UNIVERSIT `
A DI PISA
DIPARTIMENTO DI FISICA
Corso di Laurea Magistrale in Fisica
Tesi di Laurea Magistrale
Biodegradable nano-architectures
as contrast agents for X-ray imaging
Candidato:
Rosa D’Apice
Relatori:
Dr. Valerio Voliani
Prof.ssa Valeria Rosso
Contents
List of abbreviations iii
Introduction 1
1 Metal nanoparticles 3
1.1 Optical behavior . . . 4
1.2 Biological features . . . 7
1.2.1 Enhanced permeability and retention eect . . . 7
1.2.2 Renal and hepatic clearance . . . 9
2 Computed tomography 12 2.1 X-rays production . . . 12
2.2 X-rays interaction with matter . . . 15
2.2.1 Attenuation coe cient . . . 18
2.3 X-rays detection . . . 19
2.4 Image reconstruction . . . 20
2.4.1 The Filtred Back Projection . . . 22
2.4.2 Houns eld units . . . 24
2.5 Contrast Agents . . . 25
3 Passion fruit-like nano-architectures 27 3.1 Gold passion fruit-like nano-architectures . . . 29
3.2 X-ray experiments with gold nano-architectures . . . 34
3.2.1 PET/CT IRIS scanner . . . 35
3.2.2 Phantom studies . . . 36
3.2.3 Results . . . 37
4 Conclusions 49 A Synthesis and characterization techniques 51
A.1 Synthesis . . . 51
A.1.1 Gold nanostructures . . . 51
A.1.2 Platinum nanostructures . . . 52
A.1.3 Metal free nanostructures . . . 52
A.1.4 Modi ed poly(L-lysine)-Dota-Gd 5% (or Ce 5-10%) . . . 53
A.1.5 Gold thin-shell nanostructures . . . 53
A.2 Techniques for the nano-architectures characterization . . . 53
A.2.1 UV/visible spectroscopy . . . 54
A.2.2 Electron microscopy . . . 55
A.2.3 ICP-MS . . . 58
B Structural formulae 60
List of abbreviations
3D 3-Dimensional
AuPtSi Gold-platinum nanostructure
AuSi Gold nanostructure
AuSiCe Gold nanostructure with cerium AuSiGd Gold nanostructure with gadolinium AuSiT Gold thin-shell nanostructure
CA Contrast agent
CCD Charge coupled device
CT Computed tomography
EAE Energy absorption e ciency
EPR Enhanced permeability and retention eect FBP Filtered back projection
FDK Feldkamp-Davis-Kress
FS Full scan
HU Houns eld unit
ICP-MS Inductively coupled plasma mass spectrometry LSPR Localized surface plasmon resonance
MFSi Metal free nanostructure MRI Magnetic resonance imaging
NP Nanoparticle
PDT Photodynamic therapy
PET Photon emission tomography
PK Pharmacokinetics
PL Polylysine
PSS Polystyrene sulfonates PtSi Platinum nanostructure
PTT Photothermal therapy
RES Reticuloendothelial system
ROI Region of interest
SEM Scanning electron microscope
SERS Surface-enhanced Raman spectroscopy
SS Short scan
TEM Transmission electron microscope
TFT Thin lm transistor
Introduction
The work of this thesis was based on the study of contrast agents based on metal nanoparticles and their improvements for future clinical applications. As a mat-ter of fact, recent studies have demonstrated that metal nanoparticles have an enormous potential for biomedical applications. This is related to their pecu-liar chemical, physical and physiological behaviors. Moreover, the possibility to combine the nanoparticles with drugs and to modify their surface by targeting agents have paved the way to the named personalized medicine.
Until now, various imaging modalities have been developed, such as MRI, CT, PET, ultrasound and SERS, as an aid to image the presence and the state of a disease. These imaging methods dier in both techniques and instrumentation and more importantly require contrast agents with unique physiochemical prop-erties. This led to the development of various nanoparticulated contrast agents such as gold nanoparticles for their application in these imaging modalities. On the other hand, their translation from the bench to the clinical trials is hampered from in vivo accumulation issues. Indeed, metal nanoparticles are not degradable and their nal fate is usually the accumulation in liver and spleen, causing long term toxicity and interference with common medical diagnosis. In this contest, a novel family of inorganic metal nano-architectures able to jointly combine the most intriguing features of metal nanoparticles with their fast-degradation and potential renal clearance was introduced.
In this work, the optimization of the synthetic protocol to produce suitable nano-architectures for CT imaging is presented and the sensitivity of a micro-CT scanner is evaluated for the new contrast agents. First, the nanostructures were fully characterized by standard techniques, among which UV-vis spectropho-tometry, electronic microscopy (TEM-SEM) and ICP-MS. Furthermore, other synthesis and similar tests have been conducted on chelating agents. Finally, new-found nano-architecture imaging e ciency was evaluated by a micro-CT device and compared to standard nano-architectures, along with their
degrada-tion timescale. The development of this novel protocol strongly supports further insights on these innovative degradable nano-architectures suitable for imaging techniques that require high concentration of noble metals. Experimental work has been conducted at the Center for Nanotechnology Innovation@Nest, Istituto Italiano di Tecnologia and the X-ray properties of a novel contrast agent have been examinated at the Institute of Clinical Physiology of the National Research Council, in Pisa.
The work presented here is structured as follows.
In chapter 1 biological features such as enhanced permeability and retention eect will be discussed. This is the property by which nanoparticles of a certain size tend to accumulate in tumor tissues much more than they do in normal tissues. Moreover, local surface plasmon resonance, an optical behavior of metal nanoparticles, will be presented, along with the problem of accumulation.
In chapter 2 the physical features and operating principles of computed to-mography will be explained. The theory the image reconstruction is based on and the most used contrast agents in clinical imaging will be presented.
In chapter 3 inorganic metal nano-architectures that stand as promising tools to overcome the issue of accumulation of inorganic nanoparticles in organisms will be presented. A comparison between the physical and optical properties of gold nanostructures obtained using the regular and the new protocol will be made. The data obtained through experiments with X-rays will be presented and, nally, further studies on nanostructures, such as the introduction of chelating agents in their core, will be described.
In chapter 4 considerations over previous results are discussed and possible future developments are presented.
In appendix A all chemical protocols used for the synthesis of the standard and thin-shell gold nanostructures are presented along with those used for the synthesis of other chelating agents. Furthermore, some methods for the character-ization of metal nanostructures are described: UV/visible spectroscopy, electron microscopy and ICP-MS.
Chapter 1
Metal nanoparticles
Metal nanoparticles (NPs) are particularly interesting nanoscale systems because of the ease with which they can be synthesized and modi ed chemically. Those with a size between 1-100 nm are frequently spheres, but they can have many other shapes, such as rods, cages or stars. The ability to control their chemical, physical and physiological properties has led to a major improvement in research exploration of nanoparticles as well as increased the potential for biomedical applications. For this reason, noble metal nanoparticles such as gold, silver and platinum have elicited a lot of interest for biomedical applications as targeted drug delivery, therapy and diagnostic. Metal nanoparticles can be also chemically tailored for a speci c patient or disease [1].
In biomedical nanotechnology, nanoparticles oer many attractive features, including: 1) improved delivery of drugs that are poorly soluble in water and delivery of therapeutic agents into cancerous cells in high dose; 2) protection of drugs from harsh environments (e.g., the highly acidic environment in stomach or lysosomes and the high levels of proteases or other enzymes in the blood stream) before they can reach the target, leading also to an extended plasma half-life of the drug in the systemic circulation; 3) targeted delivery of drugs in a cell- or tissue-speci c manner so that the treatment e cacy can be maximized while sys-temic side eects are alleviated; 4) controlled release of drugs over a manageable period of time at precise doses and even realization of on-demand release using a more sophisticated, stimuli-responsive system; 5) co-delivery of multiple types of drugs and/or diagnostic agents (e.g., contrast agents) for combination therapy
(which has the potential to overcome multidrug resistance) and real-time readout on the treatment e cacy [2, 3].
