1. Introduction

Osteoarthritis (OA), or degenerative joint disease, is the most common form of joint disease. It is a slowly progressive disease, and a major cause of morbidity in the population over 50, affecting more than 21 million Americans. It also imposes considerable expense on the health care system. Although a number of factors contribute to its development, including obesity, trauma, and genetic predisposition, the hallmark of osteoarthritis is progressive damage to articular cartilage. As damage progresses, fissuring of the articular cartilage can result in fragmentation and deposition of small loose bodies within the joint space, causing a low-grade inflammatory reaction and increased turbidity in the synovial fluid, with a decrease in viscoelastic and lubricating properties. Subchondral bone also becomes exposed and results in sclerosis. While there is currently no cure for this disease, numerous studies have shown that the progression of articular damage may be modified by medical or surgical intervention if the disease is detected early [1–3]. These studies, coupled with recent developments in gene therapy and anticipated novel therapeutic approaches, have generated substantial demand for noninvasive techniques for detecting early changes in the joints, when intervention is likely to have its greatest effect.

Several noninvasive imaging modalities have been applied to detect OA, including x-ray radiography, computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI) [4–7]. Radiography and CT are excellent techniques to image hard tissues, and are primarily used for advanced OA stages when bone damage has occurred [4, 5]. It is difficult for these techniques to detect changes in cartilage or the early inflammatory process in synovial membrane/fluid. Ultrasound has limited utility in evaluation of the early stages of OA, and is very operator-dependent, making it difficult to be used as a quantitative tool. MRI represents a powerful modality for evaluating joint abnormalities, particularly when a surface coil or contrast agent is used [7, 8]. However, MRI is expensive, and may not be suitable for long term, routine monitoring of OA.

It has been recently recognized that optical contrast is highly sensitive to structural and metabolic changes associated with an OA joint [9, 10] due to a strong correlation between optical changes and the diseased joint. Collagen fibril network deterioration has been observed when OA was present in the cartilage area [11]. This deterioration decreased the collagen-induced optical path difference by 19-71%. The variation in fibrils together with the loss of proteoglycans in OA should result in significant changes in both the scattering and absorption coefficients of the cartilage. In addition, it is known that with the onset of OA, the synovial membrane/fluid in articular cavity becomes increasingly turbid [12,13]. The increased turbidity would accompany increased scattering and absorption coefficients in the diseased synovial membrane/fluid. In fact, this optical increase can be as large as 100% at certain wavelengths in the near-infrared (NIR) region [12, 13], which would provide good optical contrast for imaging purposes. Finally, there is increasing evidence that OA is a disease involving a metabolic dysfunction of bone [14, 15]. It is likely that this metabolic dysfunction of bone, often associated with high metabolism of subchondral bone, will cause changes in its tissue oxygenation, Hb, HbO₂ and water content. These metabolic and functional parameters should also be measurable by multispectral optical/photoacoustic imaging.

The demonstrated optical detection of OA [9], however, was based on diffuse optical tomography (DOT) [16–19], a pure optical imaging method that has relatively low spatial resolution (2~4mm) due to strong tissue scattering. While it is known that an adult articular cartilage has a typical thickness of from 2 to 4 mm, diseased cartilage typically has a thickness of 1mm or smaller [10]. Thus, to correctly image an OA joint, DOT needs to be combined with a high resolution modality such as x-ray so that the high resolution x-ray images can be used as a priori structural guidance for DOT reconstruction [10]. Laser-induced photoacoustic tomography (PAT) alone can achieve the same as the combined x-ray/DOT does, since it retains the desired high optical contrast while providing much better spatial resolution (0.5mm or better, adjustable with ultrasound frequency) than pure optical tomographic methods by detecting much less-scattering ultrasonic waves. In addition, PAT is free of the speckle artifacts present in pulse-echo ultrasonography.

PAT is concerned with an inverse problem where a single short-pulsed light beam illuminates an object and the photoacoustic waves excited by thermoelastic expansion are measured using wideband ultrasound transducers in multiple locations around the object. The geometry of the object and spatial distribution of the optical absorption are obtained from the measured scattered fields using a reconstruction algorithm. To date various PAT methods have been developed and applied to the detection of breast cancer, skin cancer and blood concentrations, port wine stain depth determination, and functional brain imaging in small animals [20–25].

The existing/conventional PAT methods, however, are based solely on the photoacoustic wave propagation model, and provide only the distribution of absorbed optical energy (the product of both absorption coefficient and photon density distribution), which is an indirect representation of the intrinsic optical properties. It is well known that it is the optical properties that reflect the tissue physiological parameters such as blood oxygenation. Several recent studies have suggested that it is possible to recover the quantitative optical absorption coefficient map when the conventional PAT is combined with a light transport model [26–31]. These studies have opened a new avenue to realize truly quantitative PAT (qPAT) by exploiting the spectral characteristics of specific chromophores in tissue, thus providing spatially resolved quantitative physiological and molecular information for valuable diagnostic-decision making.

The goal of the current study is to assess for the first time the possibility of qPAT for detection of OA in the finger joints based on our recent successful study of in vivo imaging of a normal finger joint using qPAT [32].

