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Research Papers

Enhanced Thermographic Detection of Skin Cancer Through Combining Laser Scanning and Biodegradable Nanoparticles OPEN ACCESS

[+] Author and Article Information
Chao Jin, Xue-Yao Yang

Department of Biomedical Engineering,
School of Medicine,
Tsinghua University,
Beijing 100084, China

Zhi-Zhu He

Beijing Key Lab of Cryo-Biomedical Engineering
and Key Lab of Cryogenics,
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences,
Beijing 100190, China

Jing Liu

Department of Biomedical Engineering,
School of Medicine,
Tsinghua University,
Beijing 100084, China;
Beijing Key Lab of Cryo-Biomedical
Engineering and Key Lab of Cryogenics,
Technical Institute of Physics and Chemistry,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: jliubme@tsinghua.edu.cn

1Corresponding author.

Manuscript received October 13, 2012; final manuscript received February 20, 2013; published online July 11, 2013. Assoc. Editor: Liang Zhu.

J. Nanotechnol. Eng. Med 4(1), 011004 (Jul 11, 2013) (8 pages) Paper No: NANO-12-1126; doi: 10.1115/1.4024129 History: Received October 13, 2012; Revised February 20, 2013

Through introducing biodegradable magnesium nanoparticles (Mg-NPs) with excellent property in absorbing laser photon, this paper is dedicated to present a laser scanning based thermogaphic strategy for detecting the skin cancer. It aims at selectively enhancing the thermal responses of the target regions so as to distinguish the tumor from the normal tissues on the infrared images. The carried out three-dimensional simulations and conceptual experiments quantitatively demonstrated the feasibility of the present method in improving the sensitivity and targeting-ability (i.e., specificity) of the thermography. Further parametric studies on the thermal enhanced effects such as by varying the parameters of laser beam (i.e., laser power, action time, and moving frequency) and Mg-NPs (i.e., nanoparticle concentration) disclose more quantitative mechanisms for achieving a better output of the diagnosis. The results indicate the following facts: (1) The parameters could be selected to significantly improve the sensitivity of the thermal detection, such that the maximum temperature difference could even reach 2.31 °C; (2) for safety concern to human body, the default parameter setting (P = 1 W, Δt = 40 ms, f = 1 Hz, n = 0.02 mg/ml) can be a good choice and enhanced results can thus be easily detected; and (3) with the unique biodegradable merits, the Mg-NPs can be considered as an extremely useful agent for enhancing thermogaphy in identifying the early stage tumor.

FIGURES IN THIS ARTICLE
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Malignant melanoma is regarded as a leading cause of death from the skin diseases. It has been estimated that each year more than 53,600 people were diagnosed with melanoma, and approximately 8700 deaths would occur due to melanoma in the United States [1,2]. Up to now, surgical excision still remains the primary and relatively effective means to treat the melanoma. Therefore, an early detection and treatment is the key to improving survival rate in patients with skin cancer [3,4]. Currently, various technical tools have been developed to detect skin cancer, such as digital photography, dermoscopy, multispectral imaging systems, confocal scanning laser microscopy and so on [5-13]. There is no doubt that the technical maturity of such devices would provide excellent detection platforms for skin cancer and significantly improve the sensitivity and efficiency of early diagnosis. However, the existing disadvantages such as high-cost, complex operation, etc., have limited their extended practices, especially for clinical detection or scanning the early tumor in a large population scale.

As a noninvasive, noncontact, and cost-effective functional imaging modality, infrared thermography is being increasingly employed in the diagnosis and evaluation of various diseases, such as breast cancer, skin cancer, inflammation, diabetic situation, and rehabilitation assessment, etc., [14-16]. However, thermography owes its insurmountable shortcomings. For instance, its clinical interpretation would often be tedious, challenging and even error-prone if the thermal signature induced by the abnormal pathological changes is subtle. In order to improve the accuracy of such detection, many efforts have been made to enhance the thermal manifestations of the skin over the diseases, such as active dynamic thermography, cold stress, forced conduction and induced evaporation, stimulated heating, and so on [17-22]. To some extent, all of the former attempts indeed effectively enhanced the thermal signs and thereby improved the sensitivity of thermography. With respect to the thermal detection of skin cancer, it is worthy to mention that through a series of endeavors including numerical simulations, analysis and interpretations of clinical data, Çetingül and Herman have performed a relatively efficient dynamic thermographic system to detect the early abnormalities of melanoma lesions [23,24]. Although this study has offered a feasible strategy for quantitative identification of pigmented lesions with varying melanoma potential, some limitations, for instance the specificity (e.g., ability to target the specific cancer cell) still remain a challenge and bottleneck for its clinical practice.

Recently, the emerging of nanotechnology has brought a technological revolution into the traditional medicine. A variety of functional nanomaterials (e.g., conjugated with antibody) have been synthesized as novel enhanced agents or targeted markers in the following fields: medical imaging, targeted chemotherapy, cryosurgery and hyperthermia treatment of tumor [25-28]. Meanwhile, some studies also made some attempts to improve the sensitivity of thermography relying on the enhanced effects of nanomaterials. Levy et al. proposed a tumor-specific thermographic approach for detecting and locating the tumor by utilizing tumor-specific magnetic nanoparticles exposure to an alternating magnetic field [29]. Jakobsohn and colleagues introduced the GNPs (gold nanoparticles) as a contrast agent for thermally detecting and locating the margin of recurrent tumor [30].

However, there still exist some unpredictable potential risks and side effects when injecting such nonbiodegradable materials like GNPs and carbon nanomaterials into the human body. For example, some studies found that it is the potential toxic mechanism (including oxidative stress and mitochondrial damage) of GNPs with diameter of 1.4 nm that led to the cell necrosis [31]. Besides, the injury on the hepatic and renal tissue due to intraperitoneal injection of magnetic Fe3O4-nanoparticle has also been reported [32]. It can be concluded that there is an urgent need to reveal the underlying biologic mechanism of nanomaterials interacting with human body. And it also provokes us to find and explore alternative nanomaterials with more favorable properties (e.g., causing no side effects on human body). For such particular reason, our recent work has made efforts toward this target by utilizing the biodegradable nanomaterials including Mg-NPs and MgO-nanoparticle for the localized ablation of tumor [33,34]. It is the enhanced absorption property in the near infrared region that markedly improves the therapeutic efficacy of laser thermal ablation. More importantly, the Mg-nanoparticle with biodegradable and low-cost advantages would significantly promote its medical implementation as a real “Green” laser therapy in future clinics. In this paper, through introducing the Mg-NPs, we proposed and evaluated a laser scanning enhanced thermographic strategy for early detection of skin cancer.

Principle.