In summary, dierent physical and chemical processes are still widely inves-tigated to synthesize metal nanoparticles, in order to obtain particles with the desired characteristics for multiple applications. However, unlike the most com-mon small molecule-based CAs, inorganic NPs are often rapidly sequestered from the blood and severely accumulate in liver, spleen and kidneys. Despite the at-tempts to reduce absorption by the reticuloendothelial system (RES), inorganic nanoparticles still end up in liver and spleen after circulation, resulting in low tumor targeting speci city and potential long-term toxicity [4]. In this work, biodegradable hollow silica nanocapsules embendding gold NPs have been de-signed to overcome the issue of accumulation, carrying on with the studies of the recent years.
1.1 Optical behavior
Metals are characterized by the presence of free electrons and, when the diameter of metallic nanostructures is in the 10-100 nm range, they can interact with the light through: (i) collective excitations of free electrons due to intraband transitions, giving rise to localized surface plasmon resonance (LSPR), (ii) transitions of electrons from occupied to empty bulk bands of a dierent index, called interband transitions, and (iii) surface dispersion or scattering of free or unbound electrons, when their mean free path is comparable to the dimensions of the nanostructures [5].
When the frequency of an incident electromagnetic eld matches the fre-quency of an intrinsic electronic oscillation a resonance occurs; this is a collective and coherent oscillation of the electronic cloud of the metal (see gure 1.1), called plasmon, which causes a displacement of the electrons from the nuclei, leading to the formation of various possible distributions in the nanostructure surface charges (e.g., for NPs with diameters between 10 and 30 nm, the plasmon mode is a dipole). Each type of surface charge distribution is characterized by a speci c resonance energy; gold has a strong visible plasmon resonance in the 520-540 nm range [6]. It was found that the spectrum is less intense, wider and red-shifted when the particle size increases [5]; however, for spherical particles smaller than 25 nm, the wavelength corresponding to the surface plasmon absorption
maxi-Figure 1.1: Collective oscillations of electrons in response to an external electro-magnetic eld.
mum (λmax) is almost independent from the particle size [7]. Another study has
revealed that λmaxof gold nanoparticles strongly depends on their aspect ratio r
[8], i.e., the length of the particle divided by the width of it, following the law: λmax= 420 + 95r (1.1)
The r-value increases until the NP becomes rod- or ellipsoidal-shaped, the plas-mon then appears to split into two modes corresponding to the oscillation along and perpendicular to the long axis of the particle [5, 9]. In general all the other geometrical shapes of gold nanoparticles (triangle [10], cube [11], shell [12]) ex-hibit a red-shifted LSPR band compared to their spherical analogs, since the shape aects the electron charge density on the particle surface [8]. The size and shape of the particles and the presence of other nanostructures in close proximity to each other are the factors that most in uence the extinction bands of LSPR in nanostructures [13, 14]. Plasmon shifts can give a measure of the distance be-tween pairs of NPs [15] and an empirical equation that can be used to estimate the interparticle separation from experimentally observed plasmon shifts in vitro or in biological systems has been derived [16, 17].
The LSPR peak position of gold nanostructures can be changed by tuning their size and shape during the synthesis process. In addition, strong electric elds are generated on the surface of the nanostructure, which can be used to greatly enhance the optical signals (e.g., uorescence or Raman scattering) aris-ing from molecular species in the vicinity of the surface. These light-matter interactions were employed to develop new probes or tools for biomedical appli-cations. Indeed, the gold LSPR peaks in the near-infrared region (NIR, 650=900 nm) are of the most interest in biomedical applications. At these wavelengths,
Figure 1.2: Absorption spectra of major light absorbers, haemoglobin and water. In the `biological window' - commonly used in imaging - between 650 and 900 nm, biological tissues are considered optically transparent.
light can readily penetrate deep into soft tissues1 thanks to the low absorption
from blood and water as well as the low scattering from soft tissues [18] (see gure 1.2). For example, nanoparticles are employed in photodynamic therapy (PDT) both as passive and active carrier for photosensitizer substances that chemically react with light to produce radicals or oxygen to kill target cells. Unlike PDT, photothermal therapy (PTT) does not require oxygen to interact with the target cells or tissues but it is based on absorption of radiation of suitable wavelenghts which is trasformed into heat, causing irreversible damage to the target tissue. Gold nanostrusctures have been used in this regard to enhance the absorption of light at their LSPR [19]. Nanoparticles are used in SERS for label-free molecular detection, in vitro diagnostics, in vivo spectroscopic detection and image-guided cancer surgery. SERS is highly eective in the NIR where light attenuation and auto uorescence from tissues are low [20].
1.2 Biological features
Pharmacokinetics (PK) describes a series of processes including absorption, distribution, metabolism and excretion that take place in the interaction between an organism and an exogenous substance (a drug or a contrast agent) when the exogenous substance is introduced into the body. In most cases, the drug/agent concentration in a body site is closely related to its concentration in systemic circulation. Therefore, to study PK of a contrast agent, blood concentration of the agent is dynamically monitored after administration until the elimination phase [4].
As reported in literature, gold nanoparticles have been found to be taken up by living cells and to be quite toxic for organisms [21, 22]. The studies also generally conclude that NPs are benign and biologically inert in the 10-100 nm size range. Notably, the average diameter of most of the metal inorganic nanoparticles, which show enormous potential for the therapy and/or diagnosis of a number of cancers [23], proposed for in vivo theranostics is over 20 nm [1, 24, 25]. Unluckily, excretion of objects above 10 nm occurs through liver and spleen into bile and feces but the excretion of intact metal nanoparticles from these pathways is an extremely slow and ine cient process, leading to unwanted accumulation which in turn causes increased toxicity and interference with common medical diagnosis [26, 27]. US Food and Drug Administration requires that agents injected into the human body, especially diagnostic agents, are cleared completely in a reasonable amount of time, avoiding persistence in the organism [26]; this requisite is currently not ful lled by any metal based nanoparticle [24].
1.2.1 Enhanced permeability and retention eect
One of the most important biological behavior of nanomaterials is related to the enhanced permeability and retention eect (EPR). It is interesting to remember that if a tumor or an in amed tissue is present in the organism, the vessels inside a tumor region are well-known for their leaky walls, allowing nanoparticles with the tting size to pass through e ciently and passively accu-mulate there preferentially. Tissues of cancerous tumors are poorly organised, are more acidic than normal tissues and the lymphatic system inside them is largely
Figure 1.3: Transport of nanoparticles with dierent sizes and small molecules through normal (left) and cancerous (right) tissues. In the rst one, the cells are well-organised and compacted and a functional lymphatic system is also present. The pH values of the tissues are dierent: the tumor tissue has a more acidic pH. The enhanced permeability and retention (EPR) eect is a unique feature of most tumors, allowing nanoparticles of appropriate sizes to accumulate more in cancerous tissues than in normal tissues.
absent or dysfunctional [28]. Due to the characteristically defective architecture of the vessels that supply oxygen and nutrients to these tissues [29, 30], the in-su cient drainage facilitates accumulation of nanoparticles in the tumor tissue (see gure 1.3). This eect is known as EPR, which is the basis for passive tumor targeting. These behaviors increase the passive accumulation of macromolecules and nano-objects in tumors, hampering their elimination from the organism [31]. The EPR eect is one of the most important features and results of tumor an-giogenesis. Solid tumors rely on rapid angiogenesis to maintain su cient supplies of nutrients and oxygen [32]. The rapid proliferation of endothelial cells during angiogenesis usually results in a reduced density of endothelial cells and thus in a loss of tight junctions and formation of large gaps between the cells. The presence of large gaps between the endothelial cells on the tumor vascular walls has been con rmed by direct visualization through optical and electron microscopy [33]. The underlying basement membrane of the blood vessels is mostly abnormal or missing [34]. In addition, tumor blood vessels lack pericytes and smooth muscle cell layers, making them more vulnerable to the high interstitial pressure and rapidly shifting blood ow. Depending on the tumor type, the openings in the
tumor vasculature are typically in the size range of 100800 nm (see gure 1.3). Particles smaller than this cuto size can extravasate from the blood vessels into the tumor interstitium. Despite its widespread use in the clinic, the passive targeting strategy has many limitations as the vessels formed through angiogen-esis are not evenly distributed in a solid tumor and the permeability may not be homogeneous throughout the tumor. For a small tumor or metastatic lesion that does not exhibit strong angiogenesis, the passive targeting e ciency based on the EPR eect will be rather limited [35]. Active targeting, which requires the conjugation of receptor speci c ligands that can promote site speci c targeting, will help address some of these issues [36].