2. Materials and methods

2.1 Photoacoustic imager

Our PAT system consists of a pulsed NIR light source, a spherical scanning subsystem, and an ultrasound detection array and associated acoustic signal/data acquisition. In this system, pulsed light beam at 805nm, generated by a Ti: Sapphire laser (LOTIS, Minsk, Belarus) with a repetition rate of 10 Hz and a pulse width of <10ns, illuminates the DIP joint of interest. The light beam is guided via an optical fiber and the laser energy at the DIP joint is controlled around 10mJ/cm², which is far below the safety standard of 22mJ/cm². The ultrasound detection array (Fig. 1a ) is composed of eight 1 MHz transducers (Valpey Fisher, Hopkinton, MA) arranged equally spaced along a 210° arc arm. The position and performance of each transducer in the ultrasound array was calibrated carefully with controlled tissue phantom experiments. During an exam, the palmar side of the DIP joint faced up, allowing the DIP joint to be illuminated from the dorsal side of the finger. Our experience indicates that this way of light illumination can give us maximized tissue penetration. To secure the position of the finger, rubber bands were applied to the distal tip and the proximal end of the finger. Both the ultrasound array and the examined finger were immersed in a water tank for minimized ultrasound attenuation. The detected acoustic signals were fed into preamplifiers and converted to digital signals by a multi-channel A/D board (PREAMP2-D and PCIAD1650, US Ultratek, Concord, CA). Labview programming was used to control the entire examination procedure. With the ultrasound array, the photoacoustic exam took just about 30 s, allowing the collection of acoustic signals at 40 detection positions (5 array positions) along a coronal plane of the DIP joint (Fig. 1b).

 

Fig. 1 (a) Photograph of the transducer array/finger interface of the photoacoustic tomography system used for the current study. S: Laser source; T1-T8: Transducers. (b) Schematic of the coronal section/plane for the transducer scanning.

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A qPAT reconstruction method detailed elsewhere [26, 30] was used to quantitatively recover the optical absorption coefficient images of the DIP joints. Briefly, this method includes two steps. The first is to obtain the map of absorbed light energy density through a model-based reconstruction algorithm that is based on iterative finite element solution to the photoacoustic wave equation starting from presumably uniform initial guess of optical and acoustic properties, while minimizing the weighted square errors between the measured and computed data using Newton-type method coupled with a hybrid scheme of Marquardt and Tikhonov regularizations. The second step is to obtain the distribution of optical fluence using a photon diffusion equation based optimization procedure and to recover the distribution of optical absorption coefficient from the optical fluence and the absorbed energy density obtained from the first step.

2.2 Patient examination

6 subjects (2 OA patients and 4 healthy controls) were enrolled in the study. All participants (white females; mean age 61 years, range 45–71 years) provided informed consent as part of the protocol approved by the Institutional Review Board of University of Florida (UF). Participants were recruited from the Rheumatology Clinic at the Shands Health Center of UF. One DIP finger joint, joint II of the left hand, from each subject was examined clinically and photoacoustically. The DIP joint is mostly vulnerable to OA disease and easily accessible with our current photoacoustic scanning system.

Clinical examination of each patient was performed independently by a single rheumatologist (E.S.S). Patients with OA were identified by clinical history and main clinical features, including symptoms (predominantly pain and stiffness), functional impairment and signs (joint enlargement and redness). The healthy controls had no known OA or other joint diseases.

3. Results and discussion

The in vivo 2D photoacoustic images were reconstructed using a dual mesh scheme [33] ― the fine mesh used for the forward calculation consisted of 5977 nodes and 11712 elements, while the coarse mesh used for the inverse calculation had 1525 nodes and 2928 elements. Based on the recovered 2D images, the optical absorption coefficient of different joint tissues (bone, cartilage and synovial fluid) and the structural size of joint cavity (cartilage and synovial fluid) were extracted. These parameters are used as indicators/markers for differentiation of OA from normal joints in this study.

Figures 2a -2f present the recovered absorption coefficient images (coronal sections) for the 6 subjects examined. We can see that the bones (the red regions with highest absorption) are clearly delineated from the adjacent tissues (cartilage and fluid) in the joint cavity for all the cases. While each joint has different shape and size due to their positions relative to the scanning plan, the joint tissue/space (indicated by the arrow) is readily identifiable. Compared with the healthy joints (Figs. 2a-2d), we note that the value of absorption coefficient in the joint cavity is elevated for the OA joints (Figs. 2e and 2f). It is also notable that the joint space narrowing appears to be evident for the OA joints.

 

Fig. 2 Recovered absorption coefficient images for the six subjects examined. (a)-(d): normal joints. (e)-(f): OA joints. The color scale indicates the absorption coefficient (mm⁻¹).

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The average absorption coefficient of joint tissues (cartilage, fluid, and bone) for both the normal and OA joints were calculated and presented in Figs. 3a and 3b. We can immediately tell from Fig. 3a that both the diseased cartilage and synovial fluid have significantly increased absorption coefficient compared to the normal cartilage and fluid. While we note that the value of absorption coefficient of bone for the OA joints is just slightly larger than that for the normal joints, the ratio of the absorption coefficient of the cartilage or synovial fluid to that of the bone is striking, as can be seen from Fig. 3b. These quantitative optical changes associated with the diseased joints are certainly attributed to the increased turbidity of synovial fluid, collagen fibril network deterioration, and metabolic dysfunction of bone, as elaborated in the introduction section.

 

Fig. 3 (a) Average absorption coefficient of cartilage (red) and fluid (blue) for the healthy (H1-H4) and osteoarthritic (OA1-OA2) joints. (b) Ratio of absorption coefficient of cartilage to that of bone (red), or ratio of absorption coefficient of fluid to that of bone (blue) for the healthy and OA joints.

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In summary, this study represents the first attempt to in vivo detect osteoarthritis in the finger joints using single-spectral quantitative photoacoustic tomography. We show that apparent differences in the absorption coefficient of the joint cavity exist between OA and normal joints. The results reported here suggest the possibility of qPAT as a potential clinical tool for early detection of OA in the finger joints.