According to the proposed method, firstly the Mg-NPs conjugated with biomolecules (e.g., antibody) for targeting skin tumor would be injected into human body. After that, with the help of Mg-NPs gathering at the tumor site, one can use a laser beam with safe power to scan the suspicious skin regions at a fixed rate. By selectively increasing the thermal response of the targeted region, one can distinguish the tumor from the surrounding normal skin tissues at the early stage by using the current infrared thermography system. Further, based on the coupled classic photon transfer equation (i.e., Monte Carlo simulation) and bioheat transfer model, this work has also established a feasible strategy for enhancing the thermal signs of targeted tumor region with Mg-NPs. Parametric studies quantitatively suggested a succession of comprehensive characterizations on the sensitivity of the proposed method, such as the effects of varying laser power, frequency, and Mg-NPs solutions with varying concentrations, etc. Meanwhile, some in vitro conceptual experiments by infrared thermography were also carried out to estimate the reliability of the method. All these attempts would help elevate the sensitivity and specificity of thermography in detecting the early tumor to some extent.

Theoretical Model and Calculation Domain.

Figure 1 shows the schematic of the proposed laser scanning modality, where a laser beam is mechanically controlled to move over the skin surface of the suspected region. For the enhanced absorption of laser light in the tumor with Mg-NPs, the target tumor would be detected when discovering the evident temperature elevation of localized region. According to the skin anatomy [35], the skin tissue is simplified as a 5 mm × 5 mm × 2 mm cube which consisted of a 60 μm thick epidermis with a 15 μm thick basal melanin layer, and a 1940 μm thick dermis, as depicted in Fig. 1; where z denoted the tissue depth from the skin surface while x and y are along the surface. The tumor underneath the skin surface is embedded at the site of x = 0.25 mm, y = 0.25 mm, and z = 0.1 mm which is described as a regular ellipsoid with the size of a = 1 mm, b = 0.8 mm, and c = 0.5 mm; here, a, b, and c represent the axial length of x, y, and z directions, respectively. In addition, the laser spot is characterized as a circle with a radius of 1 mm while the displacement of laser beam is set as 1 mm in each movement.

Photon Field Equation.

In this work, we adopted the radiative transfer equation to characterize the light propagation in biological tissue [36]Display Formula

(1)s·I(r,s)+(μa+μs)I(r,s)=μs4π4πp(s,s')I(r,s')dω'

where, r is the position vector; s and s' are direction vectors; I(r,s) is the intensity of laser light at position r in the direction s; ω' is the solid angle; μa and μs are absorbing coefficient and scattering coefficient, respectively; p(s,s') is the phase function, which is described as the probability of photon scattering from an initial propagation direction to a final direction.

The above equation (i.e., Eq. (1)) can be solved using the Monte Carlo method to simulate the photon propagation process during the laser scanning procedure. The absorption of photon by the tissues leads to a final photon field distribution q(X) which can be calculated by the deposited photon weight. Specifically, once the photon takes a step from position (x, y, z) to a new position (xn, yn, zn), the accumulated deposited photon weight is updated by q(x, y, z) ← q(x, y, z) + a/μt, where, the new photon weight, W, is calculated by W ← s/μt. A detailed description of the Monte Carlo model is presented in Refs. [37-40].

Besides, since tumor is embedded in the skin tissue and generally owns a distinct interface with the normal tissue, some algorithms have also been employed to describe the reflection and refraction behavior of photon at the tissue-tumor interface. According to Ref. [37], 1,000,000 photons have been simulated in each movement of laser beam to obtain a photon field with more accurate spatial resolution.

Thermal Field Equation.

It is the absorption of laser photons that leads to the temperature elevation of skin tissue as a spatial heat source. This volumetric heating term can be calculated by the relation Qr(X) = (P/N) × q(X)/V; here, q(X) is the photon field distribution calculated from Eq. (1) which is described in Sec. 2.2.1; P and N represent the laser power and the total photon number, respectively; the element volume, V, is calculated by Δx × Δy × Δz; Δx, Δy and Δz are, respectively, the element sizes in the coordinate x, y and z of the whole calculated domain.

The classic Pennes bioheat equation has been utilized to determine the temperature distribution in the skin tissue, as expressed in Eq. (2) [41]Display Formula

(2)ρcT(X,t)t=kT(X,t)+ρbcbωb(Ta-T(X,t))+Q(X,t)

where, T(X, t) is the tissue temperature, k is the thermal conductivity of tissue; ρ, c denote the density and specific heat of tissue, respectively; ρb, cb, ωb are the density, specific heat, and blood perfusion rate of the blood, respectively. Ta is arterial temperature; Q(X, t) = Qm(X, t) + Qr(X, t), Qm(X, t) represents the metabolic heat generation and Qr(X, t) is the spatial heat source term resulted from the photon absorption of the skin tissue. It is worthy to note that Qr(X, t) varies with the movement of laser beam, thus the transient three-dimensional temperature distribution would be simulated by solving Eq. (2) with varying heat source term.

The boundary conditions for the calculation domain are described as follows:

At the interface between the skin tissue and tumor, the heat transfer conditions were defined as continuum of both temperature and heat flux, i.e.,Display Formula

(3)Tt=T1,   ktTtn=k1T1n

The thermal boundary condition of the skin surface was expressed asDisplay Formula

(4)-kT(X,t)n=hf(Ts-Tf)

where, hf is the heat convection coefficient between the skin surface and the surrounding air; Ts and Tf are, respectively, the temperature of the skin surface and the surrounding air.

The adiabatic boundary condition is defined at the rest boundaries.

Numerical Methods and Parameters.

The symmetrical semi-implicit method has been utilized to solve the three-dimensional heat transfer problem in the present study, and readers can refer to Ref. [42] for more details about the numerical algorithm. In the calculation, a relatively small mesh sizes of Δx = 0.05 mm, Δy = 0.05 mm, Δz = 0.02 mm have been used and the time step is set as 5 × 10−4 s. In the dynamic simulation of laser heating, the steady-state temperature distributions have been firstly computed and imported as the initial conditions for simulating the dynamic thermal response of the skin surface exposed to the laser scanning.

Aiming at providing a more comparative result between the simulation and in vitro study, this work has determined to use a laser light with a wavelength of 810 nm. All the optical coefficients at the wavelength of 810 nm in the skin tissue region (including epidermis, dermis and basal melanin layer) and tumor region have been calculated by the estimated methods provided in Ref. [35]. Taking the tumor region for example, the absorption and scattering coefficients of tumor, μa,tumor and μs,tumor, can be estimated by the following equation [35,43]:Display Formula

(5)μtumor=(1-f)μbase+f·μblood

Here, μtumor denotes the absorption or scattering coefficients of tumor; μbase and μblood represent the corresponding absorption or scattering coefficients of melaninless epidermis and blood, respectively; f is the volume fraction of blood in the tumor.

According to the measured data and estimated model, the absorption coefficient of blood μa,blood is about 466.56 m−1 [44]. And the absorption coefficient of melaninless epidermis μa,base can be calculated by μa,base = 0.244 + 85.3 exp(−(λ − 153)/66.2), λ is the wavelength of laser light (nm) [35,43]. In addition, it has been reported that the absorption coefficient μa,tumor of gastric adenocarcinoma tissue is about 0.75 cm at the wavelength of 1064 nm, thus the volume fraction f = 13.4% can be calculated by the above equations [45]. Here, we assumed that the volume fraction of skin cancer tissue is 13.4%, thus the final estimated μa,tumor of 0.8402 cm−1 can be obtained.

