Mise en oeuvre d'un mode d'imagerie par transillumination et détection multi-vue à ultra-faible bruit dans l'imageur QOS[indice supérieur TM] pour imagerie moléculaire optique sur petit animal

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UNIVERSITÉ DE SHERBROOKE

Faculté de génie

Département de génie électrique et de génie informatique

Mise en œuvre d’un mode d’imagerie par

transillumination et détection multi-vue à

ultra-faible bruit dans l’imageur QOS

TM

pour

imagerie moléculaire optique sur petit animal

Implementation of a transillumination mode and ultra-low noise

multi-view detection in the QOS

TM

for small animal optical

molecular imaging

Mémoire de maîtrise

Specialité : génie électrique

Nikta ZARIF YUSSEFIAN

Jury: Yves BÉRUBÉ-LAUZIÈRE (Directeur)

Réjean FONTAINE (Rapporteur)

Martin LEPAGE (Évaluateur)

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RÉSUMÉ

La tomographie optique diffuse (TOD) est une technique d’imagerie médicale relativement récente qui utilise la lumière dans le proche infrarouge pour acquérir des images in vivo de façon non invasive. Cette technique est en utilisation croissante par de nombreux cher-cheurs et biologistes et plusieurs équipes dans le monde travaillent sur le développement de scanners par TOD y compris notre groupe de recherche (groupe TomOptUS).

Le Centre d’imagerie moléculaire de Sherbrooke dispose d’un appareil pour imagerie op-tique sur petit animal développé par la compagnie Quidd, soit le QOS (Quidd Optical imaging System). Cet appareil est utilisé par des biologistes et chercheurs pour diverses études précliniques sur modèles animaux (souris) de maladies humaines comme le can-cer. Le QOS est entièrement contrôlé par ordinateur à l’aide d’un logiciel sophistiqué (le QOSoft) qui permet d’obtenir des images en fluorescence et en bioluminescence. Il est toutefois limité en ne permettant d’acquérir que des images planaires de la lumière sortant d’un animal ; il ne permet pas la tomographie, à savoir obtenir des images tridimension-nelles (3D) des sources fluorescentes ou bioluminescentes situées en profondeur à l’intérieur de l’animal. Bien que le QOS offre une grande flexibilité en terme d’angle d’acquisition d’images autour de l’animal avec sa caméra montée sur un bras rotatif, il a une sensibilité limitée pour de l’imagerie en profondeur, notamment parce qu’il fonctionne en mode épi-illumination (détection de la lumière du même côté que l’injection de la lumière excitatrice dans l’animal) et aussi à cause de la sensibilité limitée de sa caméra.

Afin d’augmenter les capacités tomographiques et la sensibilité du QOS, ainsi que le contraste des images qu’il fournit, le présent projet propose des développements logi-ciels intégrés au QOSoft. Ces ajouts logilogi-ciels au niveau du contrôle d’instrumentation et de l’interface graphique permettent d’intégrer une caméra EMCCD à ultra-haute sensi-bilité et ultra-faible bruit pour remplacer la caméra CCD refroidie existante ainsi qu’un module d’illumination laser rotatif. Ce module d’illumination, développé par le groupe To-mOptUS, permet l’imagerie en mode transillumination ainsi que toutes les configurations intermédiaires jusqu’à l’épi-illumination. Ce module permet en outre d’injecter une densité de puissance lumineuse supérieure à celle possible avec la configuration actuelle du QOS. Le QOS et son logiciel mis à jour avec les ajouts faisant l’objet du présent projet sont validés par des expériences de fluorescence et de bioluminescence sur fantômes et animaux vivants.

Mots-clés : Tomographie optique diffuse, caméra à dispositif de couplage de charge (caméra CCD - charge-coupled device), caméra CCD à multiplication d’électrons (EMCCD - electron multiplying CCD ), imagerie optique moléculaire sur petit ani-mal, transillumination

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ABSTRACT

Diffuse optical tomography (DOT) is a relatively recent medical imaging technique that uses near infrared light to acquire in vivo images non-invasively. This technique is increas-ingly exploited by researchers and biologists and several teams all over the world work on developing DOT scanners including our group (the TomOptUS group).

The Sherbrooke Molecular Imaging Center possesses a small animal optical molecular imaging system developed by the company Quidd. This system, the QOS - Quidd Opti-cal imaging System, is used by biologists and researchers for various precliniOpti-cal studies on small animal models (mice) of human diseases, such as cancer. The QOS is entirely computer-controlled by a sophisticated software (the QOSoft) which allows obtaining fluo-rescence and bioluminescence images. The QOS is, however, limited by allowing to acquire only planar images of the light exiting the animal; it does not allow tomography, that is to obtain tridimensional (3D) images of fluorescence and bioluminescence sources located at depth inside the animal. Although the QOS offers great flexibility in terms of image acquisition angles around the animal with its camera mounted on a rotating arm, it has limited sensitivity for imaging at depth, notably because of its epi-illumination configura-tion (light detecconfigura-tion on the same side as excitaconfigura-tion light is injected into the animal), and the limited sensitivity of its camera.

In order to increase the tomographic capabilities and sensitivity of the QOS system, along with the contrast of the images it provides, this project proposes software add-ons to be integrated to the QOSoft. These add-ons, concerning instrumentation control and the graphical user interface, allow integrating an ultra-high sensitivity and ultra-low noise EMCCD camera which is replaced the cooled CCD camera currently in place, along with a rotating laser illumination module. This illumination module, developed by the TomOptUS group, allows transillumination-mode imaging, along with all intermediate configurations up to epi-illumination. It also allows injecting greater light power density than possible with the current configuration of the QOS. The QOS and the software add-ons that are part of this project are validated through fluorescence and bioluminescence experiments on phantoms and live small animals.

Keywords: Diffuse optical tomography, charge coupled device (CCD ), electron multiply-ing CCD (EMCCD ), small-animal optical molecular imagmultiply-ing, transillumination

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank my supervisor Prof. Yves Bérubé-Lauzière for giving me the opportunity to work in his research group. I am also grateful for his con-tinuous support, patience and caring that provided me with an excellent atmosphere for doing my research.

I wish to express my sincere thanks to Dr. Réjean Lebel who supported me in all the ex-perimental procedure, methodology and technique while I was working at CIMS. Special thanks to our lab’s research assistant, Mr. Mathieu Letendre-Jauniaux, who was always willing to help and give his best suggestions.

I also take this opportunity to express my thankfulness to all who lent a hand in helping me through this project.

Last but not least, I would like to express my appreciation and gratefulness to every mem-ber of my family for their unconditional support and consistent encouragement.

