this dissertation. I hereby want to pay
tribute to all of those involved.
First of all I want thank Prof. Dr. Pieter de Groen for the many inspiring
talks and his loyalty in difficult times. My special gratitude goes towards
Prof. Dr. Ir. Edgard Nyssen for the close and intense collaboration over the
last four years, most often after working hours, and to Prof. Dr. Peter Bot-
tenberg, for his decisiveness, diplomacy and creativity. I am very grateful
to Prof. Dr. Bart Vande Vannet, who introduced me to many people from
all over the world in the field of orthodontics. Through him a collaboration
with the university of Hasselt could come into being. Without their much
valued contribution this work could never have been accomplished.
I also want to thank Dr. Gazagnes, Marie-Dominique, “Chef de clinique
adjoint”oftheneurologicdepartmentofHospitalBrugmann, wholivesinmy
street and brought me into contact with Dr. Mathieu Jacques, former head of
theradiologydepartmentofHospitalBrugmannwhoprovidedusconsecutive
head CT images. These images were the first 3D material available to us.
Prof. Dr. Guy de Pauw and Prof. Dr. Bart Vande Vannet let me work
on lateral cephalometric images. I hope that in the near future we will be
able to study growth prediction. Panoramic images were obtained through
Prof. Dr. Roberto Cleymaet, and all dental images were provided by Prof.
Dr. Peter Bottenberg.
Thank you, Prof. Dr. Stan Politis, for allowing me to work in Hasselt and
give me access to a CBCT scanner. Thanks also to Yi Sun for the great
cooperation. I admire the initiatives taken at the university of Hasselt and
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enjoyed the enlightening lectures organized regarding CBCT imaging.
My gratitude naturally, also goes to my family and friends, who stood by
mysidewheneverIneededthem. ThankstoProf.Dr.TomvanWingandDr.
Ir. Nico Deblauwe who, in different stages of the doctoral work, listened and
allowed me to structure my thoughts and efforts. Thanks to my colleagues
and friends, Prof. Dr. Gert Sonck, Eddy Carette, and Jan Vromman for the
discussionsaboutsocietyandourexistence. Myadmirationandgratitudeto:
my beloved wife Marianne Stevens, for all crazy adventures and for making
it possible, my eldest son Ramses (10) for the discussions, questions and
wanting to understand – you are growing fast, my daughter Hera (9) for
her boldness and cuddles, my son Merlijn (4) for the fantastic imaginative
stories and endless jokes, your cheerfulness – rascal, my little boy Egon (1.6)
for being daddy’s child, and Jeff Stevens, my father in law, who helped us
whenever we needed him.
Wolfgang Jacquet
Chapter 1
Introduction
Medicalimagingisacornerstoneindiagnosis, treatmentplanning, andfollow
up of medical conditions in dentistry, orthodontics and craniofacial surgery.
Over theperiod of days, monthsor years several images are madeof a patient
using a variety of imaging techniques. Some of those images are made to see
the evolution of a condition or treatment effects. Some are made with differ-
enttechniquestovisualizedifferentaspectsofthesameanatomicalstructure.
A clever digital combination of two successive images may provide much
more information than the comparison with the naked eye can obtain from
both images separately. A first step in the combination of information is
the alignment of the images with respect to well chosen stable anatomical
structures.
In dental practice several possible areas of application can be distin-
guished, e.g. detection and evolution of caries, failure of restorations, bone
loss around implants, asceptic loosening of implants, bone and root resorp-
tion. Superimposition of images in orthodontics has a long tradition for
evaluation changes due to e.g. growth and treatment and is current practice
[97]. In dental and maxilofacial imaging traditionally projection imaging has
been the routine, especially for intra-oral applications. Recently, Cone Beam
Computed Tomography (CBCT) has been introduced to provide accurate 3D
images of resolution and quality allowing for application in orthodontics and
craniofacial surgery [114].
In a wide variety of clinical applications, the criteria of choice to align im-
ages are pixel/voxel based similarity measures, such as Mutual Information
(MI) [85]. The invariance of MI to permutation of the gray values makes
it well suited for alignment of images acquired using different modalities.
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4 CHAPTER 1. INTRODUCTION
Moreover, MI alignment is robust to differences in image quality and ex-
posure time. In what follows the MI criterion and alignment process will
be adapted to the specific needs in dentistry, orthodontics and craniofacial
surgery. Only rigid transformations, which allow correct measurement, and
affine transformations, as first order approximation, will be explored.
1.1 Motivation
Digital medical image processing, and in particular alignment and fusion of
images is not widespread in current everyday dental practice. In literature
a wide variety of techniques is introduced and discussed to align consecutive
intra-oral X-ray radiographs. Although promising, these methods did not
reach everyday dental diagnostic procedures at present. Most often image
quality and lack of standardization in image acquisition limits the perfor-
mance of the superimposition methods. This uncertainty about the perfor-
mance, together with the expertise and input required from the practitioner
by current alignment techniques might explain why these techniques did not
leave the experimental phase. Much has to be done to improve the perfor-
mance, and reduce the nature and the amount of required input.
The superimposition of consecutive lateral cephalometric images are cur-
rent practice in orthodontics to evaluate growth, evolution of pathologies,
and treatment effect – see e.g. Ongkosuwito et al. [97]. The alignment is
most often based on tracing and indication of cephalometric landmarks. The
tracing, is done manually or with the help of dental software after manual
indication of anatomical landmarks. Even with dental software, it remains
labor intensive, and the accuracy depends critically on the skills of the prac-
titioner – see e.g. Gliddon et al. [40] and Santoro et al. [108]. At present,
automated detection of landmarks cannot be expected to replace the man-
ual indication of landmarks for clinical purposes due to a lack of accuracy –
see Leonardi et al. [79]. A Mutual Information approach to the alignment
problem could provide both the desired accuracy and the ease of use.
The future in imaging for orthodontic treatment and craniofacial surgery
is three-dimensional. Correct superimposition of 3D Cone Beam Computed
Tomography (CBCT) images allows accurate measurement of evolution and
treatment outcomes, avoiding projection errors. Landmark based techniques
are available in practice. Mutual Information methods based on selection
of parts of the image have been proposed and explored by Cevidanes et al.