The behavior and fate of nanoparticles in the body are dependent on their size, shape and surface charge. They can all strongly aect their performance with respect to EPR-based tumor targeting and clearance, both renal and hep-atic. These merits enable the NPs to detect a tumor more rapidly than the dye molecules2without severe accumulation in reticuloendothelial system organs,
making them very promising as drug delivers and imaging agents.
1.2.2 Renal and hepatic clearance
Excretion is an essential biological process that prevents damage and toxicity by eliminating unwanted materials from the body. There are two major excre-tion routes: the renal (urine) and hepatic (bile to feces) pathways for contrast agents. In general, renal excretion is preferred because the contrast agents can be rapidly eliminated while little cellular internalization/metabolism is involved, thus eectively minimizing body exposure to the contrast agents. The renal pathway relies on glomerular ltration in the kidneys; thus, the material size, charge and shape all aect the ltration e ciency. The ltration-size threshold of glomerular capillary walls is typically 6-8 nm. The particle size reduction below the kidney ltration threshold often implicates the alteration or loss of their unique features and functionalities. On the other hand, it should also be noted that excessively fast clearance hampers their therapeutic and diagnostic applications [1]. A promising approach to retain these valuable characteristics while ensuring fast clearance kinetics through renal excretion is the fabrication of biodegradable inorganic NPs [37].
Figure 1.4: Biodistribution and clearance of nanoparticles from the human body. Tissue defects as well as the size, targeting ligand and stealth properties of the nanoparticles are some of the major factors that aect biodistribution and clear-ance of nanoparticles.
In summary, renal ultra ltration is a size-, charge- and shape-dependent pro-cess due to the unique architecture and structure of the glomerular capillaries. Intravascular particles with an in vivo diameter of around 5-6 nm or less are able to e ciently pass through the pores of the glomerulus in the kidneys and are thus rapidly cleared from the circulatory system via bladder and urine. NPs with in-termediate in vivo diameter of up to 8 nm are ltered depending on their surface charge, whereas positive ones are eliminated faster than negative or neutral ones. For larger non-biodegradable particles, the hepatobiliary system represents the only alternative mode of elimination. NPs bypassing the renal ltration process will inevitably end up in the spleen and in the liver. Upon internalization by hepatocytes, NPs are subjected to intracellular enzymatic breakdown potentially leading to the disruption of the surface coating and dissolution of ions out of the inorganic crystal core as well as release of toxic metals. After being slowly
degraded by these cells, NP metabolites are potentially secreted by hepatocytes into the bile and nally excreted with the feces [38] (see gure 1.4).
It is clear that cellular uptake and cytotoxicity of NPs occur as a functions of size, shape, surface functionality/charge, aggregation state of NPs, concentration of NPs, type of cell-line, incubation conditions and type of culture media.
Chapter 2
Computed tomography
X-ray computed tomography (CT) is a well-established tissue-imaging technique employed in a variety of research and clinical settings. Speci cally, CT is a noninvasive clinical diagnostic tool that allows for 3D visual reconstruction and segmentation of tissues of interest. High-resolution CT systems can be used to perform nondestructive 3D imaging of a variety of tissue types and organ sys-tems, such as the gastrointestinal tract, cardiovascular system, renal tract, liver, lungs, bones, cartilage, tumorous tissue, etc. CT is one of the most prevalent diagnostic tools in terms of frequency of use and hospital availability. The prin-ciple components of a CT scanner are an X-ray source, a detector array and the software for image processing.
2.1 X-rays production
X-rays are a type of electromagnetic radiation with wavelengths roughly between 0.01 and 10 nm. Traditionally, X-rays are generated by a vacuum tube using high voltages to accelerate electrons from a cathode to a (usually) tungsten-alloy anode. Hitting the target, the accelerated electrons release electromagnetic radi-ation in the form of X-rays and the maximum energy of the radiradi-ation is limited by the energy of the incident electrons (see gure 2.1). Operating voltages of modern clinical CT scanners dier among instrument models and manufacturers
Figure 2.1: Schematic overview of an X-ray tube and its components. but generally fall between 80 and 150 kVp. X-rays are produced by two dierent atomic processes: (i) characteristic X-ray emission: the electron interacts with an atomic electron, ejecting it from its electronic shell; subsequently the outer-shell electron lls in the vacant shell thus emitting a characteristic X-ray with energy E = E2− E1, where E1 and E2 are respectevely the energies of the inner and
outer shell (see gure 2.2); (ii) bremsstrahlung: when the energetic electron hits the metal target, it interacts with the electric eld of the nucleus of an anode atoms and it is scattered; the electronic kinetic energy loss is then converted in an X-ray (see gure 2.3).
Figure 2.4 shows the theoretical bremsstrahlung spectrum and the character-istic spectral lines for a tungsten target. The Kα emissions really are a doublet
(the Kα1 and Kα2 lines), as is the Kβ emission, but for most measured X-ray
spectra, the two lines in each doublet are not resolved and they often are real-ized experimentally as one peak. Figure 2.4 also shows the shaded spectrum, which is the bremsstrahlung X-ray spectrum emitted from an X-ray tube. The low-energy X-rays in the spectrum are preferentially absorbed by the target itself (self absorption) and other structures in the X-ray tube.
Figure 2.2: Characteristic ray production. The energy of the characteristic X-ray is the dierence between the binding energies of the two shells. Each element in the periodic table has its own unique atomic shell binding energies and thus the energies of characteristic X-rays are unique to each atom.
Figure 2.3: Bremsstrahulung radiation. Electrons (e
-1, e-2) interacting with a
glancing blow emit a small fraction of their kinetic energy as an X-ray (E1,
E2), while electrons (e-3) hitting the nucleus directly can convert their own total
Figure 2.4: Idealized X-ray spectra are shown for 100-keV electrons striking a tungsten anode. The triangle-shaped theoretical spectrum produced inside the X-ray tube is attenuated by metallic structures in the tube, producing the illustrated shaded bremsstrahlung spectrum. Characteristic X-rays appear on the spectrum as line spectra. The doublets for Kα and Kβ characteristic X-rays
are illustrated.
2.2 X-rays interaction with matter
X-rays interact with matter in several dierent ways. Interactions, usually, can result in a local deposition of energy. The types of interactions are Rayleigh scattering, the photoelectric eect, Compton scattering and pair production (see gure 2.5) and are described below.
Rayleigh scattering: the mechanism of Rayleigh scattering involves the elastic (coherent) scattering of X-rays by atomic electrons. In this kind of scat-tering, the incident X-ray interacts with the electric eld of an orbiting electron and it is scattered as a result. The energy of the scattered X-ray is equal to the energy of the incident X-ray, so no ionization and deposition of energy occur in Rayleigh scattering.