It has been reported that the scattering coefficient μs,blood of blood at the wavelength of 810 nm is 5.9 cm−1 [35]. And the scattering coefficient μs,base of melaninless epidermis can be estimated by μs,base = (2 × 105λ−1.5 + 2 × 1012λ−4)/(1 − g); here, g = 0.88 [35,43]. Therefore, the scattering coefficient μs,tumor of 175 cm can be estimated by Eq. (5).

Table 1 gives the typical physical parameters for skin tissue, tumor and Mg-NPs. A common heat convection coefficient of 10 W/m2 °C and an environmental temperature of 25.0 °C were adopted in setting the convective boundary condition on the skin surface. The relative parameters concerning the absorption and scattering coefficients have been estimated and listed in Table 1 [34,35,43-35,43-46].

Besides, the corresponding data for the composite consisting of cancerous tissue and injected Mg-NPs can be approximated by the following expressions:Display Formula

(6a)ρ3=(1-η)ρ1+ηρ2
Display Formula
(6b)c3=(1-η)c1+ηc2
Display Formula
(6c)1k3=1-ηk1+ηk2
Display Formula
(6d)μ3=(1-η)μ1+ημ2

where, η = (4nπr3)/3 represents the volume concentration of particles, n and r are the concentration and radius of Mg-NPs, respectively; the subscripts of 1, 2, 3 denote the tumor tissue, Mg-NPs and the composite, respectively; μ is either the absorption coefficient or scattering coefficient.

Dynamic Thermal Response Induced by the Mg-NPs-Enhanced Laser Scanning.

As for this proposed enhanced modality, one of the most critical elements is the design of mechanical control system of laser beam, such as the laser-type (e.g., pulse laser or continuous-wave laser), action time, moving frequency, etc. Considering the simplicity and convenience of laser control, a pulse laser beam with action time of Δt = 40 ms and power of P = 1 W has been employed in the preliminary simulation. Meanwhile, the laser beam is assumed to move at a frequency rate of f = 1 Hz, along the x axis directions of the coordinate system, as shown in Fig. 1. In this section, the simulation results concerning the laser scanning with a total duration of 3 s have been performed.

It is known that during the laser scanning procedure, a series of complicated interactions between the loading laser photon and the tissue would induce the conversion of photon energy into heat. Figure 2 gives the nondimensional heat source (divide the calculated results of heat source distributions Qr by 107 W/m3) in the two-dimensional skin cross section of y = 2.5 mm; the simulation results of the conditions relating to the tumor zones without and with Mg-NPs (the concentration of 0.02 mg/ml) are depicted in the top (i.e., (a)–(c)) and bottom (i.e., (d)–(f)) subsets of Fig. 2, respectively. Here, Figs. 2(a)–2(c) denote the results when the center of laser beam is located at three sites of (x = 1.5 mm, y = 2.5 mm, z = 0), (x = 2.5 mm, y = 2.5 mm, z = 0) and (x = 3.5 mm, y = 2.5 mm, z = 0), respectively; (d)–(f) are the corresponding calculation results with Mg-NPs. It can be clearly observed that the injected Mg-NPs greatly enhanced the absorption of laser photon thereby leading to rather higher heat source distributions within the targeted tumor zone. From Figs. 2(a)–2(c), due to its larger blood perfusion property, tumor owns relatively stronger capability of absorbing laser photons. However, the basal melanin layer of epidermis absorbs a considerable amount of laser photons when laser beam scans over the skin surface. As a result, the difference of heat source distributions between tumor and the normal tissues is so small that it is hard to be detected by the current thermal imaging system.

As depicted in Fig. 3, more quantitative information concerning the transient thermal response has been provided through monitoring the changes in temperature of three points (x = 2.5 mm, y = 2.5 mm, z = 0), (x = 2.5 mm, y = 2.5 mm, z = 0.1 mm) and (x = 2.5 mm, y = 2.5 mm, z = 0.35 mm). Meanwhile, the temperature difference at the point of interest (i.e., ΔT(t) = T(t) − T(0), T(t), and T(0) represent the transient and initial temperature of the selected point) has been calculated to characterize the dynamic thermal responses of skin tissues without and with Mg-NPs, as shown in Figs. 3(a) and 3(b), respectively. The phenomenon of temperature elevation in the tumor tissue center composited with Mg-NPs is obvious and the largest value for ΔT even reaches 2.98 °C. It also results in distinct temperature increasing (the maximum of ΔT is about 2.31 °C) at the skin surface which can be easily identified by the current thermography system, thereby achieving the goal of detecting the targeted tumor. As for the case without Mg-NPs, the temperature responses heavily depend on the propagation of laser light from the skin surface to the tissue. Therefore, the skin surface shows higher temperature manifestations than the tumor center.

Figure 4 displays the results for the temperature distributions at the skin surface to provide more visualized information in evaluating the enhanced efficiency. Figure 4(a) shows a relatively uniform temperature distribution in the area of laser spot which is mainly due to the considerable absorption of laser photons in the basal melanin layer. As shown in Figs. 4(b) and 4(c), a slight thermal response can be induced along the pathways of laser beam. However, the contrast of 0.56 °C may be a little weak to be explicitly identified by infrared thermography, especially for the clinical practice. Here, the contrast is defined by the temperature difference between the maximum and minimum of temperature distribution in the area of laser spot. By contrast, the enhanced thermal manifestations with contrast of 1.09 °C can indeed promote the detection sensitivity of thermography, as depicted in Figs. 4(d)–4(f).

Parametric Effects of Laser Beam on the Enhanced Behavior.

It is intelligible that the determination of related parameters of laser device is significant for the prototype design of the present method. Therefore, to provide more quantitative references, this section further performs some simulations to evaluate the effects of varying parameters of laser beam on the enhanced behavior. In each simulation, a parameter of laser beam is varied within a fixed range while remaining parameters are kept constant which can be determined in the database of P = 1 W, Δt = 40 ms, and f = 1 Hz. The following temperature profiles are all extracted in the line of (y = 2.5 mm, z = 0) at the transient time when the laser beam is moving to another site along the x axis.

Figure 5 presents the thermal response changes caused by the varying power P (W). When P increases from 1 W to 2 W, the maximum temperature during the laser scanning procedure increases from 35.16 to 37.25 °C. In addition, the contrast increases from 1.09 to 2.16 °C (one can refer to the definition of contrast in Sec. 3.1). The above results indicate that the increasing power of laser beam would undoubtedly improve the sensitivity of thermal detection. However, noninjury consideration is another crucial problem for such enhanced approach. For the current case, the power of 2 W seems a safe choice for human body.

