Table Of ContentALGORITHMS FOR MODELING JYRKI
ALAKUIJALA
ANATOMIC AND TARGET
VOLUMES IN IMAGE-GUIDED Department of Electrical Engineering,
University of Oulu
NEUROSURGERY AND
RADIOTHERAPY
OULU 2001
JYRKI ALAKUIJALA
ALGORITHMS FOR MODELING
ANATOMIC AND TARGET VOLUMES
IN IMAGE-GUIDED NEUROSURGERY
AND RADIOTHERAPY
Academic Dissertation to be presented with the assent of
the Faculty of Technology, University of Oulu, for public
discussion in Raahensali (Auditorium L 10), Linnanmaa, on
December 7th, 2001, at 12 noon.
OULUN YLIOPISTO, OULU 2001
Copyright © 2001
University of Oulu, 2001
Manuscript received 15 November 2001
Manuscript accepted 19 November 2001
Communicated by
Professor Hannu Eskola
Doctor Mikko Tenhunen
ISBN 951-42-6574-2 (URL: http://herkules.oulu.fi/isbn9514265742/)
ALSO AVAILABLE IN PRINTED FORMAT
ISBN 951-42-6573-4
ISSN 0355-3213 (URL: http://herkules.oulu.fi/issn03553213/)
OULU UNIVERSITY PRESS
OULU 2001
Alakuijala, Jyrki, Algorithms for modeling anatomic and target volumes in image-
guided neurosurgery and radiotherapy
Department of Electrical Engineering, University of Oulu, P.O.Box 4500, FIN-90014 University of
Oulu, Finland
2001
Oulu, Finland
(Manuscript received 15 November 2001)
Abstract
The use of image-guidance in surgery and radiotherapy has significantly improved patient outcome
in neurosurgery and radiotherapy treatments. This work developed volume definition and verification
techniques for image-guided applications, using a number of algorithms ranging from image
processing to visualization.
Stereoscopic visualization, volumetric tumor model overlaid on an ultrasound image, and
visualization of the treatment geometry were experimented with on a neurosurgical workstation.
Visualization and volume definition tools were developed for radiotherapy treatment planning
system.
The magnetic resonance inhomogeneity correction developed in this work, possibly the first
published data-driven method with wide applicability, automatically mitigates the RF field
inhomogeneity artefact present in magnetic resonance images. Correcting the RF inhomogeneity
improves the accuracy of the generated volumetric models.
Various techniques to improve region growing are also presented. The simplex search method and
combinatory similarity terms were used to improve the similarity function with a low additional
computational cost and high yield in region correctness. Moreover, the effects of different priority
queue implementations were studied.
A fast algorithm for calculating high-quality digitally reconstructed radiographs has been
developed and shown to better meet typical radiotherapy needs than the two alternative algorithms.
A novel visualization method, beam's light view, is presented. It uses texture mapping for projecting
the fluence of a radiation field on an arbitrary surface.
This work suggests several improved algorithms for image processing, segmentation, and
visualization used in image-guided treatment systems. The presented algorithms increase the
accuracy of image-guidance, which can further improve the applicability and efficiency of image-
guided treatments.
Keywords: medical technology, image processing, image processing, computer-assisted ra-
diotherapy, neurosurgery, medical electronics, image segmentation, image-guidance, radi-
otherapy, surgery, visualization
To Anniina, Minttu, and Milla
Acknowledgements
This research work was first started at the Department of Neurosurgery at Oulu University
Hospital during the years 1991-1993. The work was continued in Dosetek Oy, Espoo, and
in University of Oulu, Department of Electrical Engineering, Computer Laboratory during
years 1993-2001. I owe my warmest thanks to my supervisor, Professor Matti Pietikäinen
for guidance during this research work. I wish to thank Professor John Koivukangas for
providing the initial environment for this work and for the trust and support giving much
of the momentum for this thesis.
I am very grateful to all my colleagues who have always been a pleasure to work with.