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LIST OF FIGURES

2.1 Fluorescence [Letendre-Jauniaux, 2012]. . . 6

2.2 Cyanines dye spectrum [Altman et al., 2012]. . . 7

2.3 Phtalocyanines molecule. . . 8

2.4 Comparison between CCD and EMCCD. (a) CCD structure. (b) EMCCD structure. . . 12

2.5 Time domain measurement. . . 13

2.6 Frequency domain measurement. . . 14

2.7 Continuous-Wave measurement. . . 14

2.8 a) Epi-illumination imaging configuration b) Transillumination imaging con-figuration c) Tomographic concon-figuration. . . 16

2.9 Ntziachristos’ first prototype [Ntziachristos et al., 2002a]. . . 17

2.10 Ntziachristos’ second prototype [Ntziachristos et al., 2004]. . . 18

2.11 Ntziachristos’ third prototype [Zacharakis et al., 2005]. . . 19

2.12 Ntziachristos’ fourth prototype [Turner et al., 2005]. . . 19

2.13 Ntziachristos’ fifth prototype [Lasser et al., 2007]. . . 20

2.14 Sevick-Muraca’s scanner [Sevick-Muraca, 2004]. . . 21

2.15 Schulz’s scanner [Schulz et al., 2005]. . . 21

2.16 ART preclinical scanner [ART Advanced Research Technologies Inc., 2004]. 22 2.17 IVIS scanner [PerkinElmer, 2014]. . . 23

2.18 Xenogen scanner [Xenogen Corporation, 2005]. . . 23

2.19 Maestro scanner [Cambridge Research & Instrumentation, 2007]. . . 24

2.20 Visen medical scanner [VisEnMedical, 2010]. . . 25

2.21 TomOptUS scanner [Lapointe et al., 2012]. . . 25

2.22 Kodak scanner [CarestreamHealth, 2007]. . . 26

2.23 Pearl scanner [LI-COR, 2014]. . . 26

3.1 The QOS scanner. (a) The original configuration of QOS scanner. (b) Front-side view. . . 30

3.2 Brief block diagram of QOSoft. . . 31

3.3 Lack of sensitivity affects the results in bioluminescence experiments. (a) In cellulo bioluminescence experiment. (b) In vivo bioluminescence experiment. 32 3.4 Results of PCA analysis [Provencher, 2012]. . . 33

5.1 Graphical user interface of the EMCCD camera in the QOSoft. . . 41

5.2 EMCCD processing modes [Daigle and Ducharme, 2013]. . . 42

5.3 Quad laser module with rotating laser head, animal bed, and translation stage of the bed along the X axis. . . 42

5.4 GUI of the laser module in the QOSoft. . . 43

5.5 Virtual pivot parameters. . . 44

5.6 Modified block diagram of the QOSoft. . . 45

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5.7 Overview of hardware modifications performed on the QOS optical module, so-called the "optical block". (a) Hardware modifications for the Andor camera. (b) Hardware modifications for the NüVü camera. In blue: parts that were modified and that are used for both cameras.In green:parts that were modified and that are used only for the Andor camera. In red: parts

that were modified and that are used only for the NüVü camera. . . 46

5.8 The QOS new configuration.In blue: Eldim block. In green: NüVü camera. In purple: Quad laser module. . . 47

6.1 In cellulo bioluminscence experiment. . . 49

6.2 In vivo bioluminescence experiment. (a) CCD experiment, one image (5 minutes). (b) EMCCD experiment, an average of 3 images (7.5 seconds). . 50

6.3 Fluorescence detection in vitro. The intensity of fluorescence signal for each concentration of fluorophore is compared with a blank. Dashed line repre-sents the lowest concentration at which the fluorophore produces statisti-cally significant signal. . . 51

6.4 Injection of 1 nmole of the Cy7 and ZnPcS4 molecules. (a) The image is an average of 10 X 20 ms images. (b) A set of 300 X 200 ms images was necessary to obtain an image with reasonable SNR. . . 53

6.5 In vivo fluorescence detection limits. (a) 10 minutes post injection (10 x 200 ms images). (b) SNR as a function of injected quantities (10 x 200 ms images). (c) SNR as a function of injected dose (300 x 200 ms images). . . 54

6.6 PCA analysis of images acquired using different ZnPcS4 and Cy7 concen-trations. . . 55

6.7 CCD reflectance results. . . 56

6.8 EMCCD reflectance results. . . 57

6.9 Comparison of the reflectance signals. . . 58

6.10 The Cy7 fluorescence signal intensity of each concentration of fluorophore. 59 6.11 Illumination-detection angles for different positions of the camera and laser. 60 6.12 Images acquired in epi- and trans-illumination detection for the inclusion close to the surface of the phantom. Note that θ is the detection angle according to Figure 6.11. . . 61

6.13 Different transillumination detection for inclusion at center. Note that θ is the detection angle in accordance to Figure 6.11. . . 62

A.1 Add a new project in the QOSoft. . . 70

A.2 Add a reference to the project. . . 71

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LIST OF TABLES

2.1 Comparison of different types of small animal optical imaging systems.

Adapted from [Letendre-Jauniaux, 2012]. . . 28

3.1 Total axes movement . . . 29

3.2 The functions of the QOS major components. . . 30

5.1 Laser wavelength range . . . 44

6.1 QOS parameters used for the reflectance experiment. . . 56

6.2 QOS parameter description for transillumination experiment. . . 59

6.3 Description of laser parameters. . . 60

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LIST OF ACRONYMS

Acronym Definition

CCD Charge Coupled Device

CIMS Centre d’imagerie moléculaire de Sherbrooke

CMOS Complementary Metal-Oxide-Semiconductor Camera

CT Computed Tomography

CW Continuous Wave

DLL Dynamic Link Library

DOT Diffuse Optical Tomography

EM Electron Multiplication

EMCCD Electron Multiplying Charge Coupled Device

ENF Excess Noise Factor

FD Frequency Domain

FOV Field Of View

FRI Fluorescence Reflectance Imaging GUI Graphical User Interface

ICCD Intensified Charge Coupled Device

MCP Micro Channel Plate

MRI Magnetic Resonance Imaging

NIR Near Infra-Red

PCA Principal Component Analysis

PDT Photo Dynamic Therapy

PET Positron Emission Tomography

QE Quantum Efficiency

QOS Quidd Optical Scanner

RMSD Root Mean Square Deviation

ROI Region Of Interest

SNR Signal to Noise Ratio

SD Standard Deviation

SPECT Single Photon Emission Tomography TCSPC Time-Correlated Single Photon Counting

TD Time Domain

TPSF Temporal Point Spread Function

US Ultrasound

VB Visual Basic

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CHAPTER 1 Introduction

1.1 Context

Imaging inside the living body has recently become an important research concern as it provides valuable information about internal organs and function without need of surgery [James and Gambhir, 2012; Weissleder and Mahmood, 2001]. In fact, molecu-lar imaging stands for non-invasive, real-time visualization of biochemical events at the cellular and molecular levels within living cells and tissues [James and Gambhir, 2012; Massoud and Gambhir, 2003; Osborn and Jaffer, 2008]. Although it is still at its early stage, molecular imaging is used for studying the fundamental processes of disease de-velopment, in therapy monitoring, and in drug discovery and development [James and Gambhir, 2012]. Researchers are using several techniques to acquire information from in-side of the body. These techniques enable physicians and researchers to diagnose illnesses and develop or choose proper treatment for them. These imaging modalities allow for non-invasive visualization of the body based on different forms of energy interacting with the tissue. Some of these modalities (like positron emission tomography- PET, single pho-ton emission computed tomography- SPECT, or X-ray computed tomography- CT) use ionizing forms of radiation while some others (like diffuse optical tomography- DOT, or ultrasound- US imaging) use non-ionizing ones. Unlike PET and CT that exploit high energy radiation, DOT uses visible and near infra-red (NIR) wavelengths to acquire data. Due to its low energy, light in these spectral ranges transit with strong spatial distortion through biological tissues. In fact light is mostly scattered and absorbed when interacting with a tissue and images are made from the strongly scattered transmitted light using appropriate image reconstruction techniques [Hielscher, 2005]. In comparison with other methods, which do not easily provide information about molecular mechanisms, the most important advantage of optical biomedical imaging is its ability to acquire information from processes occurring at the cellular and molecular levels by use of appropriate fluo-rescent and bioluminescent agents [James and Gambhir, 2012; Weissleder and Mahmood, 2001]. In addition, the unique property of the fluorescent agent to be modulated by the environment has led to the development of smart or activatable probes and fluorescence quenching, a process that let the agent to be turned "on" or "off" resulting in a higher

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target-to-background ratio [Kobayashi and Choyke, 2011].

