Research and development of SPECT and SPECT CT images segmentation software for automatic detection and extraction of brain tumors using ITK, VTK, Qt (Luận văn thạc sĩ)

MINISTRY OF EDUCATION
AND TRAINING

VIETNAM ACADEMY OF
SCIENCE AND TECHNOLOGY

GRADUATE UNIVERSITY OF SCIENCE AND TECHNOLOGY
-----------------------------

Ho Thi Thao

RESEARCH AND DEVELOPMENT OF SPECT AND
SPECT/CT IMAGES SEGMENTATION SOFTWARE FOR
AUTOMATIC DETECTION AND EXTRACTION OF BRAIN
TUMORS USING ITK, VTK, QT

MASTER THESIS: ATOMIC AND NUCLEAR PHYSICS

Hanoi - 2019


MINISTRY OF EDUCATION
AND TRAINING

VIETNAM ACADEMY OF
SCIENCE AND TECHNOLOGY

GRADUATE UNIVERSITY OF SCIENCE AND TECHNOLOGY
-----------------------------

Ho Thi Thao

RESEARCH AND DEVELOPMENT OF SPECT AND
SPECT/CT IMAGES SEGMENTATION SOFTWARE FOR
AUTOMATIC DETECTION AND EXTRACTION OF BRAIN
TUMORS USING ITK, VTK, QT

Major: Atomic and nuclear physics
Code: 60440106

MASTER THESIS: ATOMIC AND NUCLEAR PHYSICS

SUPERVISORS:
Dr. Phan Viet Cuong
MSc. Le Tuan Anh

Hanoi - 2019

i


Confirmation
This thesis was written on the basic of my research works carries out at
Institute of Physics, Vietnam Academy of Science and Technology under
the supervision of Dr. Phan Viet Cuong and MSc. Le Tuan Anh. All results
of other authors that are used in this thesis are cited correctly.

April 20, 2018
The author

Ho Thi Thao

ii


Acknowledgements
I would like to express my gratitude to all people who have helped and
inspired me during my study. This thesis would not have been possible
without those supports from many people.
First of all, I would like to thank my teachers, Dr. Phan Viet Cuong and
MSc Le Tuan Anh, Research and Development Center for Radiation
technology, Vietnam Atomic Energy Institute, for giving me the
opportunity to do within their group, for their guidance, support and
constant encouragement during the entire period of preparation of this
thesis. They had often pointed out the incompleteness of my work and
helped me to improve my understandings on each problem. They taught me
a lot about nuclear physics, nuclear medicine, coding and all academic and
non-academic matters. I have been extremely lucky to have supervisors
who cared so much about my work, my study and who responded to my
questions and queries so promptly. I have learned a lot of things from them,
and more importantly they showed me that everything can be done, just
keep hard working, keep big dream, keep big courage, keep going on, day
by day .
In addition, I would also like to thank all the members at Center for
Nuclear physics, Institute of Physics, Vietnam Academy of Science and
Technology, for providing the best possible environment for us to study
and research.
Finally, I would like to thank Mom, Dad, my brother, for their constant
love and support. My sister has helped me to diminish the fact of being
away from home by the long telephone calls spent laughing. I would also
like to thank my friends, Luan, Ha, my sister, Tan,… made my time at

VAST a lot more fun.
To everybody else who accompanied me throughout my time as a
student: Thank you!
Ho Thi Thao
iii


Abstract
Digital Imaging and Communications in Medicine (DICOM) exists
as a standard for handling, storing, printing, and transmitting information in
medical imaging. The DICOM files include not only the information of
images, but also contain a lot of medical-related information. Reading and
Process an image in DICOM format is an important issue for further image
processing and visualization. In the field of medical image processing,
detection of brain tumor from computed tomography (CT), magnetic
resonance (MRI), positron emission tomography (PET) or single-photon
emission computed tomography (SPECT) is a difficult task due to
complexity of the brain hence it is one of the top priority goals.
In this thesis, the author describes a new method which combines
four different steps including smoothing, Sobel edge detection, connected
component and finally region growing algorithms for locating and
extracting the various lesions in the brain. The computational algorithm
was implemented by INMOFEVV a new software which combines Insight
Toolkit (ITK) to process input image, Visualization Toolkit (VTK) to
display and Qt software development framework to build user interface.
The main function of software includes reading and displaying DICOM
images as well as performing advanced image processing. It helps to
improve quality and efficiency of the diseases diagnosis. The analysis
results indicate that the proposed method automatically and efficiently
detected the tumor region from brain medical images. It is very clear for

physicians to separate the abnormal from the normal surrounding tissue to
get a real identification of related area; improving quality and accuracy of
diagnosis, which would help to increase success possibility in treatment by
early detection of tumor as well as reducing surgical planning time.
Key Words: DICOM, Image Processing, ITK, VTK, QT.