Photoelectric eect: the incident X-ray is completely absorbed and all of its energy is transferred to an electron. If the electron is bound to its parent atom with binding energy EB, the energy of the incident X-ray
is given by E0 and E0 > EB, the electron is ejected with kinetic energy
X-ray is less than the binding energy of the electron (E0< EB), photoelectric
interaction with that electron is energetically unfeasible and will not occur. The binding energy EB associated with the K-shell is called the K-edge,
the one associated with the L-shell is called the L-edge and so on. Once an electron is liberated from its parent atom, a vacancy in one of the electron shells of the atom is created. A cascade of electron transitions will occur, resulting in the production of characteristic radiation in a way identical to that described in subsection 2.1.
Compton scattering: Compton scattering involves the inelastic (incoherent) scattering of an X-ray photon by an atomic electron. The energy E0 of
the X-ray photon is much greater than the binding energy of the atomic electron, therefore the Compton eect is considered to occur with essentially supposed free outer-shell electrons. The electron is ejected from the atom, causing ionization, and a scattered X-ray photon with energy E0 emerges
at an angle θ relative to the incident photon's trajectory. In Compton scattering, a relationship between the scattered photon energy and the scattering angle θ is observed:
E0 = E0 1 + E0
m0c2(1 − cosθ)
(2.1) where m0c2 is the mass of the electron and is equal to 511 keV. Equation
2.1 qualitatively implies that the energy of the scattered X-ray photon gets smaller as the scattering angle increases and this eect is ampli ed at higher incident photon energies.
Pair production: pair production can occur when an incident X-ray with en-ergy E0 > 1.022 MeV interacts with the electric eld of an atom and an
electron (e=)-positron (e+) ion pair is formed in the following interaction:
E0= 2m0c2+ T++ T− (2.2)
where T+and T-are the positron and electron kinetic energies, respectively,
and m0 is the rest mass of the electron (positron). Pair production does
2.2.1 Attenuation coe cient
The interaction mechanisms discussed above combine to produce attenuation of the incident X-ray photon beam as it passes through matter. Attenuation is the removal of X-ray photons from the X-ray beam by either absorption or scattering events. If a X-ray photon beam of intensity I is incident upon a thin slab of material of thickness dx with a probability of interaction μ, the intensity loss of the photon beam is given by:
dI = −µIdx (2.3)
Rearranging and integrating, the solution is
I = I0e−µt (2.4)
where the units of thickeness (t) are typically cm and so the units of μ must be cm=1; μ is called the linear attenuation coe cient. In case of inhomogeneous
materials and polychromatic X-rays: I =
ˆ
I00(E)e−´µ(E,x)dxdE (2.5) where I'0 is the beam intensity per unit energy interval, thus I0 is given by the
integral of I'0(E) over the entire range of available energy in the spectrum.
The value of μ represents the probability per centimeter thickness of matter that an X-ray photon will be attenuated and also it is the sum of interaction probabilities of all interaction mechanisms1:
µ = σR+ σph+ σC+ σpair (2.6)
The linear attenuation coe cient describes the attenuation properties of a speci c material at a speci c X-ray energy, but the value of the linear attenuation coef- cient will depend linearly also on the density of the material. Since μ changes proportionally with the density of the material, an easy way to compensate for density is to normalize μ by the density (ρ), resulting in the mass attenuation coe cient (µ
ρ); the units of the mass attenuation coe cient are cm2/g (see gure
1In case of CT, only the Compton scattering and photoelectric interaction have relevance;
they have, respectively, a dependency on Z and E of Z4
E3 and ZE (but in diagnostic range of
Figure 2.6: Example of mass attenuation coe cient as a function of X-ray energy. As shown, while the energy increases, the mass attenuation coe cient decreases. 2.6).
2.3 X-rays detection
A radiation detector can be used to measure the intensity of the X-rays transmit-ted through an object. The purpose of the detector is to convert the distribution of incident X-ray ux into an electrical signal, which can be handled by conven-tional electronic techniques and then digitized for further processing and analysis on a computer. X-ray detectors can be classi ed as direct detectors or indirect detectors. A direct detector records the electrical charge directly resulting from ionization of the atoms in the detector by the incident X-ray. Flat panel detectors in selenium are commonly used in X-ray imaging. Indirect detection strategies employ X-ray scintillators: an X-ray interacts with the scintillator, causing a burst of light photons to be emitted. Light photons then propagate by optical diusion until they reach the photodetector (e.g., a lm emulsion or Si TFT). For this kind of detection, at panel detectors as Thallium-doped Cesium Iodide (CsI:Tl) and Therbium-doped Gadolinium Oxysul de (Gd2OS:Tb) are the most
used, but also image plate detectors (e.g. BaFBr:Eu) and Charge-Couplet Device (CCD) are employed.
X-ray detectors need to interact with incident X-ray photons to record their presence and the X-rays that pass through the detector unattenuated are es-sentially lost. The design goal of all X-ray detectors for medical imaging is to maximize the absorption e ciency, given the constraints of other performance parameters (such as spatial resolution) as well. The energy absorption e ciency (EAE) of an X-ray charge-integrating detector is given by:
EAE = ´EM AX E=0 I 0 0(E)E( µen(E) µ(E) )(1 − e −µ(E)x)dE ´EM AX E=0 I 0 0(E)EdE (2.7) The denominator is simply the amount of incident energy upon the X-ray detector (per unit area). The fraction of photons attenuated in the detector is given by the (1 − e−µ(E)x) term and the amount of energy absorbed in the detector per
attenuated X-ray photon is given by E(µen(E)
µ(E) )[39].
2.4 Image reconstruction
CT is a diagnostic technique that allows to reconstruct cross sections of the patient's body. It is based on the transmission of X-rays through the body and those that are not absorbed by the body encounter the detector array, which records the X-rays ux. In the latest con gurations the detectors are xed and the source spins around the patient to build up a 360° dataset where the X-rays absorption by the patient from all angles is known (see gure 2.7). More precisely, the detector signal at a point (u,v) of its sensitive area will depend on the incident X-ray intensity I(u,v) and on the linear attenuation coe cients μ(E,x,y,z) of the patient's body. Thus the results after the acquisition of the raw scan data in CT are a set of μ(E,x,y,z) line integrals over all the possible lines intersecting the object and the detector, for all the gantry angles. Generally, in a tomographic technique, a line integral represents the integral of some parameter of the object along a line and in the CT case, the parameter is the total attenuation suered by an X-ray beam as it travels in a straight line through the object (see gure 2.8):
pθ(x0) =
ˆ +∞ −∞
Figure 2.7: A schematic of a modern CT scanner. The increased number of detectors and the increased collimation width have led to a fan-beam geometry, which allows to completely avoid the translation step . Nevertheless, the acquired data in fan-beam geometry have some redundancies that shall be addressed before reconstruction.
By rewriting the equation using a delta function: R {f (x, y)} = pθ(x0) = ˆ +∞ −∞ ˆ +∞ −∞
f (x, y)δ(xcosθ + ysinθ − x0)dxdy (2.9) which represents the Radon transform or sinogram, in parallel-beam geometry, of the function object f(x,y) and maps the object space (x,y) in Radon space or projection space (x',θ). Every point in Radon space corresponds to an integral line in the spatial domain (see gure 2.9). The equation xcosθ + ysinθ = x0
represents a ray that travels through the object with an angle of incidence θ and a distance x' with respect to the origin.
Figure 2.9: Basic scheme of the data acquisition process in a CT scan for a simple object (in parallel-beam geometry). The set of projection data at several angles is called the sinogram (displayed on the right). On the left, the acquisition process has been schematized as projection lines relative to just three angles. The corresponding rows of the sinogram have been highlighted with dashed lines. Each row of the sinogram represents the 1D radiographic projection of the object slice for a given angle θ . Typically, a CT scan is performed over 5001000 angles of projection, with roughly 600800 lines of projection for each angle [40].