Figure 6 depicts the thermal responses induced by different action time of laser beam varying from 40 ms to 80 ms. The contrasts of all the cases increase from 1.09 to 2.55 °C. Therefore, the slight increase of action time of pulse laser can be considered as anther efficient way for enhancing the thermal manifestations of skin surface.

As shown in Fig. 7, when the moving frequency of laser beam varies from 0.1 Hz to 10 Hz, the contrast with a range of 1.03–1.86 °C can be obtained. It can be noted that for the case with higher moving frequency, the largest contrast occurs at the last moving time of laser beam, rather than the second moving time in case 1 (in Fig. 5) and case 2 (in Fig. 6). These results suggest that determination of appropriate parameters is of significance to improve the efficacy of the proposed method.

Effects of Property of Mg-NPs on the Enhanced Results.

In light of enhanced absorption of laser photon, better sensitivity of tumor detection has been obtained by introducing the Mg-NPs to the target tumor. There is also an urgent need to disclose various effects induced by the injected nanoparticles within the human body. In this section, we present the quantitative characterizations on the enhanced effects of varying amount of Mg-NPs. As depicted in Fig. 8, with the increase of the concentration of Mg-NPs from 0.00 to 0.10 mg/ml, the contrast of thermal manifestation increase from 0.56 to 2.09 °C. Therefore, by means of increasing the concentration of Mg-NPs, we can obtain a better performance of thermal detection. However, for the sake of safety, the low amount of NPs is still considered to be the best choice for enhancing the thermal manifestations of the tumor. For its good performance with the contrast of 1.09 °C, Mg-NPs with the concentration of 0.02 mg/ml can serve as an efficient enhancing agent of thermography in detecting the early tumor.

Conceptual Experiments.

In order to further estimate the enhanced effects of Mg-NPs, several in vitro experiments of solid phantom have also been carried out. In this work, four gelatin phantoms with Mg-NPs with concentrations of 0.00, 0.02, 0.06, and 0.10 mg/ml have been prepared. In addition, 3 × 10−3 percent of Chinese ink and 2% of Intralipid (Lipofndin MCT/LCT 20%, Sino-Swed Pharmaceutical Corp Ltd, China) were, respectively, used as an absorption component and a scattering component [47]. The Mg-NPs with size of 80 nm used in this study are commercially available and directly bought from Beijing Nachen S&T Ltd (Beijing, China).

A diode laser system (KS3-11311-106, BWT Beijing Ltd, China) with a continuous wave of 810 nm wavelength and maximal power of 30 W was adopted to produce the laser beam. A commercially available infrared camera (FLIR A40, FLIR, Inc., USA) with a high-resolution of 320 × 240 and thermal sensitivity of 0.08 °C was applied to record the real-time thermal response. The power of the laser beam was set as 1.0 W and the distance between the laser tip and phantom surface is about 4 cm. And the focus distance between the phantom and infrared camera was approximately set as 30 cm. For each case of Mg-NPs phantom with varying concentration, the experiment consisted of three parts, the steady state with time interval of 10 s, laser action with exposure time of 10 s, and the recovering period with duration time of 80 s. At the same time, the infrared camera kept on capturing the transient thermal response. It should be noted that the action time of laser beam in each experiment is set as 10 s due to the considerations for manual control of the used laser system.

Figure 9 presents the recorded thermal images of the Mg-NPs phantom with concentration of 0.02 mg/ml during the laser heating and recovering process. It can be observed that the Mg-NPs indeed effectively enhanced the thermal response of phantom during the laser stimulation process, as shown in Figs. 9(a)–9(d) and the temperature increases by 2.9 °C at the transient time of 20 s. In addition, more quantitative information concerning the transient temperature response profiles has also been extracted to estimate the enhanced effects of varying Mg-NPs concentration, as depicted in Fig. 10. The average temperature difference at the region of interest has been calculated to characterize the dynamic thermal responses of phantoms, where ΔT(t) = T(t) − T(0), T(t), and T(0) represent the transient and initial average temperature of the selected region; the region is a circular region with radius of 4 pixels in the thermal image and its center is the laser spot center. It can be observed that when the concentration of Mg-NPs increases from 0 to 0.1 mg/ml, the temperature difference increases from 1.5 to 5.6 °C. All the above preliminary results demonstrate that significant and measurable temperature differences are present in identifying the phantoms with varying Mg-NPs.

For the case with Mg-NPs with the concentration of 0.02 mg/ml, the simulation results suggest that the maximum temperature difference can reach to 1.09 °C in the area of laser spot. The temperature elevation of 2.9 °C appeared when laser exposure lasted for 10 s, and the maximum temperature difference in the area of laser spot can be up to 0.88 °C. However, the determination of physical property parameters for skin and tumor tissue is an unavoidable problem that led to the uncertainties of 3D temperature characterization. Besides, due to the limitation of laser device, the action time of laser beam in each experiment is set as 10 s, which would cause a deviation with the simulation results. Therefore, further work should focus on improving the sensitivity and specificity of the proposed method. Nevertheless, all the above preliminary attempts have demonstrated the feasibility of using biodegradable Mg-NP as an excellent agent for enhancing thermogaphy in detecting the early tumor.

In conclusion, through introducing a biodegradable nanomaterial (i.e., Mg-NPs) with relatively excellent property of laser photon absorption, this paper has proposed a laser scanning enhanced thermogaphic strategy for the targeted detection of skin cancer. The simulation and conceptual experimental results have quantitatively demonstrated the new method's feasibility in improving the sensitivity and targeting-ability (i.e., specificity) of the infrared thermography. However, there still exist certain uncertainties in the 3D photon distribution and temperature field prediction when evaluating the enhanced method as proposed in this study. Such limits can be attributed to two possible factors: (1) The simplification of the calculated model; and (2) the difficulty in measuring the actual physical parameters of either laser or tissues (e.g., absorption coefficient, blood perfusion, etc.). Therefore, further work should be focused on optimizing the simulation models for providing more quantitative references to guide the implementation of the enhanced strategy. Besides, there is also an urgent need to conduct a series of in vivo experiments to further justify and optimize the proposed method in detecting the early stage tumor.

This work was partially supported by the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20111011329, a NSFC Grant No. 51006114, and Tsinghua University Research Funding 20121088098.