ix


x

Table of contents

Acknowledgements ..................................................................................... iii
Abstract ....................................................................................................... ix
Table of contents ......................................................................................... x
Index of figures .......................................................................................... xii
List of acnonyms ....................................................................................... xvi
INTRODUTION .......................................................................................... 1
CHAPER 1. OVERVIEW .......................................................................... 5
1.1.
INTERACTION OF RADIATION WITH MATTER .............. 5
1.1.1. Interaction of photons with matter ..................................... 5
1.1.1.1. Types of photon interactions in matter .............................. 5
1.1.1.2. Attenuation of photons in matter ....................................... 6
1.1.2. Interaction of charged particles with matter ..................... 7
1.2.
SINGLE PHOTON EMISSION COMPUTED
TOMOGRAPHY AND COMPUTED TOMOGRAPHY ........................ 8
1.2.1. Single-photon Emission Computed Tomography .............. 8

1.2.1.1. Gamma camera .................................................................. 8
1.2.1.2. Single photon emission computed tomography (SPECT) 15
1.2.2. Computed tomography ....................................................... 18
1.2.3. Hybrid Imaging System: SPECT/CT............................... 23
CHAPTER 2. EXPERIMETAL AND METHODOLOGY .................. 27
2.1.
DICOM..................................................................................... 27
2.1.1. DICOM image ........................................................................ 27
2.1.2. DICOM Information Model .................................................. 27
2.2.
RECONSTRUCTION .............................................................. 28
2.2.1. Iterative Reconstruction Method .......................................... 29
2.2.2. Filtered Backprojection Method (FBP) ............................... 29
2.2.3. Filtering ................................................................................... 30
2.3.
TECHNOLOGIES ................................................................... 30
2.3.1. Build System - CMake ........................................................ 30
2.3.2. Source Code Libraries ........................................................ 31
2.3.3. Database ............................................................................... 31
2.4.
INITIAL RESULTS OF INMOFEVV SOFTWARE .............. 32
2.4.1. Fusion images ...................................................................... 32


xi

2.4.3. Surface and volume rendering ........................................... 34
2.4.4. Filters .................................................................................... 35
2.5.1. General description of proposed method.......................... 38
2.5.2. Preprocessing: Mean filter ................................................. 41

2.5.2.1. Factors affecting image quality CT, MRI scan................ 41
2.5.2.2. Mean filter ........................................................................ 42
2.5.3. Sobel edge detection ............................................................ 42
2.5.4. Segmentation ....................................................................... 43
2.5.4.1. Connected Component Labeling ...................................... 43
2.5.4.2. Region growing by Confidence Connected...................... 44
3.1. CHOOSING SUITABLE FILTER FILTER FOR
PREPROCESSING STEP ................................................................ 47
3.2. CHOOSING SUITABLE METHOD FOR EDGE
DETECTION STEP .......................................................................... 50
3.3. APPLYING PROPOSED METHOD FOR PHANTOM
GAMEX 463....................................................................................... 50
3.3.1. Method and test results on phantom ................................... 51
3.3.2. Result evaluation ................................................................. 57
3.4. APPLYING PROPOSED METHOD FOR BRAIN
IMAGES............................................................................................. 59
CONCLUSIONS ....................................................................................... 67
REFERENCES .......................................................................................... 69


xii

Index of figures

Figure 1.1. Predominant types of interaction for a range of incident photon
energies and absorber atomic numbers. .............................................. 6
Figure 1.2. Attenuation.......................................................................................... 7
Figure 1.3. Penetrating and nonpenetraiting radiation. ......................................... 8
Figure 1.4. Components of a standard nuclear medicine imaging system. ............ 9
Figure 1.5. Collimator detail ................................................................................ 10