2.4.1 The Filtred Back Projection
Knowing the Radon transform of an object allows to reconstruct its structure: if an in nite number of one-dimensional projections of the object made by an in nite number of dierent angles could be acquired, a perfect reconstruction of the original object f(x,y) would be computed and the reconstruction process
would consist in calculating the inverse Radon trasform; but in practice this is not possible: only a nite number of projections can be taken.
The algorithm that is currently used in almost all applications of straight ray tomography is the ltered back projection algorithm (FBP). It has been shown to be extremely accurate and will be derived by using the Fourier slice theorem2. Considering the inverse Fourier transform, the function object can be
expressed as: f (x, y) = ˆ +∞ −∞ ˆ +∞ −∞
F (u, v)e2πi(ux+uy)dudv. (2.10)
Changing the rectangular coordinate system in the frequency domain (u,v) for a polar coordinate system (ν, θ) as:
u = νcosθ (2.11)
v = νsinθ (2.12)
and rewriting also the dierentials by using:
dudv = νdνdθ (2.13)
the equation 2.10, together with the Fourier slice theorem (see footnote 2), be-comes: f (x, y) = ˆ π 0 dθ ˆ +∞ −∞ P (ν, θ)|ν|e2πiνx0dν. (2.14) This integral may be expressed as:
f (x, y) = ˆ π 0 dθ ˆ +∞ −∞ pθ(x0)h(xcosθ + ysinθ)dx0 (2.15) f (x, y) = ˆ π 0 pθ(x0)⊗h(x0, θ)dθ (2.16)
where h(x0)is the inverse trasform of a lter function with frequency response
given by |ν| (see gure 2.10)3; pθ(x0)⊗h(x0, θ)is called ltered projection. For
a given value of θ, every point (x,y) in the image plane corresponds to a value x0 = xcosθ + ysinθ. The equation 2.15 expresses that each ltered projection is
2The one-dimensional Fourier transform of the sinogram to a given angle θ coincides with
the central slice section of the bidimensional Fourier transform of f(x,y) inclined at the same angle. P (ν, θ) = F (u, v)|v0=0
Figure 2.10: Schematic view of the ramp lter |ν| in the frequency space and its transform h(x0)in the spatial domain.
back projected. The resulting projections for dierent angles are then added to form the estimate of f(x, y). Interestingly, the ramp lter shape helps to enhance image edges by increasing high frequencies intensity and dampening low frequencies that give no information. Unfortunately, this lter worsens image noise that lies in high frequencies.
To reconstruct cone-beam data from circular scan orbits, the Feldkamp-Davis-Kress (FDK) back-projection algorithm is generally employed. However, exact 3D reconstruction for a circular trajectory is impossible because the set of cone-beam projection data from circular scans do not satisfy Tuy-Smith's con-dition. Brie y, the Tuy-Smith's su ciency condition is satis ed when all the planes intersecting the object also intersect the trajectory of the source, at least in one point [41]. Despite its approximated nature, the image quality of the re-constructed image is acceptable for small cone angles. When the axial aperture of the cone-beam is increased, an underestimation of the reconstructed μ is ob-served in peripheral slices. Indeed, the FDK algorithm is an exact reconstruction algorithm only for objects that are shift invariant along the z-axis (e.g., cylinders or prisms).
2.4.2 Houns eld units
The reconstructed images are typically displayed in grayscale, with its intensity depending on the attenuation of the dierent tissues/substances in the eld of view. More strongly X-ray attenuating substances appear white or light gray, while weakly attenuating substances appear dark gray or black. X-ray attenua-tion and therefore image contrast results from dierential absorpattenua-tion or scattering
of the X-rays by tissues. Air absorbs X-rays very weakly, biological tissues absorb some of the X-rays and bones absorb X-rays strongly. X-ray attenuation µx in
CT is de ned by the Houns eld scale, where the attenuation of any substance is given in Houns eld Units by the following equation:
CT number = 1000 (µx− µwater)
(µwater− µair) (2.17)
On this scale, the attenuation of air is therefore -1000 HU, the attenuation of water is 0 HU, soft tissues range from -100 to 100 HU and bones range from 400 to 1000 HU.
2.5 Contrast Agents
While dierent types of body tissues can cause image contrast, it can be chal-lenging to image and identify the interface between two adjacent tissues (e.g., liver/tumor) or image soft tissues (e.g., clot) in contact with blood or other physiological uids. However, greater dierences in CT attenuation will facili-tate the process and improve the quality of the images (i.e., greater signal-to-noise and contrast-to-signal-to-noise ratios). Hence, imaging contrast agents are often used and required for better visualization of the tissue of interest in X-ray CT. Consequently, contrast agents that can increase CT sensitivity and enhance dif-ferentiation among dierent tissues, provide speci c biochemical information, or enable evaluation of tissue/organ function or performance are of the most inter-est and highly sought after. Therefore, CT contrast media employ elements that have much higher Z values than those found in the body, such as iodine, barium, gold and bismuth. A limitation of CT is its low sensitivity to contrast compared with other imaging techniques. The detection limit for CT is about 10=3 M
[42], whereas for MRI it is about 10=5M for gadolinium chelates and for nuclear
techniques about 10=9 M [43]. As a consequence, the search for an optimal CT
contrast agent with maximum imaging capabilities, minimal dose requirements and reduced toxicity is an ongoing task. In the design of a CT contrast agent for clinical applications some general requirements need to be satis ed [44]: (i) the contrast agent should improve visualization of the target tissue by increasing the absolute CT attenuation dierence between the target tissue and the surrounding and uids by a factor of ∼2Ö, (ii) the tissue retention time of the contrast agent should be su ciently long for a complete CT scan, (iii) the contrast agent and its metabolites should be nontoxic and (iv) the contrast agent should (for most
applications) be cleared from the body in a reasonably short amount of time, usually within several hours (<24 h).
The rst contrast agents reported and still in use for CT imaging have been ionide-based, being clinically approved as small molecule agents. Iodine relatively moderate atomic number (Z= 53) and its K-edge value (k = 33 keV) for the 120=130 kVp range in the absorption spectrum make it a suitable contrast agent for many current clinical CT scanners. Small-molecule iodinated contrast agents can be separated into two general categories: the ionic and nonionic molecules. Unfortunately, they exhibit nonspeci c biodistribution, rapid renal clearance, adverse physiological eect (pain and sensation of heat at the site of injection) and high concentrations are required, although they oer safety and imaging e cacy.
Lanthanide-based contrast agents are commonly employed in MRI, but their use as CT contrast agents is also being explored given their high atomic numbers. Free lanthanide ions are extremely toxic, however, many lanthanides form highly stable (and nontoxic) polyaminocarboxylic acid chelate complexes (e.g. gadolin-ium, Z=64). Clinically approved gadolinium-based MRI contrast agents can be used successfully for CT imaging of the cardiovascular system and for pulmonary and aortic angiography [45].
Metal nanoparticles are of signi cant interest as X-ray imaging agents [46]. For example, gold or platinum have both a high density and a high atomic number (ZAu=79 and ZPt=78), thus they possess favorable X-ray attenuating properties.
They oer increased chemical stability, long circulation times, good biocompat-ibility and an obvious advantage in terms of X-ray attenuation compared to molecular contrast agents. The size of metal nanoparticles is easily controlled and can be optimized for vascular extravasation and renal clearance from the or-ganism [47], or for accumulation in the reticuloendothelial system. Nanoparticles also provide for easy surface modi cation, allowing for targeted biodistribution and tailoring of the physical/chemical properties of the imaging agent. Moreover, ease of conjugation of the nanoparticles to imaging probes for many alternative imaging methods makes it easy to prepare multimodal contrast media with sig-ni cantly broadened reporting capabilities. However, one of the major concerns regarding the clinical translation of metal nanoparticles is releated to the ques-tion of their persistence in organism, thus increasing the likelihood of toxicity and the interaction/interference with common medical diagnoses [37].