 

 Nomenclature
  • r =

    position vector

  • s =

    direction vector

  • s' =

    direction vector

  • ω' =

    solid angle

  • μa =

    absorption coefficient (m−1)

  • μs =

    scattering coefficient (m−1)

  • g =

    anisotropic factor

  • μt =

    total attenuation coefficient, μt = μa + μs (m−1)

  • X =

    Cartesian coordinates x, y, z

  • ρ =

    tissue density (kg/m3)

  • c =

    tissue heat capacity (J/kg °C)

  • k =

    tissue thermal conductivity (W/m °C)

  • ρb =

    mass density of blood (kg/m3)

  • cb =

    heat capacity of blood (J/kg  °C)

  • ωb =

    blood perfusion rate (ml/s/ml)

  • Qm =

    metabolic heat production (W/m3)

  • Qr =

    heat source (W/m3)

  • Ts =

    temperature of skin surface (°C)

  • Tf =

    environmental temperature (°C)

  • hf =

    heat convection coefficient (W/m2 °C)

  • t =

    real time (s)

  • T =

    tissue temperature distribution (°C)

  • P =

    power of laser beam (W)

  • Δt =

    action time of laser beam (ms)

  • f =

    moving frequency of laser beam (Hz)

  • n =

    Mg-NPs concentration (mg/ml)

  • η =

    volume concentration of nanoparticles

 
 Subscripts
  • t =

    tissue

  • 1 =

    tumor tissue

  • 2 =

    Mg-NPs

  • 3 =

    the composite composed of the tumor tissue and Mg-NPs

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Mogensen, M., and Jemec, G. B. E., 2007, “Diagnosis of Nonmelanoma Skin Cancer/Keratinocyte Carcinoma: A Review of Diagnostic Accuracy of Nonmelanoma Skin Cancer Diagnostic Tests and Technologies,” Dermatol. Surg., 33(10), pp. 1158–1174. [CrossRef] [PubMed]
Mogensen, M., Joergensen, T. M., Nürnberg, B. M., Morsy, H. A., Thomsen, J. B., Thrane, L., and Jemec, G. B. E., 2009, “Assessment of Optical Coherence Tomography Imaging in the Diagnosis of Non-Melanoma Skin Cancer and Benign Lesions Versus Normal Skin: Observer-Blinded Evaluation by Dermatologists and Pathologists,” Dermatol. Surg., 35(6), pp. 965–972. [CrossRef] [PubMed]
Paoli, J., Smedh, M., Wennberg, A. M., and Ericson, M. B., 2007, “Multiphoton Laser Scanning Microscopy on Non-Melanoma Skin Cancer: Morphologic Features for Future Non-Invasive Diagnostics,” J. Invest Dermatol., 128, pp. 1248–1255. [CrossRef] [PubMed]
Mogensen, M., Thrane, L., Jørgensen, T. M., Andersen, P. E., and Jemec, G. B. E., 2009, “OCT Imaging of Skin Cancer and Other Dermatological Diseases,” J. Biophoton., 2(6–7), pp. 442–451. [CrossRef]
Woodward, R. M., Wallace, V. P., Pye, R. J., Cole, B. E., Arnone, D. D., Linfield, E. H., and Pepper, M., 2003, “Terhertz Pulse Imaging of Ex Vivo Basal Cell Carcinoma,” J. Invest Dermatol., 120(1), pp. 72–78. [CrossRef] [PubMed]
Wan, S. K., Guo, Z., Kumarb, S., Aberc, J., and Garetzc, B. A., 2004, “Noninvasive Detection of Inhomogeneities in Turbid Media With Time-Resolved Log-Slope Analysis,” J. Quant. Spectrosc. Radiative Transfer, 84(4), pp. 493–500. [CrossRef]
Guo, Z., Wan, S. K., August, D. A., Ying, J., Dunn, S. M., and Semmlow, J. L., 2006, “Optical Imaging of Breast Tumor Through Temporal Log-Slope Difference Mappings,” Comput. Biol. Med., 36(2), pp. 209–223. [CrossRef] [PubMed]
Jiao, J., and Guo, Z., 2009, “Thermal Interaction of Short-Pulsed Laser Focused Beams With Skin Tissues,” Phys. Med. Biol., 54(13), pp. 4225–4241. [CrossRef] [PubMed]
Jones, B. F., 1998, “A Reappraisal of the Use of Infrared Thermal Image Analysis in Medicine,” IEEE Trans. Med. Imaging, 17(6), pp. 1019–1027. [CrossRef] [PubMed]
Ng, E. Y. K., 2009, “A Review of Thermography as Promising Non-Invasive Detection Modality for Breast Tumor,” Int. J. Therm. Sci., 48(5), pp. 849–859. [CrossRef]
Kateb, B., Yamamoto, V., Yu, C., Grundfest, W., and Gruen, J. P., 2009, “Infrared Thermal Imaging: A Review of the Literature and Case Report,” Neuroimage, 47(2), pp. 154–162. [CrossRef]
Ohashi, Y., and Uchida, I., 2000, “Applying Dynamic Thermography in the Diagnosis of Breast Cancer,” IEEE Eng. Med. Biol., 19(3), pp. 42–51. [CrossRef]
Carlo, A. D., 2004, “Telethermography With Thermostimulus in the Study of Temporal Arteritis,” Infrared Phys. Technol., 46(1–2), pp. 57–61. [CrossRef]
Hu, L., Gupta, A., Gore, J. P., and Xu, L. X., 2004, “Effect of Forced Convection on the Skin Thermal Expression of Breast Cancer.,” ASME J. Biomech. Eng., 126(2), pp. 204–211. [CrossRef]
Bharara, M., Viswanathan, V., and Cobb, J. E., 2008, “Cold Immersion Recovery Responses in the Diabetic Foot With Neuropathy,” Int. Wound J., 5(4), pp. 562–569. [CrossRef] [PubMed]
Bharara, M., Viswanathan, V., and Cobb, J. E., 2008, “Warm Immersion Recovery Test in Assessment of Diabetic Neuropathy—A Proof of Concept Study,” Int. Wound J., 5(4), pp. 570–576. [CrossRef] [PubMed]
Schnell, H., and Zaspel, J., 2008, “Cooling Extensive Burns: Sprayed Coolants can Improve Initial Cooling Management: A Thermography-Based Study,” Burns, 34(4), pp. 505–508. [CrossRef] [PubMed]