Especially, I wish to thank Dr. Jarkko Oikarinen for tutoring me into programming and
scientific writing. Hannu Helminen and Sami Sallinen provided extensive support and
delight with their great wit and reason. Dr. Xiaoyou Ying was always in good mood and a
pleasure to work with. His dedication and international experience gave me a more
comprehensive perspective to scientific work. Professor Yrjö Louhisalmi taught me a lot
on the process flow and user requirements for surgical workstations. This work would
have been a great deal more laborious without his founding work at the Department of
Neurosurgery.
I want to express my special thanks to Pekka Aalto and Harri Puurunen for maintaining
and promoting an open and research-oriented atmosphere at Dosetek Oy (now renamed
Varian Medical Systems Finland Oy after a fusion with Varian Medical Systems, Inc.).
The lively discussions, challenging product requirements, and vast economical
possibilities gave a lot of inspiration for building faster and more accurate algorithms. I
got a lot of stimulus for this work by seeing that successful research could be immediately
transferred to help people. I wish to thank all my colleagues at Dosetek for this valuable
support. Especially, I am expressing thanks and appreciation to Dr. Katja Pesola, Dr.
Jarkko Heinonen, and Dr. Corey Zankowski for guiding and commenting this work. I wish
to thank Joakim Laitinen for always having time and patience for thorough discussion on
my work. His support was indispensable for this work.
I want to thank Professor Hannu Eskola and Dr. Marcel van Herk for the pre-
examination of this thesis.
I am also indebted to my parents, Eeva-Liisa and Risto Alakuijala, who gave their
continuous support and took interest in my education.
Still, the most important supporting role belongs to my family. I want to thank my wife,
Anniina, for her understanding and support during this long-lasting project. A researcher
is unlikely the best mate; many hours together were lost because of this work. I would also
like to thank our children for their patience. “Daddy works on PhD thesis” was among
Milla’s first sentences, which was a clear indication on slowing down writing. Minttu and
Milla have given me a new viewpoint on what is really important.
This work was financially supported by Jenny and Antti Wihuri Foundation and the
Progress of Technology Foundation.
Espoo, August 2001 Jyrki Alakuijala
List of abbreviations and acronyms
3d Three-dimensional.
BEV Beam’s Eye View is a commonly used technique in radiotherapy treatment
planning to visualize patient geometry from the radiation source.
CART Computer Aided Radiation Therapy project in Nordic countries during 1985-
1987.
CCD Charge-Coupled Device is an imaging device commonly used in video and
digital cameras.
CT Computed Tomography is a volumetric imaging technique that uses X-ray
radiation.
DICOM Digital Imaging and Communications in Medicine. The most commonly used
network protocol for computerized devices to transfer medical image data
from one device to another.
DRR Digitally Reconstructed Radiograph is an image reconstruction technique that
calculates a simulated X-ray image through a CT volume image.
DVH Dose Volume Histogram is a one-dimensional mapping of volume for
radiation dose level.
eV Electron volt. A unit of energy used for particles produced by radiotherapy
accelerators.
Gy Gray is the SI unit of absorbed radiation dose, one joule per kilogram of tissue.
LCD Liquid Crystal Display. A planar display device.
MEG Magnetoencephalography. Magnetic imaging of brain activity.
MeV Mega electron Volt, 106 eV. A unit of energy used for particles produced by
radiotherapy accelerators.
MR Magnetic Resonance.
MRI Magnetic Resonance Imaging.
P1, P2, P3 Symbols that are used for similarity modes in the competitive region growing
algorithm presented in this thesis. These modes are derived from progressive
similarity mode.
PACS Picture Archival and Communication System.
PET Positron Emission Tomography.
PTV Planning Target Volume. The PTV is often constructed from the clinical target
volume by adding a margin around it.
RF Radiofrequency.
S1, S2, S3 Symbols that are used for similarity modes in the competitive region growing
algorithm presented in this thesis. These modes are derived from static
similarity mode.
SPECT Single-Photon Emission Computed Tomography.
STL Standard Template Library. A collection of data types and algorithms for C++
programming.
T1, T2 Relaxation parameters in MR imaging.
Voxel An elementary cell in three-dimensional images analogous to a 2d pixel.
Description:Especially, I wish to thank Dr. Jarkko Oikarinen for tutoring me into programmingand Jarkko Heinonen, and Dr. Corey Zankowski for guiding and commenting this work.