Chapter 3
Passion fruit-like
nano-architectures
In order to ll the gap between metal nanomaterials and oncology, a modular system that combines the optical behavior of metal nanoparticles with a poten-tially complete body clearance will be introducted. The typical shape of nano-architectures obtained by such preparation resembles that of a passion fruit, thus this kind of structures have been called passion fruit-like nano-architectures. The presence of metals, such as gold and platinum which have high atomic num-ber and consequently high attenuation, within the core of their structure makes them very promising for X-ray imaging.
The reaction processes to synthesize this nanostructures are based on the reduction of salts of the metal of interest in the presence of reducing and sur-factant agents in aqueous solution. Noble metal nanoparticles are coated by negative poly(sodium 4-styrene sulfonate), i.e. PSS, then they are aggregated in spherical arrays by the positive poly(D- or L-lysine)1, i.e. PL, by means of
ionic interactions; the silica shell is grown on this template by a modi ed Stöber
1Polylysine is a synthetic polymer which can be composed of either L-lysine or D-lysine.
method2 in a solution of ammonium hydroxide (NH
4OH) and ethanol (EtOH).
The resulting structures are 100 nm hollow degradable silica nano-architectures containing arrays of gold or platinum nanoparticle (average diameter 2.8 ± 0.4 nm and 2.1 ± 0.6 nm, respectively) surrounded by commercial polymers (see gure 3.1) [48]. Interestingly, it was demonstrated that silica nanoparticles biodegrade in physiological media in few days [49, 50]. Thus the degradation products of these nano-architectures are polymers, small gold/platimun nanoparticles and silic acid, compatible with the possibility to overcome accumulation owing to the renal clearance of the building blocks.
Figure 3.1: Scheme for the general formation of the passion fruit-like nano-architectures.
By changing some key variables such as the reactants, their relative molar concentrations and the reaction temperature or the stirring velocity, it is possi-ble to control the nucleation and growth processes, achieving colloids3 with the
desired properties [6]. The physical-chemical properties of colloids, such as lo-calised surface plasmon resonance (LSPR) modes and catalytic activity, depend on their size and shape, therefore it is important to remember that tight control of the reactions is necessary to achieve the desired size and shape of the particles. Furthermore, a percentage of the amine4 of poly(L-lysine) can be modi ed
with molecules of interest without aecting its rolling properties, thus providing the chance to include dyes or drugs or other functional groups in the cavity of the nanocaspules (obtaining, for example, AuSiCe and AuSiGd nanostructures,
2The Stöber process is a chemical process used to prepare spherical silica (SiO2) particles of
controllable and uniform size for applications in materials science. Silica precursor tetraethyl orthosilicate (TEOS) is hydrolyzed in alcohol (typically ethanol) in the presence of ammonia as a catalyst.
3A colloid is a mixture in which one substance of microscopically dispersed insoluble particles
is suspended throughout another substance.
4Amine are compounds and functional groups that contain a basic nitrogen atom with a
Figure 3.2: Gold, platinum, gold-platinum and metal free nanostructures in milliQ water solution.
see section A.1.4). It is possible to modify the known protocol (see section A.1.1) [37] to increase the metal loading in the nanostructures (obtaining the so-called gold thin-shell nanostructures, i.e. AuSiT, see section A.1.5).
Some of the synthesis obtained during this work are visually shown in gure 3.2.
In this chapter the novel simple and robust protocol to increase the loading of gold nanoparticles in nano-architectures is discussed and compared to the standard synthesis approach (see section 3.1); this protocol would seem very promising in clinical diagnosis applications (see section 3.2). Results obtained by various characterization studies of the other nanostructures are presented in section 3.3.
3.1 Gold passion fruit-like nano-architectures
Usually, by the standard approach (see subsection A.1.1) passion fruit-like nano-architectures are obtained, showing a diameter of 107.6 ± 16.1 nm, 18.9 ± 2.2 nm of wall thickness and containing 5.9 ± 1.3% w/w of metal (that is an average on ten samples analyzed with ICP-MS, see subsection A.2.3) on the total weight of freeze-dried samples (see gures 3.3 and 3.4). Gold nanosystems (AuSi) have demonstrated their biodegradation in physiological uids and in cellular envi-ronment showing the potential kidney-clearability of their building blocks [37]. However, the metal loading of AuSi could be not enough for some kind of imaging techniques, such as X-ray imaging.
Figure 3.3: Typical SEM image of AuSi, scalebar: 100 nm. Bottom-right inset: AuSi detail acquired by TEM con rming the presence of gold nanoparticles inside the cavity.
Figure 3.4: Size distribution histograms of AuSi diameter (left) and of their wall thickness (right). All the histograms were made on measurements of at least 100 nanoparticles collected by TEM.
By changing some key variables such as the reactants and the chemical reac-tion time, dierent nanostructures could be obtained.
The usual mechanism of silica shell formation was studied during the syn-thetic process by TEM observations every 15 minutes [37]. After 15' from the beginning of the reaction, tetraethyl orthosilicate (TEOS) is adsorbed in the gold-polymer arrays and begins to polymerize. Then the silica shell begins to
be formed from the inner cavity, until the capsule is completely composed af-ter 2 hours. In detail, it is inaf-teresting to notice that in the standard Stöber synthesis [51], the introduction of TEOS into the reaction medium is followed by its hydrolysis reactions, resulting in orthosilicic acid. The polymerization of orthosilicic acid occurs when the concentration exceeds the saturation limit in ethanol (about 0.020.03%) [52]. The process yields in sequence: i) low-molecular polymers, ii) high-molecular polymers and iii) particles of 12 nm in size due to the condensation and formation of extra siloxane bonds. Then nuclei increase in size following a LaMer growth5 pattern until their diameter reaches a critical
value of 57 nm, after which they start to aggregate to form silica nanoparticles [52]. This process continues until the concentration of orthosilicic acid in the reaction medium exceeds the saturation limit.
The approaches to increase their metal loading produce passion fruit-like nano-architectures with a tripled content of ultrasmall metal nanoparticles and a thinner silica shell. In order to produce nano-architectures with a thinner silica shell and keeping in mind the mechanism of formation of silica nanoparticles, the quantity of TEOS in the standard protocol was reduced by a quarter. Sur-prisingly, the nanoparticles produced by this approach were no more hollow, but cookies-like nanoparticles (AuSiC, see gure 3.5). This can probably be ascribed to the reaching in the solution medium of the non-saturation of TEOS before the end of the reaction, i.e. the full formation of the silica shell. Thus, besides reducing the TEOS quantity, the addition of the gold nanoparticles (AuNPs) polymeric arrays to the reaction medium was delayed by 30 minutes. During this time, the silica nuclei and 5-7 nm silica nanoparticles are formed in the reaction medium. Thus, the delayed addition results in the aggregation of these silica nanomaterials on the outer surface of the arrays for ionic interaction and not in the absorption of TEOS inside the arrays before its hydrolysis-condensation. Then, the remaining orthosilicic acid continues to form a complete shell until its saturation limit is reached.
By this novel protocol, passion fruit-like nano-architectures with a thinner silica shell (AuSiT) were obtained, showing a diameter of 79.7 ± 5.2 nm and a shell thickness of 11.1 ± 1.1 nm (see gures 3.6 and 3.7). It is worth to notice
5The LaMer model for the kinetics of the formation of a colloid is widely applicable for
production of monodisperse systems. The concentration increases rapidly, rising above the saturation concentration for a brief period, when a short burst of nucleation occurs with the formation of a large number of nuclei in a short time. These particles grow rapidly and lower the concentration below the nucleation level whilst allowing the particles to grow further at a rate determined by the slowest step in the growth process, thus separating nucleation and growth in time.