Çetingül, M. P., and Herman, C., 2011, “Quantification of the Thermal Signature of a Melanoma Lesion,” Int. J. Therm. Sci., 50(4), pp. 421–431. [CrossRef]
Çetingül, M. P., and Herman, C., 2011, “The Assessment of Melanoma Risk Using the Dynamic Infrared Imaging Technique,” ASME J. Thermal Sci. Eng. Appl., 3(3), p. 031006. [CrossRef]
Weissleder, R., and Pittet, M. J., 2008, “Imaging in the Era of Molecular Oncology,” Nature, 452(7187), pp. 580–589. [CrossRef] [PubMed]
Corot, C., Robert, P., Idée, J. M., and Port, M., 2006, “Recent Advances in Iron Oxide Nanocrystal Technology for Medical Imaging,” Adv. Drug Delivery Rev., 58(14), pp. 1471–1504. [CrossRef]
Yan, J. F., and Liu, J., 2008, “Nanocryosurgery and Its Mechanisms for Enhancing Freezing Efficiency of Tumor Tissues,” Nanomedicine, 4(1), pp. 79–87. [CrossRef] [PubMed]
O'Neal, D. P., Hirsch, L. R., Halas, N. J., Payne, J. D., and West, J. L., 2004, “Photo-Thermal Tumor Ablation in Mice Using Near Infrared-Absorbing Nanoparticles,” Cancer Lett., 209(2), pp. 171–176. [CrossRef] [PubMed]
Levy, A., Dayan, A., Ben-David, M., and Gannot, I., 2010, “A New Thermography-Based Approach to Early Detection of Cancer Utilizing Magnetic Nanoparticles Theory Simulation and In Vitro Validation,” Nanomedicine, 6(6), pp. 786–796. [CrossRef] [PubMed]
Jakobsohn, K., Motiei, M., Sinvani, M., and Popovtzer, R., 2012, “Towards Real-Time Detection of Tumor Margins Using Photothermal Imaging of Immune-Targeted Gold Nanoparticles,” Int. J. Nanomed., 7, pp. 4707–4713. [CrossRef]
Pan, Y., Leifert, A., Ruau, D., Neuss, S., Bornemann, J., Schmid, G., Brandau, W., Simon, U., and Jahnen-Dechent, W., 2009, “Gold Nanoparticles of Diameter 1.4 nm Trigger Necrosis by Oxidative Stress and Mitochondrial Damage,” Small, 5(18), pp. 2067–2076. [CrossRef] [PubMed]
Ma, P., Luo, Q., Chen, J. E., Gan, Y. P., Du, J., Ding, S. M., Xi, Z. G., and Yang, X., 2012, “Intraperitoneal Injection of Magnetic Fe3O4-Nanoparticle Induces Hepatic and Renal Tissue Injury via Oxidative Stress in Mice,” Int. J. Nanomedicine, 7, pp. 4809–4818. [CrossRef] [PubMed]
Di, D. R., He, Z. Z., Sun, Z. Q., and Liu, J., 2012, “A New Nano-Cryosurgical Modality for Tumor Treatment Using Biodegradable MgO Nanoparticles,” Nanomedicine, 8, pp. 1233–1241. [CrossRef] [PubMed]
Wang, Q., Xie, L. P., He, Z. Z., Di, D. R., and Liu, J., 2012, “Biodegradable Magnesium Nanoparticle-Enhanced Laser Hyperthermia Therapy,” Int. J. Nanomed., 7, pp. 4715–4725. [CrossRef]
Dai, T., Pikkula, B. M., Wang, L. V., and Anvari, B., 2004, “Comparison of Human Skin Opto-Thermal Response to Near-Infrared and Visible Laser Irradiations: A Theoretical Investigation,” Phys. Med. Biol., 49(21), pp. 4861–4877. [CrossRef] [PubMed]
Niemz, M., 2002, Laser-Tissue Interactions: Fundamentals and Applications, Springer, Berlin.
Wang, L. H., and Jacques, S. L., 1995, “Monte Carlo Modeling of Light Transport in Multi-Layered Tissues in Standard C,” University of Texas M. D. Anderson Cancer Center, http://labs.seas.wustl.edu/bme/Wang/mcr5/Mcman.pdf
Wang, L., Jacques, S. L., and Zheng, L., 1995, “MCML—Monte Carlo Modeling of Light Transport in Multi-Layered Tissues,” Comput. Methods Programs Biomed., 47(2), pp. 131–146. [CrossRef] [PubMed]
Guo, Z., Kumar, S., and San, K. C., 2000, “Multidimensional Monte Carlo Simulation of Short-Pulse Laser Transport in Scattering Media,” J. Thermophys. Heat Transfer, 14(4), pp. 504–511. [CrossRef]
Zhou, J. H., and Liu, J., 2004, “Numerical Study on 3-D Light and Heat Transport in Biological Tissues Embedded With Large Blood Vessels During Laser-Induced Thermotherapy,” Numer. Heat Transfer, Part A, 45(5), pp. 415–449. [CrossRef]
Pennes, H. H., 1998, “Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm,” J. Appl. Physiol., 85(1), pp. 5–34. [PubMed]
Livne, E., and Glasner, A., 1985, “A Finite Difference Scheme for the Heat Conduction Equation,” J. Comput. Phys., 58(1), pp. 59–66. [CrossRef]
Jacques, S. L., 1998, “Skin Optics,” Oregon Medical Laser Center News, http://omlc.ogi.edu/news/jan98/skinoptics.html
Prahl, S., 1999, “Optical Absorption of Hemoglobin,” Oregon Medical Laser Center, http://omlc.ogi.edu/spectra/hemoglobin/index.html
He, B. H., Wang, J., and Li, L. B., 2008, “Characteristic of Absorption Coefficient Spectrum and Scattering Coefficient Spectrum for Human Gastric Adenocarcinoma,” Acta Med. Univ. Sci. Technol. Huazhong, 37(6), pp. 795–797 (in Chinese).
Çetingül, M. P., and Herman, C., 2010, “A Heat Transfer Model of Skin Tissue for the Detection of Lesions: Sensitivity Analysis,” Phys. Med. Biol., 55(19), pp. 5933–5951. [CrossRef] [PubMed]
Cubeddu, R., Pifferi, A., Taroni, P., Torricelli, A., and Valentini, G., 1997, “A Solid Tissue Phantom for Photon Migration Studies,” Phys. Med. Biol., 42(10), pp. 1971–1979. [CrossRef] [PubMed]
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References

