Conventionally, initial diagnosis of OA-related diseases is performed by the clinician via physical examination and based on symptoms described by the patient. How-ever, joint injuries or degeneration may also be asymptomatic, which delays the diagnosis [75]. The most common symptoms of late stage OA are joint stiffness and pain [5]. The diagnosis is conventionally confirmed via plain radiographs [76–78]
with the radiographs being generally scored according to Kellgren-Lawrence grad-ing system [79]. Due to the poor soft tissue contrast of conventional radiography, the grading is based on joint space narrowing and bone changes. These conditions are, however, associated with late stages of OA. Therefore, plain radiographs are insufficient for diagnosing initial signs of OA [78]. The contrast between the joint space and cartilage can be enhanced in X-ray tomography by injecting contrast agent in the synovial cavity (contrast-enhanced computed tomography) [80], thereby visu-alizing the cartilage surfaces of the joint and enabling detection of cartilage loss. In addition, magnetic resonance (MR) imaging may be conducted as it enables evalua-tion of soft tissues (e.g., cartilage and meniscus). However, MR imaging suffers from relatively poor resolution and has limited availability due to relatively long imag-ing times, thus limitimag-ing the feasibility of the technique in diagnosimag-ing early stage degeneration [81, 82].
Ligament and meniscal tears are conventionally treated in arthroscopic repair surgery. In these surgeries, previously undiagnosed cartilage lesions may be discov-ered, which may require surgical repair to prevent the initiation or progression of PTOA. Current arthroscopic evaluation of cartilage lesions relies on visual inspec-tion with an arthroscopic camera and tissue palpainspec-tion using a metallic hook. While subjective, the evaluation of cartilage softening presents the earliest detectable clini-cal sign of pre-OA changes, also known as chondromalacia or chondrosis [11,71]. To evaluate visible cartilage lesions, several arthroscopic scoring systems have been in-troduced, such as the Internal Cartilage Repair Society (ICRS) and Outerbridge scor-ing systems, which are applied in quantifyscor-ing the severity of cartilage lesions [10,11].
In ICRS scoring system, the severity of cartilage lesion is described with ICRS0 as normal cartilage, ICRS1 as superficial softening, fibrillation, and fissures, ICRS2 as defects extending less than 50% of cartilage thickness, ICRS3 as defects extending deeper than 50% of cartilage thickness but not extending into subchondral bone, and ICRS4 as defects extending into subchondral bone [10]. However, quantify-ing the aforementioned differences with current arthroscopic tools is poorly repro-ducible [13–15], requiring adaptation of novel diagnostic techniques.
Several quantitative techniques, such as ultrasound and optical coherence to-mography (OCT), have been proposed for accurate and objective assessment of car-tilage integrity. Ultrasound has been applied for carcar-tilage imaging non-invasively
via linear transducers [83]. In addition, intravascular ultrasound catheters have been utilized in arthroscopies [17, 84]. Similarly, OCT has been applied arthroscopically by utilizing intravascular catheters [23, 85], which enable imaging in narrow joint cavities. These techniques provide superior resolution compared to conventional imaging modalities (i.e., computed tomography and MR imaging) and evaluation using an arthroscope. In addition, ultrasound and OCT techniques have been in-troduced for quantitative analysis of cartilage properties,e.g., to evaluate cartilage biomechanical properties [17, 86]. To determine cartilage true mechanical properties in arthroscopyin vivo, arthroscopic indentation systems (e.g., Artscan [87, 88] and ACTAEON Probe [89]) have been introduced. However, the measurements with these systems were user-dependent with relatively poor reproducibility. In addition, an ion-streaming potential probe (arthro-BST) has enabled evaluation of cartilage stiffness and healthex vivo [90, 91]. Near infrared spectroscopy (NIRS) was intro-duced in the last decade for quantitative evaluation of cartilage in arthroscopy [47].
The technique enables determination of cartilage composition and could thus sub-stantially enhance the outcome of conventional arthroscopic evaluation of joint in-tegrity. However, the full-potential of the technique for in vivo assessment of joint integrity has not been explored, including adaptation of novel analysis techniques (e.g., partial least squares regression (PLSR), artificial neural networks (ANN), and sophisticated variable selection techniques), as well as evaluation of the health of tissue surrounding cartilage lesions.
3 INFRARED SPECTROSCOPY
Infrared spectroscopy is based on the vibrational and rotational transitions of atoms and molecules in temperatures higher than absolute zero. These vibrations are bond-specific and can, thus, enable identification of molecules [92]. Mid infrared (MIR) spectroscopy has been applied for evaluation of tissues due to the strong fundamental vibrations within this spectral region [93, 94]. However, in the visible (VIS) and near infrared (NIR) spectral regions, only the weaker overtone and com-bination vibrations can be observed [95, 96]. This results into overlap of spectral features, which substantially limits the use of these regions [96]. Only recently has NIR spectroscopy been utilized due to advances in chemometrics,e.g., in agriculture to determine wheat quality [97, 98] and in biomedical engineering to evaluate tissue properties [32, 41, 99].