Figure 3.5: Typical TEM image of AuSiC, scalebar: 50 nm. The image con rmed the absence of a shell.
that the metal loading of AuSiT resulted to be 18 ± 3% w/w on the total weight of freeze-dried samples (that is an average on ten samples analyzed with ICP-MS, see subsection A.2.3).
The optical behavior of AuSi, AuSiT and their components are reported in gure 3.8. The absorption peak of AuNPs is around 514 nm. As expected, after the aggregation with poly(L-lysine), the band shifts to 539 nm due to the close proximity of AuNPs in the polymeric arrays [48]. After the formation of AuSi, the extinction band shifts to 532 nm, likely to an increased size-homogeneity of the processed nanostructures. Interestingly, AuSiT demonstrate an overlapping spectrum to AuSi, supporting the retention of the intriguing optical features of passion fruit-like nano-architectures.
Finally, the degradation kinetics of these nano-architectures in physiological media was evaluated, comparing the results with similar ndings obtained with nano-architectures standard, i.e. AuSi [48]. The experiment was conducted by incubating 100 μg of AuSiT in 500 μL of human plasma and the resulting solution was kept under stirring at 700 rpm at 37 °C. After certain time (0, 1, 6, 12 and 15 h) a droplet of the solution was put on silicon and imaged at SEM after oxygen plasma treatment. The images were acquired has shown in gure 3.9. After six hours the nanosystems lose their spherical shape appearing eroded, after 12 hours
Figure 3.6: Typical SEM image of AuSiT, scalebar: 100 nm. Bottom-right inset: AuSiT detail acquired by TEM con rming the presence of gold nanoparticles inside the cavity.
Figure 3.7: Size distribution histograms of AuSiT diameter (left) and of their wall thickness (right). All the histograms were made on measurements of at least 100 nanoparticles collected by TEM.
Figure 3.8: Spectral behavior of ultrasmall gold nanoparticles (green small dash), AuNPs polymeric arrays (pink dot), AuSi (red dash-dot) and AuSiT (blue dash). Spectra were collected in milliQ water, normalized and the background sub-tracted.
they are almost completely disappeared and after 15 hours no more nanosystems are observed in the samples. Thus the AuSiT degrade faster than AuSi due to their smaller shell.
In summary, earlier studies have been aimed at nding the right balance between accumulation/clearence of metal nanoparticles and retention of their intrinsic physical and physiological behavior. This in order to use them in thera-peutic and diagnostic applications. It would seem that AuSiT, thanks to a tripled loadind of metal of interest, result to be the most promising candidates for X-ray imaging applications. The aims were then focused on these nano-architectures.
3.2 X-ray experiments with gold
nano-architectures
The micro-CT is the preclinical equivalent of CT largely used to study small-animal models of human diseases. It has been proven to be useful in tumor detec-tion, progression and imaging tumor angiogenesis thanks to the use of nanopar-ticles as contrast agents: this is proven by rodent studies [4, 40]. Using various targeting strategies on probes, these nanoparticle-based contrast agents have also
Figure 3.9: SEM images of the nano-architectures during incubation in human plasma at 37 °C. Scale bars: 200 nm.
opened the door for molecular CT imaging. The micro-CT is also used to inves-tigate the structure and density of rodent bones and it is a powerful modality for lungs and pulmonary-vascular system imaging [53]. A micro-CT device was employed to test the potentiality of AuSiT as contrast agents and the results were compared with AuSi.
3.2.1 PET/CT IRIS scanner
The PET/CT IRIS scanner is a multimodal preclinical tomograph for PET/CT imaging of small animals. A micro-CT scanner is based on the same underlying physical principles of a clinical CT scanner, but it is designed for higher-resolution imaging with spatial resolutions in the order of 10-100 µm.
The CT section is equipped with a microfocus X-ray source and a at-panel X-ray detector. The tube has a tungsten anode that can operate at 35-80 kVp and the cathode current ranges from 10 to 1000 mA, it is enclosed in a glass envelope and 2 mm of alluminium have been added in order to remove low energy photons that contribute only to the dose rate. The X-ray detector consists in a
at-panel CMOS6 detector coupled to a 150 µm thick (CsI:Tl) scintillator and it is
subdivided in 1536x1944 square pixels (transaxial x longitudinal direction) with side length ot 75 µm, resulting in a total active area of 115x145 mm2. CT images
are reconstructed using a Feldkamp-type FBP algorithm (see subsection 2.4.1), reconstructed images are calibrated in HU and converted in a DICOM7. The
choice of the scanning protocol depends substantially on the kind of specimen to be examined and consequently on the dose level it can be delivered on, trying at the same time to get the better spatial resolution possible. Several protocols are available for the IRIS scanner according to their function: CT or PET acquisition protocols. For a CT acquisition, the parameters to set are those that substantially determine the dose level to the patient, as the tube voltage, the cathode current, the exposure time per frame and the number of projection views. CT data are typically acquired in Full Scan (FS) or in Short Scan (SS) mode, corresponding to a full (360°) or partial (180° + fan-angle of the projection) rotation of the gantry around the specimen, respectively. The SS acquisition modality enables to greatly reduce the acquisition time, with a consequent reduction of the dose absorbed and an increase in the temporal resolution but a worsening of spatial resolution. The available tube voltage settings in the protocols are 35, 50, 65 and 80 kVp, with a number of projections for the FS mode ranging from 400 to 2,000 depending on the desired reconstruction voxel size: 60, 80, 120, 160 and 240 µm. The rotation range is [-180°; +180°] for a FS protocol and [-110°; +110°] for a SS protocol. The total scan time varies from 16 s to 560 s for the FS protocols and is 7.3 s for the SS ones. Typical reconstruction times range from 20 s for low resolution scan to 2-10 minutes for the medium and high resolution protocols.
3.2.2 Phantom studies
For gold nano-architectures X-ray experiments (both AuSi and AuSiT), a speci c phantom was realized. It consists in a plexiglas cylinder in which ve small cavities were dug all at the same distance from the phantom axis. These small wells are 4 mm in diameter and equally deep, resulting in a volume of 50 ± 1 µL. A speci c plexiglas cover, which is screwed into the cylinder, was realized in order to avoiding contrast agent leakage from the wells. For greater clarity, gure 3.10 illustrates the home-made phantom here described. As it can be
6Complementary metaloxidesemiconductor, abbreviated as CMOS, is a technology for
constructing integrated circuits.
7Digital Imaging and Communications in Medicine (DICOM) is a standard for handling,
storing, printing, and transmitting information in medical imaging. It includes a le format de nition and a network communications protocol.
Figure 3.10: Home-made phantom to characterize the gold nanostructures X-ray attenuation properties.
noticed, the cover presents overturned funnel-shaped holes in correspondence of the wells through which they can be directly lled with the contrast medium with a syringe. This structure was designed to limit the formation of air bubbles, but since NPs solutions were handled with pipette and not with siringe, wells were lled directly without recurring to this device.
Two studies were done, one with AuSi and one with AuSiT, both at four dierent tube voltages. The starting was 100 µL in which gold NPs were diluted in milliQ water with a concentration of 16 mg AuSi/mL and 7 mg AuSiT/mL, respectevely. In both cases, the wells of the home-made phantom were lled with four dierent concentrations, each one halved with respect to the previous one, except for one well lled with milliQ water serving as a benchmark for the measurement of the HU values.