Jemal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Murray, T., and Thun, M. J., 2008, “Cancer Statistics 2008,” Ca-Cancer J. Clin., 58(2), pp. 71–96. [CrossRef] [PubMed]
Diepgen, T. L., and Mahler, V., 2002, “The Epidemiology of Skin Cancer,” Br. J. Dermatol., 146(1), pp. 1–6. [CrossRef] [PubMed]
Smith, R. A., Cokkinides, V., and Eyre, H. J., 2006, “American Cancer Society Guidelines for the Early Detection of Cancer 2006,” Ca-Cancer J. Clin., 56(1), pp. 11–25. [CrossRef] [PubMed]
Jerant, A. F., Johnson, J. T., Sheridan, C. D., and Caffrey, T. J., 2000, “Early Detection and Treatment of Skin Cancer,” Am. Fam. Physician, 62(2), pp. 357–368. [PubMed]
Ulrich, M., Stockfleth, E., Roewert-Huber, J., and Astner, S., 2007, “Noninvasive Diagnostic Tools for Nonmelanoma Skin Cancer,” Br. J. Dermatol., 157(s2), pp. 56–58. [CrossRef] [PubMed]
Mogensen, M., and Jemec, G. B. E., 2007, “Diagnosis of Nonmelanoma Skin Cancer/Keratinocyte Carcinoma: A Review of Diagnostic Accuracy of Nonmelanoma Skin Cancer Diagnostic Tests and Technologies,” Dermatol. Surg., 33(10), pp. 1158–1174. [CrossRef] [PubMed]
Mogensen, M., Joergensen, T. M., Nürnberg, B. M., Morsy, H. A., Thomsen, J. B., Thrane, L., and Jemec, G. B. E., 2009, “Assessment of Optical Coherence Tomography Imaging in the Diagnosis of Non-Melanoma Skin Cancer and Benign Lesions Versus Normal Skin: Observer-Blinded Evaluation by Dermatologists and Pathologists,” Dermatol. Surg., 35(6), pp. 965–972. [CrossRef] [PubMed]
Paoli, J., Smedh, M., Wennberg, A. M., and Ericson, M. B., 2007, “Multiphoton Laser Scanning Microscopy on Non-Melanoma Skin Cancer: Morphologic Features for Future Non-Invasive Diagnostics,” J. Invest Dermatol., 128, pp. 1248–1255. [CrossRef] [PubMed]
Mogensen, M., Thrane, L., Jørgensen, T. M., Andersen, P. E., and Jemec, G. B. E., 2009, “OCT Imaging of Skin Cancer and Other Dermatological Diseases,” J. Biophoton., 2(6–7), pp. 442–451. [CrossRef]
Woodward, R. M., Wallace, V. P., Pye, R. J., Cole, B. E., Arnone, D. D., Linfield, E. H., and Pepper, M., 2003, “Terhertz Pulse Imaging of Ex Vivo Basal Cell Carcinoma,” J. Invest Dermatol., 120(1), pp. 72–78. [CrossRef] [PubMed]
Wan, S. K., Guo, Z., Kumarb, S., Aberc, J., and Garetzc, B. A., 2004, “Noninvasive Detection of Inhomogeneities in Turbid Media With Time-Resolved Log-Slope Analysis,” J. Quant. Spectrosc. Radiative Transfer, 84(4), pp. 493–500. [CrossRef]
Guo, Z., Wan, S. K., August, D. A., Ying, J., Dunn, S. M., and Semmlow, J. L., 2006, “Optical Imaging of Breast Tumor Through Temporal Log-Slope Difference Mappings,” Comput. Biol. Med., 36(2), pp. 209–223. [CrossRef] [PubMed]
Jiao, J., and Guo, Z., 2009, “Thermal Interaction of Short-Pulsed Laser Focused Beams With Skin Tissues,” Phys. Med. Biol., 54(13), pp. 4225–4241. [CrossRef] [PubMed]
Jones, B. F., 1998, “A Reappraisal of the Use of Infrared Thermal Image Analysis in Medicine,” IEEE Trans. Med. Imaging, 17(6), pp. 1019–1027. [CrossRef] [PubMed]
Ng, E. Y. K., 2009, “A Review of Thermography as Promising Non-Invasive Detection Modality for Breast Tumor,” Int. J. Therm. Sci., 48(5), pp. 849–859. [CrossRef]
Kateb, B., Yamamoto, V., Yu, C., Grundfest, W., and Gruen, J. P., 2009, “Infrared Thermal Imaging: A Review of the Literature and Case Report,” Neuroimage, 47(2), pp. 154–162. [CrossRef]
Ohashi, Y., and Uchida, I., 2000, “Applying Dynamic Thermography in the Diagnosis of Breast Cancer,” IEEE Eng. Med. Biol., 19(3), pp. 42–51. [CrossRef]
Carlo, A. D., 2004, “Telethermography With Thermostimulus in the Study of Temporal Arteritis,” Infrared Phys. Technol., 46(1–2), pp. 57–61. [CrossRef]
Hu, L., Gupta, A., Gore, J. P., and Xu, L. X., 2004, “Effect of Forced Convection on the Skin Thermal Expression of Breast Cancer.,” ASME J. Biomech. Eng., 126(2), pp. 204–211. [CrossRef]
Bharara, M., Viswanathan, V., and Cobb, J. E., 2008, “Cold Immersion Recovery Responses in the Diabetic Foot With Neuropathy,” Int. Wound J., 5(4), pp. 562–569. [CrossRef] [PubMed]
Bharara, M., Viswanathan, V., and Cobb, J. E., 2008, “Warm Immersion Recovery Test in Assessment of Diabetic Neuropathy—A Proof of Concept Study,” Int. Wound J., 5(4), pp. 570–576. [CrossRef] [PubMed]
Schnell, H., and Zaspel, J., 2008, “Cooling Extensive Burns: Sprayed Coolants can Improve Initial Cooling Management: A Thermography-Based Study,” Burns, 34(4), pp. 505–508. [CrossRef] [PubMed]
Çetingül, M. P., and Herman, C., 2011, “Quantification of the Thermal Signature of a Melanoma Lesion,” Int. J. Therm. Sci., 50(4), pp. 421–431. [CrossRef]
Çetingül, M. P., and Herman, C., 2011, “The Assessment of Melanoma Risk Using the Dynamic Infrared Imaging Technique,” ASME J. Thermal Sci. Eng. Appl., 3(3), p. 031006. [CrossRef]
Weissleder, R., and Pittet, M. J., 2008, “Imaging in the Era of Molecular Oncology,” Nature, 452(7187), pp. 580–589. [CrossRef] [PubMed]
Corot, C., Robert, P., Idée, J. M., and Port, M., 2006, “Recent Advances in Iron Oxide Nanocrystal Technology for Medical Imaging,” Adv. Drug Delivery Rev., 58(14), pp. 1471–1504. [CrossRef]
Yan, J. F., and Liu, J., 2008, “Nanocryosurgery and Its Mechanisms for Enhancing Freezing Efficiency of Tumor Tissues,” Nanomedicine, 4(1), pp. 79–87. [CrossRef] [PubMed]
O'Neal, D. P., Hirsch, L. R., Halas, N. J., Payne, J. D., and West, J. L., 2004, “Photo-Thermal Tumor Ablation in Mice Using Near Infrared-Absorbing Nanoparticles,” Cancer Lett., 209(2), pp. 171–176. [CrossRef] [PubMed]
Levy, A., Dayan, A., Ben-David, M., and Gannot, I., 2010, “A New Thermography-Based Approach to Early Detection of Cancer Utilizing Magnetic Nanoparticles Theory Simulation and In Vitro Validation,” Nanomedicine, 6(6), pp. 786–796. [CrossRef] [PubMed]
Jakobsohn, K., Motiei, M., Sinvani, M., and Popovtzer, R., 2012, “Towards Real-Time Detection of Tumor Margins Using Photothermal Imaging of Immune-Targeted Gold Nanoparticles,” Int. J. Nanomed., 7, pp. 4707–4713. [CrossRef]
Pan, Y., Leifert, A., Ruau, D., Neuss, S., Bornemann, J., Schmid, G., Brandau, W., Simon, U., and Jahnen-Dechent, W., 2009, “Gold Nanoparticles of Diameter 1.4 nm Trigger Necrosis by Oxidative Stress and Mitochondrial Damage,” Small, 5(18), pp. 2067–2076. [CrossRef] [PubMed]
Ma, P., Luo, Q., Chen, J. E., Gan, Y. P., Du, J., Ding, S. M., Xi, Z. G., and Yang, X., 2012, “Intraperitoneal Injection of Magnetic Fe3O4-Nanoparticle Induces Hepatic and Renal Tissue Injury via Oxidative Stress in Mice,” Int. J. Nanomedicine, 7, pp. 4809–4818. [CrossRef] [PubMed]
Di, D. R., He, Z. Z., Sun, Z. Q., and Liu, J., 2012, “A New Nano-Cryosurgical Modality for Tumor Treatment Using Biodegradable MgO Nanoparticles,” Nanomedicine, 8, pp. 1233–1241. [CrossRef] [PubMed]
Wang, Q., Xie, L. P., He, Z. Z., Di, D. R., and Liu, J., 2012, “Biodegradable Magnesium Nanoparticle-Enhanced Laser Hyperthermia Therapy,” Int. J. Nanomed., 7, pp. 4715–4725. [CrossRef]
Dai, T., Pikkula, B. M., Wang, L. V., and Anvari, B., 2004, “Comparison of Human Skin Opto-Thermal Response to Near-Infrared and Visible Laser Irradiations: A Theoretical Investigation,” Phys. Med. Biol., 49(21), pp. 4861–4877. [CrossRef] [PubMed]
Niemz, M., 2002, Laser-Tissue Interactions: Fundamentals and Applications, Springer, Berlin.
Wang, L. H., and Jacques, S. L., 1995, “Monte Carlo Modeling of Light Transport in Multi-Layered Tissues in Standard C,” University of Texas M. D. Anderson Cancer Center, http://labs.seas.wustl.edu/bme/Wang/mcr5/Mcman.pdf
Wang, L., Jacques, S. L., and Zheng, L., 1995, “MCML—Monte Carlo Modeling of Light Transport in Multi-Layered Tissues,” Comput. Methods Programs Biomed., 47(2), pp. 131–146. [CrossRef] [PubMed]
Guo, Z., Kumar, S., and San, K. C., 2000, “Multidimensional Monte Carlo Simulation of Short-Pulse Laser Transport in Scattering Media,” J. Thermophys. Heat Transfer, 14(4), pp. 504–511. [CrossRef]
Zhou, J. H., and Liu, J., 2004, “Numerical Study on 3-D Light and Heat Transport in Biological Tissues Embedded With Large Blood Vessels During Laser-Induced Thermotherapy,” Numer. Heat Transfer, Part A, 45(5), pp. 415–449. [CrossRef]
Pennes, H. H., 1998, “Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm,” J. Appl. Physiol., 85(1), pp. 5–34. [PubMed]
Livne, E., and Glasner, A., 1985, “A Finite Difference Scheme for the Heat Conduction Equation,” J. Comput. Phys., 58(1), pp. 59–66. [CrossRef]
Jacques, S. L., 1998, “Skin Optics,” Oregon Medical Laser Center News, http://omlc.ogi.edu/news/jan98/skinoptics.html
Prahl, S., 1999, “Optical Absorption of Hemoglobin,” Oregon Medical Laser Center, http://omlc.ogi.edu/spectra/hemoglobin/index.html
He, B. H., Wang, J., and Li, L. B., 2008, “Characteristic of Absorption Coefficient Spectrum and Scattering Coefficient Spectrum for Human Gastric Adenocarcinoma,” Acta Med. Univ. Sci. Technol. Huazhong, 37(6), pp. 795–797 (in Chinese).
Çetingül, M. P., and Herman, C., 2010, “A Heat Transfer Model of Skin Tissue for the Detection of Lesions: Sensitivity Analysis,” Phys. Med. Biol., 55(19), pp. 5933–5951. [CrossRef] [PubMed]
Cubeddu, R., Pifferi, A., Taroni, P., Torricelli, A., and Valentini, G., 1997, “A Solid Tissue Phantom for Photon Migration Studies,” Phys. Med. Biol., 42(10), pp. 1971–1979. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