Figure 1.6. Scintillation crystal. A sodium iodide crystal ―doped‖ with a thallium
impurity is used to convert gamma photons into light photons. ...... 11
Figure 1.7. Sodium iodide crystal scintillation detector. .................................... 13
Figure 1.8. Photomultiplier tube and its preamplifer and amplifer. .................... 13
Figure 1.9. The positioning algorithm improves image resolution. .................... 14
Figure 1.10. Small matrix. ................................................................................... 16
Figure 1.11. Storing image data in a matrix. ....................................................... 16
Figure 1.12. SPECT camera. ............................................................................... 17
Figure 1.13. Three-headed SPECT camera ......................................................... 17
Figure 1.14. Confgurations of two-headed SPECT camera. ............................... 17
Figure 1.15. (A.) Slices through the level of the heart from selected projection
views are stacked to create a sinogram. (B) Complete sinogram. .... 19
Figure 1.16. Basic components of one type of CT scanner, containing a
stationary detector ring and rotating inner X-ray tube. .................... 20
Figure 1.17. Rotate–stationary configuration. A rotating source and collimator
generate a fan-shaped X-ray beam that is directed toward a stationary
ring of detectors. ............................................................................... 20
Figure 1.18. Rotate–rotate configuration. The opposing source and detector
rotate synchronously. ........................................................................ 20


xiii

Figure 1.19. Multislice CT detector array composed of multiple rows of
detectors placed side by side along the z-axis. ................................. 21
Figure 1.120. SPECT-CT. (a) Two-gantry system with CT system contained
within one gantry and SPECT heads supported on a second gantry;
(b) Single-gantry system with one gantry supporting both the SPECT
camera heads and an X-ray tube and detector. ............................. 26
Figure 2.1. DICOM Information Object Definition (IOD) of a patient. ............. 28

Figure 2.2. Projection views of a liver are backprojected to create transaxial
slices. ................................................................................................ 29
Figure 2.3. Star artifact and backprojection ―blur‖ artifact. ................................ 30
Figure 2.4. Interface of image processing software ............................................ 32
Figure 2.5. Fusion result of CT and SPECT image (a,b,c) and SPECT/CT (d)
obtained from machine of 108 Central Military Hospital. ............... 33
This is the original fusion result of INMOFEVV software. The correct
SPECT/CT image must be captured at the same time. DICOM
images obtained from 2 devices must be the same size, recorded
time,… Overcoming the disadvantages of fusionimages from any
two devices: time, dose projection, shooting angle,… is a big
challenge for image reconstrution algorithms. ................................. 34
Figure 2.6. Multiplanar reconstruction a, brain image; b, abdominal image ...... 34
Figure 2.7. 3D visualization results. .................................................................... 35
Figure 2.8. Some filters are often used. .............................................................. 36
Figure 2.9. The distribution of contour lines....................................................... 37
Figure 2.10. Flowchart of the proposed method ................................................. 39
Figure 2.11. Proposed image segmentation algorithm ........................................ 40
Figure 2.12. Sobel edge detection algorithm. ..................................................... 43
Figure 3.1. The graph shows the change of area by threshold. ........................... 57
Figure 3.2. Three regions are extracted corresponding to the selected seed points
of Gamex phantom. .......................................................................... 58


xiv

Figure 3.3. Raw input images.............................................................................. 60
Figure 3.4. Preprocessing mean filter ................................................................. 60
Figure 3.5. Sobel edge detection. ........................................................................ 60
Figure 3.6. Connected component without using Sobel. .................................... 62