3.2.3 Results
Sections of the reconstructed images of the home-made phantom for AuSi CT scan at two dierent X-ray tube voltages, 35 kV and 80 kV, are shown in gure 3.11. Using the ImageJ program, a circular ROI was drawn inside each well in order to compute the mean and the standard deviation of those regions and
Figure 3.11: Comparison between average images of ten phantom slices acquired at 35 kV and 80 kV for AuSi. At 35 kV the attenuation coe cient of plexiglas is lower than that of milliQ water and the ve wells appear brighter respect to the plexiglas background. At 80 kV the behaviors of the attenuation coe cients of milliQ water and plexiglas are inverted: indeed the ve wells appear darker than that of plexiglas background.
then evaluate the CT numbers for each AuSi mixture. The mean milliQ water value, which represents the zero AuSi concentration, has been subtracted from the estimated average for all AuSi concentrations at every voltage; then the linearity between AuSi concentration and reconstructed CT numbers at the various tube voltages was to be veri ed. Thus a linear t of the obtained ROI values was performed to determine the slope and the coe cient of determination (R2) for
all the tube voltages (R2 ≥0.957 for all tube voltages, except for 80 kV, that
has R2= 0.905). Results are illustrated in gure 3.12 as a function of both AuSi
and gold concentration taking into account that the estimated weight fraction of gold amounted to 9%8. It can be noticed from gure 3.12 that reconstructed CT
numbers show a linear correlation with respect to the AuSi concentration for all voltages.
Contrast-to-noise-ratio9 (CNR) values were calculated with respect to the
plexiglas background. They are graphically reported in gure 3.13. As it can be
8Analysis done only on this sample.
9CNR is a measure used to determine image quality and is de ned as CNR = |IROI−Ibg| σbg , where IROI and Ibgare the CT numbers of ROI and background, respectively, and σvbgis the
Figure 3.12: Plot of CT enhancement in ΔHU measured for all available AuSi concentrations and for all tube voltages. Their ts of data have also been reported and R2≥0.957 for all tube voltages, except for 80 kV, that has R2= 0.905.
noticed, the Rose's threshold10is reached for all AuSi concentrations, except for
milliQ, only at the tube voltage of 35 kV. At 50 kV only the highest concentra-tion is above the threshold, while at 65 kV and 80 kV all the CNR values are considerably below this threshold. CNR decreases with increasing tube voltage. This behavior is evident in gure 3.11; indeed, when the voltage is at 80 kV, the wells and the phantom background are not discernible completely.
The same analysis was performed for AuSiT and is shown in gure 3.14. A circular ROI was drawn inside each well in order to compute the mean and the standard deviation of those regions and then evaluate the CT numbers for each AuSi mixture. The mean milliQ water value, which represents the zero AuSiT concentration, has been subtracted from the estimated average for all AuSiT concentrations at every voltage; then the linearity between AuSiT concentration and reconstructed CT numbers at the various tube voltages was to be veri ed. A linear t of the obtained ROI values was performed to evaluate the slope and the coe cient of determination for all the tube voltages (R2≥0.992 for all tube
voltages, except for 80kV that has R2= 0.861). Results are illustrated in gure
3.15 as a function of both AuSiT and gold concentration taking into account that
10The Rose's criterion states that a signal-to-noise-ratio (SNR) of at least 5 is needed to
be able to distinguish image features with a 100% certainty. A SNR less than 5 means less than 100% certainty in identifying image details. However for CNR ≥ 5 the Rose condition is satis ed [54].
Figure 3.13: Contrast-to-noise-ratio for all gold nano-architectures (AuSi) con-centrations at each tube voltage. The rise of CNR values for higher voltages is due to an invertion of attenuation coe cients of milliQ water and plexiglas. Indeed, the attenuation coe cient of the water decreases with tube voltage more rapidly than that of the plexiglas and they intersect around 50 kV, when the water begins to attenuate less than plexiglas. Thus, from the de nition CNR = |IROI−Ibg|
σbg , it
follows that CNR has a minimum around the point of inversion.
the estimated weight fraction of gold amounted to 28%11. It can be noticed from
gure 3.15 that reconstructed CT numbers show a linear correlation with respect to the AuSiT concentration for all voltages.
Also in this case, CNR values were calculated with respect to the plexiglas background. They are graphically reported in gure 3.16. As it can be noticed, the Rose's threshold is reached only for the two highest AuSiT concentrations at the tube voltage of 35 kV. For the other tube voltages, the CNR value of 5 was not exceeded by any AuSiT concentration. CNR decreases with increasing tube voltage and that emerges also from the two transaxial slices of the phantom acquired at 35 and 80 kV presented in gure 3.14; again, when the voltage is at 80 kV, the wells and the phantom background are not discernible completely. AuSiT have lower CNR values than AuSi due to the lower concentration of synthesis used in the X-ray experiment (less than half of it). But this does not mean that they are not exploitable at all. Indeed, the slopes of the linear ts of gure 3.12 and gure 3.15 were used to estimated the sensitivity of the system to AuSi
and AuSiT concentrations. The sensitivity is reported in gure 3.17 for all tube voltages. This proves that the scanner is more sensible to AuSiT than to AuSi.
Figure 3.14: Comparison between average images of ten phantom slices acquired at 35 kV and 80 kV for AuSiT. At 35 kV the attenuation coe cient of plexiglas is lower than that of milliQ water and the ve wells appear brighter respect to the plexiglas background. At 80 kV the behaviors of the attenuation coe cients of milliQ water and plexiglas are inverted: indeed the ve wells appear darker than that of plexiglas background.
Figure 3.15: Plot of CT enhancement in ΔHU measured for all available AuSiT concentrations and for all tube voltages. Their ts of data have also been reported and R2≥0.992 for all tube voltages, except for 80kV that has R2= 0.861.
Figure 3.16: Contrast-to-noise-ratio for all gold thin-shell nano-architectures (AuSiT) concentrations at each tube voltage. The rise of CNR values for higher voltages is due to an invertion of attenuation coe cients of millQ water and plexiglas. Indeed, the attenuation coe cient of the water decreases with tube voltage more rapidly than that of the plexiglas and they intersect around 50 kV, when the water begins to attenuate less than plexiglas. Thus, from the de nition CN R = |IROI−Ibg|
σbg , it follows that CNR has a minimum around the point of
inversion.
Figure 3.17: Comparison between AuSi (in purple) and AuSiT (in dark red) sensitivity in HU/mg/mL for all tube voltages resulting from experimental data.
3.3 More passion fruit-like nano-architectures
All the other synthesis obtained by the standard protocols allow to achieve nano-architectures showing a diameter of approximately 100 nm and about 20 nm of wall thickness, containing dierent metals in their core (see subsection A.1.2). Furthermore, by modifying the polimer, metallic or metal free nanostructures containing lanthanides can be synthesized (see subsections A.1.4 and A.1.3). The silica shell protects the material in the inner cavity, while metal nanoparticles confer the optical and physical behavior needed for imaging applications [55].
The synthesis obtained by changing only the metal (platinum or gold-platinum) are shown below in gure 3.18.
Figure 3.18: Typical SEM images of PtSi (left) and AuPtSi (right), scalebar: 100 nm. Bottom insets: PtSi and AuPtSi details acquired by TEM.
From a study conducted with the ICP-MS (see subsection A.2.3), 1.6 ± 0.4% w/w of platinum on the total weight of freeze-dried samples for the PtSi and 1.4 ± 0.2% w/w of platinum for the AuPtSi were found; 3.2 ± 0.2% w/w of gold on the total weight of freeze-dried samples for the AuPtSi was obtained. Regarding their optical characteristics, the spectra are shown in gure 3.19. Unfortunately the platinum peak at 215 nm is not observable because it is too weak, while for the AuPtSi the gold peak at 532 nm is visible.
Lanthanide-based contrast agents are commonly used in MRI imaging, but their use as CT contrast agents is also being explored due to their high atomic numbers. Along with high Z, lanthanide atoms possess higher k absorption edge values than iodine12(50 keV for Gd , 40.4 keV for Ce and 33.2 keV for I), allowing
for increased CT instrument sensitivity. Lanthanide atoms thus would oer high