The most current applications of optical imaging are in breast cancer imaging [Ntziachris-tos et al., 2002b; Pogue et al., 2001], limb and joint imaging [Hielscher et al., 2004], blood oximetry in human muscles [Benaron et al., 2000; Bluestone et al., 2001; Hillman et al., 2001] as well as in the brain for functional imaging [Habermehl et al., 2011], and in small animal optical molecular imaging [Hielscher, 2005]. Use of non-ionizing radiation, lower cost and portability are the most important advantages of optical imaging [Choyke and Kobayashi, 2012].

To investigate diseases and to develop proper treatments and medications to cure them, small animals are currently used as models for human diseases [Masciotti et al., 2005]. Applying this technique coupled with novel biochemical markers that are sensitive to molecular processes opens the way to researchers for visualizing and quantifying human diseases at the molecular level [Weissleder and Mahmood, 2001]. Moreover, DOT can be exploited potentially for whole body small animal imaging.

Considering the importance of this harmless branch of tomography, researchers are using their skills in creating various novel imaging devices in order to increase spatial resolution, avoid loss of sensitivity as a function of depth and provide more quantitative images with optical imaging, some aspects that have been its main shortcomings thus far. These scan-ners should be equipped with sensitive detectors and have flexibility in terms of acquisition angle and geometry to provide tomographic data. One such imaging system is the Quidd optical scanner (QOS) developed by the company Quidd, which is an optical imaging system for preclinical research on small animals. This scanner is available in our imaging center (Centre d’imagerie moléculaire de Sherbrooke - CIMS). This scanner allows obtain-ing fluorescence and bioluminescence images. However, it suffers from a lack of sensitivity, and it does not provide tomographic information since it can acquire imaging data only in epi-illumination mode. To enable 3D tomographic image reconstruction, it is necessary to acquire sets of data from around the subject which is impossible with the current QOS configuration. Hence, the purpose of the present project is to add software tools to the QOS to endow it with tomographic capabilities by controlling hardware components added to it.

1.2 Overview of the Project

Tomographic image reconstruction is hampered by a lack of flexibility in the illumination configuration as well as by the low sensitivity of the camera used in the standard configu-ration of the QOS. This project is about improving the QOS to endow it with tomographic

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1.3. CONTRIBUTIONS 3 capabilities for optical molecular imaging by integrating an ultra-low noise and ultra-high sensitivity EMCCD camera and a rotating laser module. This will pave the way to perform DOT imaging of tissue intrinsic optical properties (absorption and scattering coefficients), and of fluorescence and bioluminescence sources inside small animals with appropriate image reconstruction algorithms. The development of such algorithms is not in the scope of present work, which rather concentrates on developing the software tools needed for the integration of the EMCCD camera and of the illumination module to provide tomographic capabilities to the QOS small animal imaging system in terms of allowing the acquisition of large sets of data around the animal with lower noise.

1.3 Contributions

Although the QOS system has been used extensively so far by researchers and biologists at CIMS, it has shown some limitations in terms of sensitivity. It proves difficult to ac-quire bioluminescence signals as well as fluorescence signals in the case of low fluorophore concentrations or of fluorophores with low quantum yield, which occur often in practice. To alleviate such difficulties, the TomOptUS group has undertaken to modify the QOS to improve its sensitivity. Hence, a quad laser illumination module and a highly sensitive low-noise EMCCD camera have been integrated to it. The integration of the laser mod-ule increases the flexibility of the QOS in terms of acquisition and illumination geometry and makes transillumination imaging feasible, while integration of the EMCCD camera enhances the sensitivity of the scanner significantly. This makes the QOS the only CW small animal imaging system with completely independent degrees of freedoms as regards illumination and detection.

Published conference papers: The work described herein has led to two published conference papers [Lebel et al., 2013; Yussefian et al., 2014], and a journal paper is in preparation.

1.4 Plan of Document

The present document consists of 7 chapters. Chapter 2 discusses the state of the art pertaining to the project including fluorescence and bioluminescence imaging, different types of imaging systems, imaging geometries and parameters that affect low light de-tection along with a section on the scanners developed so far and their pros and cons.

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Chapter 3 explains the basics of the QOS imaging system as well as its limitations. This chapter ends by showing some results obtained previously with the scanner that show its limitations. This leads to Chapter 4 wherein the definition and objectives of the project described in this thesis are given. Chapter 5 details the modifications made on the QOS to endow it with tomographic capabilities and improve on its sensitivity. This is followed by Chapter 6 that presents results obtained with the modified QOS scanner, which attest the improvements made on its sensitivity as well as on enhancing its functionality in terms of tomographic capabilities and imaging geometry. Finally, Chapter 7 concludes this thesis.

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CHAPTER 2 State of the Art

2.1 DOT Imaging

DOT in the context of small animal optical molecular imaging is used to image in 3D the distribution of fluorescence and bioluminescence compounds. These allow probing biomolecular processes related to disease [Chen et al., 2005]. DOT is preferably carried out with near-infrared (NIR) light in the wavelength range from 650 to 900 nm, since in that range light can penetrate the deepest as it is the least absorbed by hemoglobin, which is the main chromophore in biological tissues [Bremer et al., 2003; Mahmood, 2004]. Light in that range, however, suffers strong scattering during its propagation, which causes DOT images to be blurred. This is the main reason for the low spatial resolution attained by DOT imaging compared to other imaging modalities such as CT or MRI [James and Gambhir, 2012]. Intense research is dedicated to the development of fluorophores that have their absorption and emission spectra in the NIR range. In the case of bioluminescence, whose emission peak is typically around 560 nm, one is bound to work with visible light and high absorption. Light propagating inside a tissue will be mostly absorbed and par-tially emerged from its surface where it can be detected. This can be done by either optical fiber-based detectors or CCD cameras with the latter being more suitable for preclinical animal studies due to their lower cost and easy set up [Lin et al., 2011].

2.1.1 Fluorescence Imaging

Fluorescence imaging resorts to fluorescent dyes conjugated to molecular probes (such compounds being called fluorescent probes; more will be said on molecular probes in Sec-tion 2.2 below), or to fluorescent proteins [Klose, 2009]. Fluorescent probes are engineered to target specific biological processes at the cellular or molecular level, whereas fluorescent proteins are expressed by reporter genes inside cells. Fusing a reporter gene to a gene of interest enables imaging fluorescent proteins which are primarily used in gene expression and gene regulation studies. Fluorescent proteins are primarily used in gene expression and gene regulation studies. In the case of small animal imaging, the difference between a fluorescent probe and a fluorescent gene is that a probe is administered to the animal, whereas a fluorescent protein is produced within the animal at the site of transcription

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of the reporter gene. Fluorescent molecules require energy in the form of light excitation of appropriate wavelength in order to emit light at a longer wavelength (Figure 2.1). The difference in wavelength, caused by internal non-radiative transition energy losses within the molecule is called the Stokes shift [Choyke and Kobayashi, 2012]. The detected light from the surface of an animal, however, may be contaminated with autofluorescence signal. The autofluorescence signal is that naturally emitted by biological tissues (such as skin) and can be interfering with the desired signal. This signal is, therefore, considered as a background noise and eliminating it provides cleaner data. The fact that fluorescence is relatively easy to use, inexpensive, safe, and fast, makes it a popular approach in the field of molecular imaging.

Figure 2.1 Fluorescence [Letendre-Jauniaux, 2012].