Figure 3.7. Connected component using Sobel. .................................................. 62
Figure 3.8. Segmented images after using region growing. ................................ 63
Figure 3.9. The results of extracting large and small brain tumors use the
proposed segmentation method on MRI images. ............................. 65
Figure 3.10. The results of extracting large and small brain tumors use the
proposed fragmentation method on lung images.............................. 65


xv

List of Tables
Table 1.1. Hounsfield values of some tissues………………………………….17
Table 3.1. Results of MSE, PSNR values of 4 Noise filters: Mean, Median,
Gauss and Bilateral…………………………………………………42
Table 3.2. Dependency of segmentation results on input threshold value with
minsize= 20, 50, 100, 200. …………………………………………45
Table 3.3. Dependency of segmentation results on input threshold value with
minsize= 20…………………………………………………………47
Table 3.4. Results of seed points, area of 3 round holes extracted within the
threshold of 15. ……………………………………………………..53
Table 3.5. Results of evaluation of MSE and PSNR indicators for edge detection
methods……………………………………………………………. 55
Table 3.6. Area of the extracted tumor…………………………………………58
Table 3.7. Results of brain tumor extraction (1, 2, 3, 4, 5) and lung tumor (6),
tumor in liver (7) and extraction of interest area 8 of region growing
method………………………………………………………………60


xvi

List of acnonyms

Acronym

Meaning

DICOM
ITK
VTK
CT
SPECT
PET
PACS
MRI
LEAP
PMT
ADC
GBPS
MUGA
ART
MLEM
OSEM
FBP
NEMA
Qt
VR
VL
PMTs
HU

Digital Imaging and Communications in Medicine
Insight Segmentation and Registration Toolkit
Visualization Toolkit
Computed tomography
Single-photon emission computed tomography
Positron Emission Tomography
Picture archiving and communication system
Magnetic resonance
Low-energy all-purpose collimators
Photomultiplier tube
Analog-to-digital converter
Gated Blood Pool Study
Multigated Acquisition
Algebraic reconstruction technique
Maximum likelihood expectation maximization
Ordered-subsets expectation maximization
Filtered Backprojection Method
National Electrical Manufacturers' Association
Cute 
Value representation
Value length
Photomultiplier tubes
Hounsfield units



1

INTRODUTION
Together with the development of the image diagnostics technology,

nowadays most hospitals have been already equipped with a variety of digital
imaging equipments, and, PACS - Picture archiving and transmission system
has been established. In the field of the medical imaging, DICOM is the
standard used for the storage and transmission of medical images which can
provide the interface standards and protocols for the manufacturers and users
of the medical imaging equipment. The interpretation of DICOM medical
image files, the reading of medical image data, the display, and processing of
image processing are very important.
Most of medical imaging equipment such as Computed tomography
(CT), Magnetic resonance (MR), Positron emission tomography (PET),
Single photon emission computed tomography (SPECT) etc. have supported
DICOM standard. Currently, numerous foreign software packages are
available for medical image processing and analysis in Vietnam, such as
eFilm, 3D-Doctor, DICOMWorks, BrainSuite etc. Difficulty in equipping and
using such packages in Vietnam hospitals are their high price and proprietary
technology of the manufacturer. There are many groups that have written
about medical image processing software in Vietnam. However their
application software, DROC, V-Doctor, BKDICOM etc. has a limited number
of functionalities: Image processing and enhancement functions are still
limited, so it is difficult to understand the detail anatomical structure of the
patient; Most are not upgraded frequently; The ability to protect patient
information has not yet been developed; They have not been applied in
practice for communication between doctors and patients.
From above facts, we have built a multi-purpose medical image
processing application featuring enhancement, segmentation of multimodal
images obtained from different equipment.
In the frame of this dissertation, the author describes about our
development of the software to read DICOM images of CT, MRI and SPECT.

2

Brain tumors are known to be one of the main diseases leading to
human death in the world. A brain tumor varies according to its location, size,
shape, and appearance. Early detection of brain tumor tends to be very
challenging as normally there seem to be no clear symptoms from the
beginning stages. Clearly visible from CT, MRI images, there is overlap
between the boundaries of the tumor in surrounding and tissue, the edges can
be obscured by the structure of the skull, resulting in a lot of contrast to the
background. Therefore, it is difficult to distinguish the boundary between
normal and abnormal tissues. Removing the tumor without affecting the
surrounding tissues is a big challenge for the doctors [1]. The continuous need
for enhanced and accurate automated brain segmentation and detection
methods is an important part of computer-aided diagnosis. Other requirements
include: fast processing time; high-level of automation avoiding the need for
manual intervention; low cost, maintenance and support requirement
including training; ability to shapes, sizes, and types.
In this thesis, the author focussed on providing an application
framework to be used in brain segmentation - the process of tumor detection.
This constitutes setting up software bundled into a Window machine which is
easily distributable. Moreover, a segmentation algorithm was implemented
and tested using the provided software. In order to test the bundled software
by implementing specific brain segmentation from the literature, the author
used CT scans freely available from the database of 108 Central Military
Hospital.
Early detection and treatment can increase the rate of survival for
patients. There are many approaches used in many researches to differentiate
biological tissue edges of brain images. Biji et al. [2] proposed a technique to
detect tumors from MR images using fuzzy clustering and minimum error
thresholding. This method shows how this technique overcomes the problem