Schematic of the proposed laser scanning modality for detecting the tumor embedded in the skin tissue

Grahic Jump Location
Fig. 5

The temperature response profile at the skin surface induced by the laser stimulation with varying power of 1 W–2 W at transient time of (a) t = 0.04 s, (b) t = 1.04 s, and (c) t = 2.04 s

Grahic Jump Location
Fig. 9

The thermal images of the solid phantom with Mg-NPs concentration of 0.02 mg/ml during the laser heating and recovering process. (a), (b), (c), and (d) represent the thermal image data at 0 s, 15 s, 20 s, and 25 s, respectively.

Grahic Jump Location
Fig. 2

Comparison between the nondimensional heat source without (a)–(c) and with (d)–(f) Mg-NPs during the laser scanning process at section y = 2.5 mm. Here, (a), (b), and (c), respectively, denote the simulation results when the laser beam is located at the site of x = 1.5 mm, 2.5 mm, and 3.5 mm (the default y = 2.5 mm and z = 0 mm); (d)–(f) are the corresponding calculation results of heat source for the case of tumor tissue with Mg-NPs.

Grahic Jump Location
Fig. 3

Comparison of the transient temperature difference profile between cases without (a) and with (b) Mg-NPs. Here, three profiles are the transient thermal response of three sites located at z = 0 mm, 0.10 mm, and 0.35 mm, respectively; the coordinate values of x and y of the three points are all set as 2.5 mm.

Grahic Jump Location
Fig. 4

Comparison between the temperature distributions at the skin surface without (a)–(c) and with (d)–(f) Mg-NPs during the laser scanning process at section y = 2.5 mm. Here, (a), (b), and (c), respectively, denote the simulation results when the laser beam is located at the site of x = 1.5 mm, 2.5 mm, and 3.5 mm (the default y = 2.5 mm and z = 0 mm); (d)–(f) are the corresponding calculation results of temperature mapping at the skin surface for the case of tumor composited with Mg-NPs.

Grahic Jump Location
Fig. 10

The temperature response profile of phantom samples with varying Mg-NPs concentrations of 0, 0.02, 0.06, and 0.10 mg/ml during the laser heating and recovering period in the region. Here, ΔT denotes the temperature difference between transient and initial average temperature in the circle region with 4 pixels in the thermal image (the center is laser spot center).

Grahic Jump Location
Fig. 6

The temperature response profile at the skin surface induced by the laser stimulation with varying action time of 40 ms–80 ms

Grahic Jump Location
Fig. 7

The temperature response profile at the skin surface induced by the laser stimulation with varying moving frequency of 0.1 Hz–10 Hz

Grahic Jump Location
Fig. 8

The temperature response profile at the skin surface induced by the laser stimulation for the cases of tumor composited with varying Mg-NPs concentrations of 0, 0.02, 0.06, and 0.10 mg/ml at transient time of (a) t = 0.04 s, (b) t = 1.04 s, and (c) t = 2.04 s. n denotes the concentration of Mg-NPs.

Tables

Table Grahic Jump Location
Table 1 Physical parameters for skin tissue, tumor, and NPs [34,35,43-46]
Table Footer NoteNote: “—” denotes the default value.

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