2.1.2 Bioluminescence Imaging

Bioluminescence imaging relies on energy dependent reactions catalyzed by luciferases which lead to the emission of photons in the visible range. Liberation of energy in the form of light is a product of a luciferase-luciferin reaction taking place in a cell [Weissleder, 2002]. Luciferases are enzymes that have been obtained from a large number of organisms including bacteria, fireflies, corals and jellyfishes [Thorne and Contag, 2005; Weissleder, 2002], and are not toxic to the cells. They convert their substrate, luciferin, to oxyluciferin, with the emission of a detectable photon. There is thus no need of an excitation source in bioluminescence imaging [Choyke and Kobayashi, 2012]. The fact that bioluminescence imaging does not require an excitation source makes this imaging modality distinctive because there is no background signal produced by excitation light, which results in images with higher contrast. Bioluminescence imaging is most demanding in terms of sensitivity. It typically requires several minutes (ten minutes not being unusual) to obtain enough statistic for image reconstruction. This is because the emission of light is intrinsically weak and it is in the visible spectral range where biological tissues are strongly absorbing.

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2.2. MOLECULAR IMAGING PROBES 7

2.2 Molecular Imaging Probes

Molecular imaging probes are small molecules such as ligands or enzymes substrates, or higher molecular weight affinity ligands, such as recombinant proteins [Massoud and Gambhir, 2003] that can easily escape from the vasculature. In this way, they can reach their target in a specific short period of time and therefore create the desired signal that is produced from the interaction with their target [James and Gambhir, 2012]. Among them, fluorescent probes that absorb and emit light in the NIR are employed to visualize molec-ular targets in vivo and also enhance the sensitivity of the whole detection process [Rao et al., 2007; Weissleder and Mahmood, 2001], since, as mentioned earlier, NIR light has the maximum depth of penetration. One such type of probes are the molecules of the cyanine family (CyX), notably Cy5 and Cy7, that are among the mostly used molecules since they are easily synthesized for several wavelengths and provide better contrast in the NIR (Figure 2.2) [Altman et al., 2012].

Figure 2.2 Cyanines dye spectrum [Altman et al., 2012].

2.3 Phantoms

Phantoms are objects which are used for validating novel methods in optical biomedi-cal imaging. These objects seek to imitate the optibiomedi-cal properties of biologibiomedi-cal tissues, so that they can be used for calibration if their properties are exactly known and control-lable [Spinelli et al., 2014].

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2.4 Multimodal Imaging

Multimodal imaging is recently attracting the attention in pre-clinical studies, and the parallel information obtained in one imaging session by this method has been proven to be beneficial in tomography [Zhang et al., 2011]. One application of this technique is the incorporation of a radioisotope within a fluorescence probe [Huang et al., 2012]. This approach is limited by the concentration that should be within the sensitive range of both modalities. The suitable concentration is in the order of 10−9 for PET/Fluorescence imaging. Multimodal imaging is still challenging in this range of concentrations.

Figure 2.3 Phtalocyanines molecule.

Probe stability, infrastructure costs, and low risks of manipulation of the fluorescence imaging are some of the motivations for the substitution of PET and SPECT imaging by fluorescence imaging. The high sensitivity of PET imaging makes it possible to detect low molecular concentration targets, such as receptors. To be a proper substitute, fluorescence optical scanners must be optimized to have highly efficient filters, powerful illuminations, and extremely sensitive cameras.

A family of molecules named phtalocyanines (Pc) have been recently introduced (Fig-ure 2.3), which have potential to be used in photodynamic therapy (PDT) [Cauchon et al., 2006]. Phtalocyanines are chelators that are able to trap a metal (or radiometal). This spe-cific feature results in affecting their physicochemical properties. As an instance, if a Zn atom is trapped, the Pc molecule becomes highly fluorescent in water. While by inserting a 64Cu radioisotope, this molecule transforms into a PET tracer [Ranyuk et al., 2011]. This duality makes the Pcs suitable to be used as bimodal probes if their pharmacological properties are similar, i.e. by injecting a mixture of both CuPc and ZnPc.

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2.5. CHARGED-COUPLED DEVICE (CCD) FUNDAMENTALS 9

2.5 Charged-Coupled Device (CCD) Fundamentals

A critical component of an imaging system is its detector or array of detectors as it is an important part contributing to the quality of the images obtained and system sensitivity. Typical detectors in imaging systems are photomultiplier tubes, photodiodes, avalanche photodiodes and cameras. In the present work, the detector of interest is the CCD cam-era. Among different sorts of cameras available nowadays, CCD cameras have been ex-tensively used by both astronomers and biologists [Daigle and Ducharme, 2013]. Based on the sensor technology and readout circuitry, such cameras can be categorized into four types: CCD, EMCCD (electron multiplying CCD), CMOS (complementary metal-oxide-semiconductor), and ICCD (intensified CCD) cameras. Although each of these types has its own weaknesses and strengths, they share common performance parameters that are described below.

2.5.1 Dynamic Range

The dynamic range is the ratio of the maximum to the minimum signal intensities that can be simultaneously measured in the same image. The dynamic range is often related (but it is not necessarily equivalent) to the number of bits used to digitize (or quantize) measured light intensities. For instance an n-bit image sensor can provide a 2n:1 dynamic range, but this is not necessarily the ratio of light intensities that it is able to measure. It is perfectly possible to quantize a small interval of intensities with a large number of bits; this will then provide greater intensity resolution.

2.5.2 Quantum Efficiency

The quantum efficiency (QE) of a light detector is defined as the probability of gener-ating a photo-electron for each photon absorbed. This is the same as the percentage of electrons generated by photons impinging the detector. The inverse of the QE (i.e. 1/QE) corresponds to the number of photons needed to generate 1 photo-electron. The quantum efficiency is generally dependent on the wavelength of the photons; it is related to the properties of the material used to fabricate a detector.

2.5.3 Noise

The detection limit of a camera is determined by either the number of photons or the minimum detectable light that is equal to the noise. In low light applications, cameras are

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often compared by way of their noise figures. The sources of noise are mainly the following.

– Thermal noise or dark current is thermally generated noise. It arises from the ejection of electrons by thermal agitation in the silicon substrate of the CCD. This noise can be significantly reduced by cooling the camera [Andor, 2001].

– Readout noise comes from the conversion of the charges knocked free by the interaction of photons with sensor to an electrical potential which is on the order of 2 to 10 electrons. This noise may be considered as insignificant; however, in low light conditions, the few electrons generated by noise through the conversion process can become very significant. This can hinder obtaining a good quality image [Andor, 2001; Daigle and Ducharme, 2013].

– Excess noise relates to the process of electron multiplication used to amplify photoelec-trons; it is a stochastic process. This implies that the multiplicative gain that is applied on a specific pixel is not always the same and only the mean value of the gain can be calculated. This stochastic process gives rise to the excess noise factor (ENF) of√2 that induces the same distortion on SNR as reducing the QE [Daigle and Ducharme, 2013]. – Shot noise is related to the fundamentally quantum (discrete) nature of light (i.e. the photons). More precisely, the number of photons resulting from a flux of light impinging on a surface (e.g. a detector) in a given time interval is a stochastic variable following a Poisson distribution. The standard deviation which quantifies the noise is then equal to the square root of the mean number of photons (i.e. the signal).

2.5.4 Signal to Noise Ratio (SNR)

The SNR is used to characterize the quality of signal detection. In the case of CCD cameras, the SNR is the ratio of the light signal to the noise present in that signal and is expressed in units of decibels (dB) [Andor, 2001; Bushberg et al., 2002].

2.5.5 Camera Sensitivity or Detection Limit

The detection limit of a camera is determined by the number of photons or equivalently the minimum detectable light signal that is equal to the noise present in the process of detecting that signal (when this number is low, this means that the process is low noise, and then this is interpreted as a high detection limit). This is equivalent to the number of photons for generating as many electrons that are equivalent to an SNR of 1. This is also called the camera sensitivity (again, when this number of photons is low, this is interpreted as a high sensitivity).

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2.6. COMPARING CAMERAS 11

2.5.6 Spatial Resolution

The spatial resolution is the smallest distance between two small objects that can still be discriminated. Lines or points are often used for the purpose of characterizing spatial reso-lution. An alternative is to define the spatial resolution as the full-width at half maximum of the point-spread function, which is the image of a point source [Bushberg et al., 2002].