of over-segmentation with watershed algorithm but the major drawback is the
computational time required. Bhattacharyya et al. [3] have concluded that a
set algorithms based on thresholding are a powerful tool for the detection of


3

brain tumor in MRI images. The method proposed by Anam Mustaqeem, Ali
Javed, Tehseen Fatima [4] required a watershed algorithm for segmentation.
The article goes into anatomical analysis of the brain and symptoms, damage
caused by encephalopathy. The only downside is the over-segmentation leads
to poor detection of significant areas with low contrast boundaries that
commonly results in MRI brain images. M.C. Jobin Christ and R.M.S.
Parvathi [5] have introduced the method of the brain tumor detection that
integrates K Means clustering with a marker-controlled watershed algorithm
and integrates Fuzzy C Means clustering with marker-controlled watershed
algorithm separately for medical image segmentation. The drawback of Kmeans clustering is that it requires multiple loops. M. Masroor Ahmed et al.
[6] proposed the method for the detection and extraction of brain tumor from
MRI images using K-means Clustering. This method is very effective, that is
proven to be less time consuming and achieves maximum lossless data
compression. But this approach causes fake edges on the image.
Up to now, there are many different algorithms that have been
proposed and implemented. And each of the technique has its own advantage
and disadvantage. However, there is no single approach that can generally
solve the problem of segmentation for the large variety of image modalities
existing today. Segmentation algorithms most effectively are obtained by
customizing combinations of components carefully. Parameters of these
components are adjusted for the characteristics of the imaging method used as
input and for the features of the segmented anatomy [7].
Many researches, softwares in the world today focus on the

segmentation of medical images such as Slicer3D, Osirix [8, 9],... are either
manual or semi-manual, takes a lot of processing time. Tumor detection is a
long and time-consuming process. Location determination, characterization of
the tumor depends much on the experience and skill of the doctor. Most of
these segmentation works are manually done by hand. Manual segments are
often inaccurate. If manual adjustments bring good results, it is not practical


4

for large datasets. Thus, the location of tumor is needed to determine
automatically.
In this thesis, the author uses a region growing method for efficient
segmentation with marking the region of interest (ROI) as well as the
background in gray image. This process combines the basic approaches:
smoothing, edge detection, and region growing segmentation. Here, the
author proposed mean smoothing in order to reduce the noises in CT, MRI
images. Sobel algorithm is used for image segmentation. It uses the connected
component as well to set proper boundaries between adjacent regions. The
texture feature is extracted using region growing method. Hence, it is easy to
implement and provides more stable results than using individual methods.
The Insight Toolkit is an open source cross-platform application toolkit
widely used by researchers in the field of medical image processing. In this
thesis, an environment containing the newest versions of ITK integrated
together with Visualization Toolkit and Qt framework was prepared.
Moreover, a detailed literature survey relating to brain segmentation was also
carried out which resulted in developing a semi-automated region growingbased brain segmentation method implemented and tested using a database
from 108 Central Military Hospital.
The content of this dissertation includes four chapters:
The first begins with introduction and motivation, the current literature and

state of the art techniques for brain segmentation.
Chapter 1 dives into the structure, basic principle, basic characteristics of
SPECT and hybrid imaging system SPECT/CT.
Chapter 2 describes algorithms, filters commonly used in SPECT, SPECT/CT
image processing, and their advantages and disadvantages. These filters are
discussed and they are realized by software programming. The segmentation
of tumor using edge and region growing operations is discussed in detail.
Chapter 3 discusses the structure of proposed approaches and results.


5

CHAPER 1. OVERVIEW
1.1.