3.1 THEORETICAL BACKGROUND
Light can be described using the wave-particle duality concept, in which the behav-ior of a photon is not only explained as a particle but also as a wave. The energy of a photon is defined by:
E=hv= hc
λ, (3.1)
whereh is Planck’s constant,vis frequency,cis velocity, andλis wavelength. The energy of a photon is, therefore, dependent on the wavelength. Based on photon energy and the magnitude of its contribution, VIS (λ = 0.4–0.75 µm), NIR (λ = 0.75–2.5 µm), and MIR (λ = 2.5–1000 µm) spectral regions have been introduced.
These regions include bond-specific vibration frequencies, commonly presented as wavenumbers (v, cm−1). In these spectral regions, absorptions arise due to bending and stretching bond vibrations in molecules [92, 96]. These vibration frequencies describe the potential energy difference between two energy levels and each of these levels is described by a quantum numberq= 0, 1, 2, ... (Figure 3.1).
The transitions between the ground level (i.e., level 0) and level 1 are called the fundamental transitions (appear in MIR region), whereas transitions from 0 to q= 2, 3, 4, ... are called overtones and other transitions are called hot transitions (ap-pear in VIS and NIR region). The vibration frequency of overtones can be roughly estimated as the product of the fundamental transition frequency (in wavenumbers) and an integer; however, the overtone frequencies can be determined more accu-rately. Based on the harmonic diatomic oscillator model (Figure 3.1), the allowed energy level (expressed in cm−1) can be derived as
Gq = (q+1
whereqis the vibrational quantum number (0, 1, 2, ...),kis the force constant of the bond, andµm is the reduced mass of the diatomic molecule [96]. Furthermore, the
harmonic oscillator model is only valid for neighboring energy levels (∆q=±1) and does not account for repulsive forces between the atoms or dissociation of strongly extended vibration bonds [92, 96]. Morse potential (Figure 3.1), however, accounts for the anharmonicity and bond breaking [92, 96]. The allowed energy levels are described by:
Gq = (q+1
2)v0−(q+1
2)2χv0, (3.3)
where χis the anharmonicity constant [96, 100]. The frequencies of overtones (vq) can be determined by [100]
vq =Gq−G0
=qv0(1−χ(q+1))
= qv1(1−χ(q+1)) 1−2χ ,
(3.4)
where q = 2, 3, 4, ... andv1 is the fundamental vibration frequency (transition: 0
→ 1). Additionally, the effects of rigid rotor [92], centrifugal distortion [92], and rotational-vibrational coupling [101] can be determined with the molecular-specific constants of rotation, centrifugal distortion, and vibration-rotation interaction, re-spectively, along with the rotational quantum number.
Figure 3.1:Schematic of harmonic oscillator potential and morse potential.
In addition to overtones (λ= 0.7–1.8 µm), combination bands (λ= 1.3–2.7 µm) can be observed in the NIR spectral region (Table 3.1) [96]. The association of over-tones and these combinations bands results in overlapping spectral features and, thus, substantially decreases the specificity of absorption in this spectral region. The relative contribution of absorption bands is strongest in the MIR region (fundamen-tal vibrations). Furthermore, with increasing overtones (decreasing wavelength), the relative absorbance is weaker(∼10–100 times weaker) [95]. Recent advances in computer hardware, regression techniques, and availability of computational power have accelerated the adaptation of these spectral regions in multiple applications.
The optical response of a sample can be expressed as absorbance, transmittance, or reflectance spectroscopy, of which reflectance spectroscopy was utilized in this
thesis. In reflectance spectroscopy, light is transmitted into a sample, and the re-flected and back-scattered light is collected (Figure 3.2). Rest of the light is either absorbed by the sample or transmitted through the sample. To determine spectral properties of a sample, Beer-Lambert’s law, also know as Beer’s law [102], can be applied. The law describes the decrease in light intensity as it traverses through the tissue and is described by:
I= I010−µl, (3.5)
where I is light intensity after material, I0 is the initial light intensity, µ is the material-specific absorption coefficient, and l is the distance traveled through the material. Transmittance (T) or absorbance (A) of the material is determined with
A=−log10T=−log10( I
I0). (3.6)
Table 3.1:Common NIR bands [95, 96]
Wavelength (µm) Other
0.78–0.85 Third overtone N–H stretching 0.85–0.95 Third overtone C–H stretching
0.95–1.10 Second overtones of N–H and O–H stretching 1.10–1.23 Second overtone C–H stretching
1.30–1.42 Combination C–H stretching
1.40–1.55 First overtones of N–H and O–H stretching 1.65–1.80 First overtone C–H stretching
1.90–2.00 Second overtones of O–H bending and C=O stretching 2.00–2.20 Combination N–H stretching, combination O–H stretching,
Second overtone N–H bending
In reflectance spectroscopy, the absorbance is based on the reflection and scatter-ing in the sample, and can be determined with
A=−log10(S−D
R−D), (3.7)
whereS is the scattered and reflected light collected from a sample,Dis the spec-trum from a dark reference standard, and R is the spectrum from a reflectance standard. The dark reference is determined to account for hardware-related noise, whereas the reflectance standard is an optimal specular reflector that conventionally reflects over 99% of light across the specified wavelength region.