2.6 Comparing Cameras

Looking into the most important parameters for scientific cameras, it is worth to compare shortly different kinds of cameras to explore their efficiency and functionality.

2.6.1 Charged Coupled Device (CCD) Cameras

A CCD detector is composed of an array of photosensors. Each sensor consists of a silicon photodiode coupled to a charge storage region which, in turn, is connected to an amplifier to allow reading the accumulated charges. When an incident photon with sufficient energy hits the sensor detector, it is absorbed and an electron is liberated (photoelectric effect). Such electronic charges are momentarily stored and then transferred from parallel registers to linear registers which are followed by an amplifier. Since there is only one amplifier in these devices, the charges are transferred sequentially to it and this puts a limitation on the readout speed. Figure 2.4a depicts the structure of a CCD array (frame transfer architecture) that consists of two sections: an image section where photons are converted into charges, and a store section in which charges are stored before being read out. This area is covered with the optical shielding metal preventing it from being exposed to the light while integration is in process.

2.6.2 The Complementary Metal-Oxide-Semiconductor (CMOS)

Camera

CMOS detectors achieve significantly higher readout speeds compared to CCD detectors, because these detectors are equipped with multiple amplifiers, with each amplifier serving one column of photosensors. Hence, a row of pixels can be readout in parallel. A CMOS device is essentially a parallel readout device and therefore can achieve higher readout speeds particularly required by imaging applications. However, CMOS detector technology has still long way to find its place in scientific research due to its lower sensitivity compared to CCDs.

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(a) (b)

Figure 2.4 Comparison between CCD and EMCCD. (a) CCD structure. (b) EMCCD structure.

2.6.3 Intensified Charge Coupled Device (ICCD) Camera

An ICCD is basically an image intensifier coupled with a CCD. This intensifier mainly consists of a micro-channel plate (MCP), a photocathode and a phosphor screen. The inci-dent photons are converted into photoelectrons by photocathode and then are accelerated towards the MCP by a high-voltage electric field. The MCP then amplifies the number of electrons which then hit the phosphor screen and are converted to photons that are later detected with the CCD [Zhang and Chen, 2009]. ICCDs are generally dedicated to applications requiring very short exposure times (on the order of nanoseconds or less).

2.6.4 The Electron Multiplying Charged Coupled Device

(EM-CCD) Camera

As Figure 2.4b depicts, the EMCCD structure is the same as that of the CCD with the only difference that the stored charge is first transferred to an additional register, so-called a multiplication register in which the charge is amplified via an ionization effect. In the multiplication register, the charge is amplified above the readout noise of the amplifier and hence EMCCDs can deliver higher performance than CCDs in low light detection in terms of QE and low dark current, and also present negligible readout noise. Despite their higher price, EMCCDs have been recently taking the place of traditional CCDs in several scientific imaging applications.

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2.7. TYPES OF MEASUREMENTS IN OPTICAL IMAGING SYSTEMS 13

2.7 Types of Measurements in Optical Imaging

Sys-tems

In general, diffuse optical imaging systems can be divided into three different categories according to the type of measurements they carry: time-domain, frequency-domain or continuous-wave.

2.7.1 Time-Domain (TD) Systems

In this type of systems, the tissue is illuminated with short pulses of light and non-absorbed photons that have passed through different paths inside the medium are detected and make a curve according to their flight at the output (Figure 2.5). This time-of-flight distribution (temporal point spread function- TPSF) is used to recover the optical characteristics that discriminate absorption and scattering properties from each other inside the tissue. Nowadays, time-domain systems exploit photon counting detectors which are sensitive (but also expensive). TD systems have the advantage of acquiring information at practically all frequencies1_{, and also provide additional information about photon path}

lengths [Boas et al., 2001; Gibson et al., 2005; Lapointe et al., 2012], which can be exploited in image reconstruction [Bouza Domínguez and Bérubé-Lauzière, 2012; Leblond et al., 2009].

Figure 2.5 Time domain measurement.

2.7.2 Frequency-Domain (FD) Systems

Such systems resort to an amplitude modulated source at high frequency (a few hundred MHz). The reduction of amplitude and the phase shift of the transmitted light signal after 1. In reality, it is not at all frequencies, because the laser pulse width is finite, but for practical purposes, TD systems, especially those based on time-correlated single photon counting - TCSPC - can be considered to deliver information at all frequencies, because their bandwidth goes up to a few hundreds of GHz.

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its propagation through the tissue are measured for each frequency. Figure 2.6 shows a FD measurement. FD systems are less expensive and easier to use than TD systems, because only a finite frequency range can be measured (typically from 100 MHz to 1 GHz) due to limitations in modulating light sources at very high frequencies, they do not provide as complete data as TD systems [Gibson et al., 2005].

Figure 2.6 Frequency domain measurement.

2.7.3 Continuous-Wave (CW) Systems

CW systems measure the intensity of light transmitted through a tissue (Figure 2.7). They resort sources that emit light of constant intensity. The transmitted intensity is a function of the light source used, the tissue and the positions of both the source and the detector. These systems are relatively easy and inexpensive to implement. They provide best signal to noise performance since only light of constant intensity in time is measured. However, neither the information on the fluorescence lifetime nor the information on the absorption and scattering coefficients can be distinguished by these scanners since they solely mea-sure intensity. In addition, the signals outputted by these systems are depth-sensitive. This means they are more dependent on optical properties of the tissue below the surface than the optical properties associated to deeper structures [Gibson et al., 2005; Ntziachristos et al., 2005].

The information content and complexity of hardware and imaging software increases from CW to FD and TD systems. However, CW systems allow for much faster data acquisition than TD ones [Hielscher, 2005], although this tends to be less and less the case with tech-nological advances in TCSPC [Becker, 2012].

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2.8. IMAGING GEOMETRY 15

Figure 2.7 Continuous-Wave measurement.

2.8 Imaging Geometry

The geometry of optical measurements is also an important factor in obtaining the data necessary for tomographic reconstruction. Generally, a data set acquired around the sub-ject is essential. This can be done either by detection of photons from the subsub-ject while it is illuminated over different angles, or different detection angles while the position of illumination is fixed, or both. In particular, two geometries that are often encountered are the epi-illumination and transillumination configurations.

2.8.1 Epi-illumination Configuration

This configuration refers to illuminating an object on a given side and detecting the light emerging from the same side as shown in Figure 2.8a. In the context of fluorescence, this is also known as fluorescence reflectance imaging (FRI), whereby illumination light impinging the surface will penetrate the tissue and propagate diffusely inside it and excite fluorochromes therein. The faint fluorescent light reaching the surface and exiting the tissue can then be captured with a sensitive detector, such as a CCD [Ntziachristos, 2006]. Although epi-illumination is widely used in the field of fluorescence molecular imaging [Ke et al., 2003; Ntziachristos, 2006; Zaheer et al., 2001], it has some significant drawbacks as it does not allow sampling with high efficiency regions remote from where light is injected. An example would be when a fluorophore is mostly located close to the surface on the opposite side from where light is injected, see [Lapointe et al., 2012] for a full discussion on this. Moreover, with an epi-illumination configuration, the detection of fluorescence light, which is faint, is generally more contaminated by light from the illumination source which is typically several orders of magnitude more intense. Regardless of these limitations, a majority of scanners are designed with an epi-illumination configuration [ART Advanced

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Research Technologies Inc., 2004; Xenogen Corporation, 2005; Zacharakis et al., 2005] due to the simplicity.

Figure 2.8 a) Epi-illumination imaging configuration b) Transillumination imaging configuration c) Tomographic configuration.