INTERACTION OF RADIATION WITH MATTER

When radiation strikes matter, both the nature of the radiation and the
composition of the matter affect what happens. The process begins with the
transfer of radiation energy to the atoms and molecules, heating the matter or
even modifying its structure.
If all the energy of a bombarding particle or photon is transferred, the
radiation will appear to have been stopped within the irradiated matter.
Conversely, if the energy is not completely deposited in the matter, the
remaining energy will emerge as though the matter were transparent or at
least translucent. The thesis will introduce some of the physical phenomena
that are involved as radiation interacts with matter, the interactions in matter
of both photons (gamma rays and X-rays) and charged particles (alpha and
beta particles).
1.1.1.

Interaction of photons with matter

As they pass through matter, photons interact with atoms. The type of
interaction is a function of the energy of the photons and the atomic number
(Z) of the elements composing the matter.
1.1.1.1. Types of photon interactions in matter
In the practice of nuclear medicine, where gamma rays with energies
between 50 and 550 keV are used, Compton scattering is the dominant type of
interaction in materials with lower atomic numbers, such as human tissue (Z =
7.5). The photoelectric effect is the dominant type of interaction in materials
with higher atomic numbers, such as lead (Z = 82). A third type of interaction
of photons with matter, pair production, only occurs with very high photon
energies (greater than 1020 keV) and is therefore not important in clinical


6

nuclear medicine. Figure 1.1 depicts the predominant types of interaction for
various combinations of incident photons and absorber atomic numbers.

Figure 1.1. Predominant types of interaction for a range of incident
photon energies and absorber atomic numbers.
1.1.1.2. Attenuation of photons in matter
As a result of the interactions between photons and matter, the intensity
of the beam, that is, the number of photons remaining in the beam, decreases
as the beam passes through matter (Figure 1.2).
This loss of photons is called attenuation. Specifically, attenuation is
the ratio of the intensity at the point where the beam exits the attenuator, I out,
to the intensity it had where it entered, Iin. The attenuation is an exponential

function of the thickness x of the attenuator in centimeters. This resembles the
exponential manner in which radioactivity decays with time. Expressed
symbolically:


7

Iout/Iin=e−µx

(1.2)

where μ, the linear attenuation coefficient. The linear attenuation
coefficient is greater for dense tissue such as bone than for soft tissue such as
fat. In general, the linear attenuation coefficient depends on both the energy
of the photons and on the average atomic number (Z) and the thickness of the
attenuator. The lower the energy of the photons or the greater the average
atomic number or thickness of the attenuator, the greater the attenuation.

Figure 1.2. Attenuation
1.1.2.

Interaction of charged particles with matter

Because of the strong electrical force between a charged particle and
the atoms of an absorber, charged particles can be stopped by matter with
relative ease. Compared with photons, they transfer a greater amount of
energy in a shorter distance and come to rest more rapidly. For this reason,
they are referred to as nonpenetrating radiation (Figure 1.3).

8

Figure 1.3. Penetrating and nonpenetraiting radiation.
In contrast to a photon of energy 100 keV, an electron of this energy
would penetrate less than 0.00014 cm in soft tissue [10].
1.2.
SINGLE
PHOTON
EMISSION
TOMOGRAPHY AND COMPUTED TOMOGRAPHY
1.2.1.

COMPUTED

Single-photon Emission Computed Tomography

1.2.1.1. Gamma camera
A gamma camera, also called a scintillation camera or Anger
camera, is a device used to image gamma radiation emitting radioisotopes, a
technique known as scintigraphy. The applications of scintigraphy include
early drug development and nuclear medical imaging to view and analyze
images of the human body or the distribution of medically injected, inhaled,
or ingested radionuclides emitting gamma rays.
The components of the Anger camera are depicted in Fig. 1.4, which
includes: Collimator; Camera head; Computers.


9

Figure 1.4. Components of a standard nuclear medicine imaging

system.