2.8.2 Transillumination Configuration

In transillumination imaging, an object is illuminated on a given side and light on the opposite side is detected, see Figure 2.8b. An advantage of such a configuration is that detection of fluorescence is less contaminated by excitation light since excitation light is necessarily more attenuated compare to epi-illumination. Indeed, in some studies, it was further shown that using transillumination provides more contrasted images [Ntziachristos, 2006; Ntziachristos et al., 2005]. Besides, another advantage of transillumination imaging is that autofluorescence does not appear in the detected signal, so images tend to have better contrast than those obtained with epi-illumination [Leblond et al., 2010]. However, when a fluorophore is more concentrated near to where excitation light is incident, it appears that epi-illumination will provide for stronger signal. The signal detected with a transillumination configuration is highly influenced by the optical properties and variations in the geometry of the subject. To reduce the effect of geometry, some systems used fluid-filled chamber wherein the animal is immersed [Lasser et al., 2007] in order to simplify the geometry (e.g. cylindrical geometry).

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2.9. ALL-AROUND DETECTION 17

2.9 All-Around Detection

For the purpose of small animal imaging, using solely either epi- or trans-illumination imaging is not sufficient to find the accurate position of inclusions [Lapointe et al., 2012]. This is due to the presence of optically dense organs (heart, liver, etc.) which strongly scatter the light and increase the image reconstruction error. Hence, obtaining the data over 360◦ is essential that should contain the data obtained via both epi-illumination measurements from backscattered light and transillumination measurements (Figure 2.8c). The full angle measurement provides reliable data that makes it possible to detect deeper through the biological tissue compared to transillumination measurements. In addition, it is possible to obtain the images with better spatial resolution compared to epi-illumination measurements.

2.10 Available Imaging Systems

This section presents a summary of available imaging systems with short discussions on their positive and negative aspects.

2.10.1 Scanners of Ntziachristos’ group

This group has developed several prototypes, which will now be summarized. First prototype

The first DOT scanner prototype introduced by Ntziachristos’ group acquired images in CW measurement mode [Ntziachristos et al., 2002a] (Figure 2.9). Excitation and detection were performed through a set of 24 and 36 optical fibers, respectively, while the excitation source was a 675 nm laser diode2 _{coupled to the excitation fibers via a beam splitter.}

The data were acquired by a CCD via the detection fibers. This configuration allowed for localizing the fluorophore signal in 3D in a target.

Second prototype

The second prototype of this group is capable of acquiring planar fluorescence images of small animals [Ntziachristos et al., 2004]. As shown in Figure 2.10, the excitation source is a CW laser diode emitting at 672 nm. The excitation light reaches the target via a set of fiber optics. Detection is performed with a cooled CCD camera that acquires CW data. The animal is submersed in a fluid that mimics the optical properties of tissue and contained in a water tight chamber; this is to simplify the geometry of the object being

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Figure 2.9 Ntziachristos’ first prototype [Ntziachristos et al., 2002a].

imaged.

This prototype can acquire the data in both epi- and transillumination mode, but the amount of data is limited from the fact that the camera and subject are fixed with respect to one another. Keeping the animal in a fluid brings additional scattering and absorption which reduces the quality of the acquired signals.

Figure 2.10 Ntziachristos’ second prototype [Ntziachristos et al., 2004].

Third prototype

The third scanner is similar to its predecessor, but it can acquire data only in an epi-illumination geometry [Zacharakis et al., 2005], Figure 2.11. The excitation source has been replaced by a blue laser emitting in the range of 488 nm to 514 nm. This is to excite

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2.10. AVAILABLE IMAGING SYSTEMS 19 green fluorescent proteins (GFPs) which are of great interest to biologists.

This scanner has the same limitation as its predecessors which is to immerse the animal in a fluid-filled chamber.

Figure 2.11 Ntziachristos’ third prototype [Zacharakis et al., 2005]. Fourth prototype

The fourth scanner developed by this group was presented in 2005 [Turner et al., 2005]. It performs TD measurement with a time-gated ICCD camera as the detector and a Ti-Sapphire laser as the excitation source. Figure 2.12 depicts the configuration of this scanner.

There is one improvement that has been made on this imager and that is the rotatable animal platform. However, since the illumination laser and camera are fixed with respect to one another, this scanner can obtain data only in transillumination mode.

Another limitation of this scanner comes from the ICCD camera that can acquire only small part of the TD signal per input laser pulse sequence; hence it requires long acquisition times.

Fifth prototype

In 2007, a modified version of the third prototype was introduced [Lasser et al., 2007]. This scanner is shown in Figure 2.13 and has a non-contact configuration in which the animal is placed on a rotational stage and is illuminated with 748 nm laser light. This lets the animal to be rotated over 360◦. In this configuration, the animal is held vertically with two portions of cylindrical tubes and it does not need to be immersed in a matching fluid. This scanner provides data only in a transillumination configuration.

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Figure 2.12 Ntziachristos’ fourth prototype [Turner et al., 2005].

Figure 2.13 Ntziachristos’ fifth prototype [Lasser et al., 2007].

2.10.2 Sevick-Muraca’s Scanner

The group of Sevick-Muraca presented an imager in 2002 that can acquire both CW and FD data [Sevick-Muraca, 2004]. Figure 2.14 shows the configuration of the scanner. In this scanner, amplitude modulated laser light (100 MHz) that can propagate either spherically or as a planar wave illuminates the subject. The phase-delayed and attenuated emitted signal is detected via an image intensifier by a CCD camera.

The fact that the camera is fixed relative to the subject, and that the configuration only allows acquiring epi-illumination data are the main drawbacks of this scanner. However, there is no need to put the animal in a fluid-filled chamber and the device is considered to be non-contact since there is no fiber optic interfaced to the animal.

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2.10. AVAILABLE IMAGING SYSTEMS 21

Figure 2.14 Sevick-Muraca’s scanner [Sevick-Muraca, 2004].

2.10.3 The Schulz Scanner

The scanner developed by Schulz’s group [Schulz et al., 2005] (Figure 2.15), has a 670 nm laser diode as an excitation source that is placed on a linear stage moving forward and backward longitudinally along the animal which is positioned horizontally. A CCD camera serves as the detector. It is interfaced to a lens to cover the whole animal’s body at the distance of 6 cm. The whole set-up comprising the laser and CCD camera rotates around the animal lying horizontally. This CW imager provides transillumination data from several projection angles. Figure 2.15 depicts the scanner configuration.

2.10.4 Advanced Research Technologies Inc (ART) Scanner

ART was a Montréal-based company that commercialized a non-contact system for small animal imaging exploiting the fluorescence lifetime [ART Advanced Research Technologies Inc., 2004]. The scanner performs TD measurements and is equipped with a pulsed laser source that shines onto the animal short light pulses with a width on the order of 70 ps FWHM. Detection is performed with a photomultiplier tube sensitive to wavelengths within the spectral band of 450 - 900 nm coupled with a time correlated single photon counting (TCSPC) card. This scanner incorporates a laser profilometer to measure the outer surface geometry of the animal (Figure 2.16). Besides, galvanometer mirrors allow for scanning the laser illumination and detection spots over the animal rapidly. However,

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Figure 2.15 Schulz’s scanner [Schulz et al., 2005].

this architecture provides only epi-illumination images. Hence, the ART system is severely limited in its ability to image at depth.

Figure 2.16 ART preclinical scanner [ART Advanced Research Technologies Inc., 2004].

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2.10. AVAILABLE IMAGING SYSTEMS 23

2.10.5 Scanners of the PerkinElmer Suite

PerkinElmer has been very active in the small animal imaging business by acquiring the smaller-scale companies Caliper Life Sciences (that previously acquired Xenogen Corp.), CRI and VisEn that developed small animal imaging systems.