10

Figure 1.5. Collimator detail
- Collimators: A collimator is used restricts the rays from the source so
that each point in the image corresponds to a unique point in the source.
Collimators are composed of thousands of precisely aligned holes (channels).
They are usually depicted in cross section (Fig. 1.5). Nuclides emit gamma
rays in all directions. The collimator allows only those photons traveling
directly along the long axis of each hole to reach the crystal. Photons emitted
in any other direction are absorbed by the septa between the holes. Without a
collimator in front of the crystal, the image would be indistinct.
- Camera Head: The camera head contains the crystal, photomultiplier
tubes, and associated electronics. The head housing envelopes and shields
these internal components. Typically, it includes a thin layer of lead. A gantry
supports the heavy camera head.
+ Crystals: The crystal for an imaging camera is a large slab of
thallium-―doped‖ NaI crystal similar to that used for the scintillation crystals.
Scintillation crystals are translucent slabs in which gamma rays are converted
to light. The most widely used crystals are made of sodium iodide (NaI); they
are fragile and can easily be cracked. Because sodium iodide crystals absorb


11

moisture from the atmosphere, they must be sealed in an airtight aluminum
container.
Except

at
very
low
temperatures, pure sodium iodide
crystals do not scintillate unless they
are doped with small amounts (a
fraction of a percent) of stable
thallium (Tl). The thallium atoms
dispersed in the crystal alter its
response to the gamma ray photons
and are said to ―activate‖ the
scintillation (Figure 1.6).
The process of converting
gamma rays to light can be
summarized as absorption of the
gamma ray energy by the crystal,
leaving its electrons in an excited
state. The gamma photon transfers its energy in one or more Compton or
photoelectric interactions in the crystal. Each of the energetic electrons
produced by these gamma ray interactions distributes its energy, in turn,
among electrons in the crystal, leaving them in an excited state. As these
return to their original state, some of their energy is released as light photons.
In a typical detector arrangement, photomultiplier tubes are optically coupled
to scintillation crystals to detect these light photons. The design of the crystal
affects its performance. The thickness of the sodium iodide crystals used
ranges from less than a centimeter to several centimeters. Thicker crystals, by
absorbing more of the original and scattered gamma rays, have a relatively
high sensitivity, because almost all of the gamma ray energy reaching the
crystal is absorbed. Thinner crystals have a lower sensitivity because more
photons escape. For photons in the 140 keV range (99mTc), typical thicknesses

range from 0.6 to 1.2 cm [14]. After the crystal has absorbed energy from a
Figure 1.6. Scintillation crystal. A
sodium iodide crystal ―doped‖ with a
thallium impurity is used to convert
gamma photons into light photons.


12

gamma ray impact, the excited electrons in the crystal do not all return to their
original state at exactly the same time, but do so over the course of a few
nanoseconds to milliseconds, depending on the scintillator. As a result, the
light photons are also emitted by the crystal over a very short span of time
instead of as a single simultaneous burst.
+ Photomultiplier Tubes: The photomultiplier tube (PMT) is a
vacuum tube with a photocathode on the end adjacent to the crystal. A
photocathode is a light-sensitive material, usually a type of semiconductor.
The PMT is coupled with a light-conductive transparent gel to the surface of
the crystal (Figure 1.7). The transparent gel has the same refractive index as
the crystal and the PMT window. The light striking the photocathode causes it
to emit electrons, referred to as photoelectrons. On average, four to six light
photons strike the photocathode for each photoelectron produced. The number
of electrons produced at the photocathode is greatly increased by the
multiplying action within the tube (Figure 1.8). As soon as they are produced,
the electrons cascade along the multiplier portion of the tube, successively
striking each of the tube‘s dynodes. These are metal electrodes, each held at a
progressively higher voltage. As an electron strikes a dynode, it knocks out
two to four new electrons, each of which joins the progressively larger pulse
of electrons cascading toward the anode at the end of the tube. In other words,
for each electron entering a cascade of just three such dynodes, there will be

between 23 and 43 electrons leaving; cascading against 10 dynodes will yield
between 210 and 410 electrons [11]. Sixty or more PMTs may be attached to
the back surface of the crystal using light-conductive jelly.
+ Preamplifers and amplifers: The current from the photomultiplier
must be amplifed further before it can be processed and counted. Despite the
multiplication in the photomultiplier tube, the number of electrons yielded by
the chain of events that begins with the absorption of a single gamma ray in
the crystal is still small and must be increased further, or amplifed. Typically,
this amplifcation is a two-stage process.In the first stage, a small preamplifer
located close to the photomultiplier increases the number of charges