Xenogen

Xenogen Corp. was the first company to successfully commercialize a small animal molec-ular imaging scanner exploiting fluorescence and bioluminescence [Xenogen Corporation, 2005]. This scanner ultimately led to the IVIS series (Figure 2.17).

Figure 2.17 IVIS scanner [PerkinElmer, 2014].

This is a planar imaging scanner. In the course of its operations, Xenogen had also de-veloped a scanner capable of acquiring CW tomographic data (Figure 2.18). This was equipped with a cooled ICCD camera for the detection and a rotary mirror that allowed to acquire planar data images from 8 different angles around the subject. The animal bed had two degrees of freedom for not interfering with the mirror rotation. This was a non-contact system in epi-illumination data. It involved a complex mechanical configuration since the animal should be repositioned to locate in front of the mirror for each illumina-tion angle.

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Figure 2.18 Xenogen scanner [Xenogen Corporation, 2005].

Maestro

Another scanner that is now part of the PerkinElmer suite was formerly developed by Cam-bridge Research & Instrumentation Inc (CRI). It is shown in Figure 2.19. The strength of this system lies on its ability to separate the autofluorescence signal from the signal of a fluorescent agent [Cambridge Research & Instrumentation, 2007]. Using a 12 bits CCD camera as a detector, this imaging system obtains images in CW with transillumination measurement that can quantify the intensity of the fluorescent signal. This system does not, however, provide tomographic data.

Figure 2.19 Maestro scanner [Cambridge Research & Instrumentation, 2007].

VisEn Medical

This small animal scanner was commercialized and developed as an outcome of the work in Ntziachristos’ laboratory by VisEn medical, a spin-off from Massachusetts General Hos-pital. This scanner, which is now the basis for the FMT- 1000 -2000, and -4000 line of products from PerkinElmer, acquires surface images of an animal containing a fluorescence

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2.10. AVAILABLE IMAGING SYSTEMS 25 distribution. These images are obtained from a transillumination configuration by using two CW laser diodes and a 12 bits CCD camera as a detector [VisEnMedical, 2010]. The disadvantages of this imager come from the use of a fluid-filled animal chamber and fixed source and detection positions. Figure 2.20 shows a schematic of this system.

Figure 2.20 Visen medical scanner [VisEnMedical, 2010].

2.10.6 The TomOptUS Scanner from the Université de Sherbrooke

A TD small animal scanner that can acquire data in both epi- & trans-illumination config-urations is developed by TomOptUS group [Lapointe et al., 2012]. The excitation source of this scanner is an ultra- short pulse laser (Tsunami, Spectra-Physics) and the data are acquired with a series of PMTs positioned around the animal (Figure 2.21) and that can be rotated with a turnable plate. The subject is kept vertically tied up during the acquisi-tion. The unique feature of this scanner is that using a rotary plate along with its mounted PMTs, provides a large quantity of data in both transillumination and epi-illumination configurations.

2.10.7 Kodak

Kodak has developed medical imaging equipment; among which a small animal in vivo imaging system, which was later taken over by Carestream now part of Bruker [Care-streamHealth, 2007], Figure 2.22. The excitation source in this series of scanners is a Xenon lamp (175 W) filtered to provide different illumination wavelength ranges and the detector is a 16 bits CCD camera. These scanners acquire data in CW and epi-illumination and are limited in providing planar images only of the subject. They come, however, ready to be coupled to an X-ray module for bimodal imaging.

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Figure 2.21 TomOptUS scanner [Lapointe et al., 2012].

Figure 2.22 Kodak scanner [CarestreamHealth, 2007].

2.10.8 Pearl

The Pearl scanner is developed by LI-COR Biosciences. Excitation is performed with a set of two lasers (one set at 680 nm and the other at 780 nm). Detection is carried with a CCD camera. Employing two crossed lasers for illumination at the same time allows for uniform illumination. This scanner is, however, limited in acquiring images only in epi-illumination and providing solely planar data. A schematic of the scanner is presented in Figure 2.23.

2.10.9 Summary of Available Imaging Systems

Table 2.1 lists small animal optical imaging systems both commercially available and developed in research laboratories. The vast majority resort to CW measurements and to either epi- or transillumination, but not both, with the exception of the TomOptUS TD

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2.11. CONCLUSION 27

Figure 2.23 Pearl scanner [LI-COR, 2014].

scanner. Among the systems presented in the table, the QOS system, located at CIMS with 4 degrees of freedom, has potentials for providing tomographic images. The QOS scanner is discussed in detail in the next chapter.

2.11 Conclusion

After having investigated background material for the field of DOT imaging and scanners including different types of cameras, their limitations, epi- and transillumination imaging, and available imaging systems, one may hypothesize that a system combining both types of measurement configurations (i.e. epi- and trans-illumination) equipped with a highly sensitive camera should provide for more probing sensitivity throughout the tissue volume. To investigate this hypothesis, the improvement of the QOS imaging system has been undertaken. Next chapter will describe in depth the basic configuration of the QOS system.

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A cquired Data System Non-con tact Planar Data Angular Data Epi-illumination T ransillu m ination CW FD TD Ntzia1 X X X X Ntzia2 X X X X Ntzia3 X X X Ntzia4 X X X Ntzia5 X X X X Sevic k-Muraca X X X X X Sc h ulz X X X X AR T X X X X X Xenogen X X X X T omOptUS X X X X X Maestro X X X X Visen Medi-cal X X X X K o dak in-viv o X X X X P earl X X X X QOS X X X X T able 2.1 Comparison of differen t typ es of small animal optical imaging systems. A dapted from [Letendre-Jauniaux, 2012].

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CHAPTER 3 The QOS Imager

The available imaging systems have been previously introduced. This chapter describes the QOS optical small animal imager, located at CIMS, since it was for that system that the work described in the present thesis was dedicated for.

3.1 QOS Hardware

The QOS is a scanner developed by the company Quidd inc for preclinical research on small animals [Quidd Inc., 2009] (Figure 3.1). It is capable of acquiring images in CW measurement mode; wherein subjects can be exposed to light within the visible and NIR ranges. This imaging system is equipped with three translation stages, two to move hori-zontally the platform holding the animal to a desired location along the x and y axes and one for adjusting the height of the detector along the z axis. Additionally, the camera can be rotated from -60◦ to +60◦ with respect to the vertical axis with a rotation stage to allow the camera to move around the subject (T stage). Table 3.1 shows the movement ranges.

The detector is a 12 bits cooled CCD camera (custom Andor Newton camera, 1536 x 1024 pixels, frame rate 9 images/s, sensitivity 1.2 photons/s/pixel). The excitation source is a white light halogen lamp (250 W) that can be filtered through 16 excitation filters mounted on two motorized wheels. It is thus possible to have several wavelengths within the visible and NIR ranges for illuminating the subject. The range of detected wavelengths can also be selected within the visible and NIR with two filter wheels containing a total of 16 interchangeable filters (emission filters). The field of view (FOV) and the depth of field can be adjusted by way of motorized sets of lenses and controlling apertures respectively;

Table 3.1 Total axes movement

Axis Total Distance Dimension

X 520 mm

Y 600 mm

Z 900 mm

T 120 degree

where the FOV can be changed from 13.8 x 9.2 mm2 for small-scale high spatial resolution imag-ing up to 300 x 200 mm2 for large-scale lower resolution. More-over, this scanner is equipped with an anesthesia module to

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(a) (b)

Figure 3.1 The QOS scanner. (a) The original configuration of QOS scanner. (b) Front-side view.

keep the animal still (inhaling isoflurane gas) while performing in vivo experi-ments. Table 3.2 describes the functions of the 6 major components of the QOS.

3.2 QOS Software

Table 3.2 The functions of the QOS major components.

Component Function Cooled CCD Camera Detector Halogen Lamp (250 W) Source

Emission Filters Filtering emitted light Excitation Filters Filtering excitation light

Motor Block Providing 4 degrees of freedom Anesthesia Module Anesthetize the animal

The QOS is entirely computer-controlled by a sophisticated software, so-called the QOSoft that has been written mainly in C# and C++ and partially in visual basic (VB) and the C pro-graming languages. Figure 3.2 depicts a block diagram of the QOSoft. The QOSoft, yet, con-tains more projects and classes than shown in Figure 3.2, which is only partial view of the main classes and projects of the software. The

QOSoft gives the ability to users to directly interact with each hardware component of the QOS and contains some image analysis tools as well. The complexity of the QOSoft comes from its large-scale and the cross-language interfacing issues. This software can be separated into two subsets: the Quidd image protocol (QIP) and Quidd hardware protocol (QHP).

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3.3. QOS LIMITATIONS 31

Figure 3.2 Brief block diagram of QOSoft.

The QHP is controlling each hardware component of the QOS by communicating with these components, while QIP is responsible for gathering and processing the information such as saving the images, scaling, applying a colormap, etc.

The QOSoft is unfortunately not a well-documented software and the lack of resources to provide the information about the template and coding convention of this software has caused lots of problems in understanding and modifying it.

3.3 QOS Limitations

Although the QOS has been used extensively by researchers and biologists [Bouchard et al., 2013; Lebel et al., 2013; Ranyuk et al., 2013], it has sensitivity limitations which preclude the detection of bioluminescence signals and also limit the ability to work at low fluorophore concentrations or low fluorescence yields in fluorescence imaging. This also makes image acquisition slow by requiring long acquisition times.

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(a) (b)

Figure 3.3 Lack of sensitivity affects the results in bioluminescence experi-ments. (a) In cellulo bioluminescence experiment. (b) In vivo bioluminescence experiment.

To show the impact of these limitations, the results of bioluminescence experiments are presented in Figure 3.3. Detection of cells in an in cellulo bioluminescence experiment is shown in Figure 3.3a. It is shown that it was possible to detect down to 247 cells with the original configuration of the QOS. The exposure time of the camera was 5 minutes. It will be shown later that by using a more sensitive camera, it is possible to detect down to 100 cells with a lower exposure time. Figure 3.3b depicts the results obtained in an in vivo bioluminescence experiment. The image shows a faint signal that is barely detectable. Dynamic contrast-enhanced imaging is an imaging technique in which a series of images are acquired over time following the injection of a contrast agent in a living organism. It allows measuring the pharmacokinetics of the injected agent as it passes through different organs and tissues, and so can provide information on these tissues and organs, such as the presence of a disease, the binding of a targeted agent, etc. It can also serve to provide contrast to different features. However, the time dimension added in imaging sequences results in a huge amount of data. This makes data analysis of series of images or reconstructed volumes more complex and time consuming. Hence, dimension reduction techniques, such as principal component analysis (PCA), can be applied to make the data more easily analyzable, or to better capture the information content.

Briefly, in the PCA method, the signal associated with each pixel is considered as a function of time S(t). This function is decomposed along several vectors - the principal components or basis vectors, P1...PN obtained from the covariance matrix of the data. A signal S(t)

is then decomposed as S(t) = C1P1 + C2P2+ C3P3+...+ CNPN. In this decomposition,

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3.3. QOS LIMITATIONS 33 signals from which they were computed. This variance can be determined via the eigen-values (principal eigen-values) of the covariance matrix formed with the data. For this, the basis vectors Pi (i = 1, ..., N ) are placed in descending order according to their associated

principal values with low order P ’s containing more information. P2 to P4 were selected

for our experiment, with C2, C3 and C4 being used respectively as the R, G and B

com-ponents of a color image. To do this, a PCA analysis software has been developed in our group [Provencher, 2012] based on the procedure presented by Hillman and Moore [Hill-man and Moore, 2007].

[Provencher, 2012] and [Hillman and Moore, 2007] was to cluster pixels having similar de-compositions along the principal components, the rationale being that pixels having sim-ilar decompositions represent tissues having simsim-ilar kinetics for the injected fluorophore. This was then used to segment the organs of a nude mouse, thus allowing to easily obtain anatomical imaging with optical means, which is otherwise difficult. To do this, the animal was firstly injected with the non-specific ICG dye, then a series of in vivo fluorescence im-ages were acquired for five detection angles (placing the camera at five different positions with a 15◦ step between two successive ones). The images were qualitatively graded to determine the minimal injected dose to obtain satisfying results in the experimental con-ditions. One sample of the obtained images is shown in Figure 3.4 which is not as accurate as images obtained by Hillman and Moore [Hillman and Moore, 2007]. This occurs mostly due to non-homogeneities of the source of illumination and the relatively low sensitivity of the camera, leading also to long integration times. Nonetheless, the results demonstrate the feasibility of obtaining anatomical images with dynamic contrast with the QOS, but better can be achieved considering the results obtained by Hillman and Moore.

Figure 3.4 Results of PCA analysis [Provencher, 2012].

The previous examples illustrate the limitations of the QOS in specific applications. Fur-thermore, users increasingly require tomographic imaging capabilities with the QOS which cannot be achieved with the epi-illumination configuration of the QOS. Due to these

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lim-itations, two modifications have been undertaken to improve the functionality of the QOS.

3.4 Conclusion

This chapter explained the QOS basic configuration on both hardware and software sides. It was further shown that the QOS has encountered significant limitations with present-ing the results of the bioluminescence and PCA experiments obtained with the original QOS configuration. Next chapter will present the objectives of this project focused on eliminating the QOS limitations and thus improving its performance.

Mise en oeuvre d'un mode d'imagerie par transillumination et détection multi-vue à ultra-faible bruit dans l'imageur QOS[indice supérieur TM] pour imagerie moléculaire optique sur petit animal

UNIVERSITÉ DE SHERBROOKE

Faculté de génie

Département de génie électrique et de génie informatique

Mise en œuvre d’un mode d’imagerie par

transillumination et détection multi-vue à

ultra-faible bruit dans l’imageur QOS

TM

pour

imagerie moléculaire optique sur petit animal

Implementation of a transillumination mode and ultra-low noise

multi-view detection in the QOS

for small animal optical

molecular imaging

Mémoire de maîtrise

Specialité : génie électrique

Nikta ZARIF YUSSEFIAN

Jury: Yves BÉRUBÉ-LAUZIÈRE (Directeur)

Réjean FONTAINE (Rapporteur)

Martin LEPAGE (Évaluateur)

RÉSUMÉ

ABSTRACT

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

LIST OF ACRONYMS

CHAPTER 1

Introduction

1.1

Context

1.2

Overview of the Project

1.3

Contributions

1.4

Plan of Document

CHAPTER 2

State of the Art

2.1

DOT Imaging

2.1.1

Fluorescence Imaging

2.1.2

Bioluminescence Imaging

2.2

Molecular Imaging Probes

2.3

Phantoms

2.4

Multimodal Imaging

2.5

Charged-Coupled Device (CCD) Fundamentals

2.5.1

Dynamic Range

2.5.2

Quantum Efficiency

2.5.3

Noise

2.5.4

Signal to Noise Ratio (SNR)

2.5.5

Camera Sensitivity or Detection Limit

2.5.6

Spatial Resolution

2.6

Comparing Cameras

2.6.1

Charged Coupled Device (CCD) Cameras

2.6.2

The Complementary Metal-Oxide-Semiconductor (CMOS)

Camera

2.6.3

Intensified Charge Coupled Device (ICCD) Camera

2.6.4

The Electron Multiplying Charged Coupled Device

(EM-CCD) Camera

2.7

Types of Measurements in Optical Imaging

Sys-tems