We report a quantitative tissue imagining method that combines spatial frequency domain imaging (SFDI) and near-infrared spectroscopy (NIRS) by using an illumination of multiple scanned lines. SFDI is suitable for measuring superficial and shallow tissues (<a few mm) with sinusoidal or stripe illuminations while NIRS, that measures the points apart from a point illumination, is for deeper tissues (a few to tens mm). Our scheme performs these methods by one measurement and provide depth-dependent reflectance images for shallow and deep tissues. At every pixel, a series of the pixel values for all scanning steps is processed in two different ways: SFDI by fast Fourier transform and NIRS by finding the minimum value. We assembled a prototype system and measured a phantom and a human arm. The line pitch and wavelength were 10mm and 785nm, respectively. We obtained reflectance images from the 32 captured images using the SFDI processing for 0-0.4mm-1 and the NIRS processing for the line pitches of 0.625- 10mm. The images by SFDI for higher spatial frequencies depicted the shallower blood vessels. In those by NIRS, the contrast of the deeper blood vessels was enhanced for the larger line pitches.
In this study, spatial frequency domain imaging (SFDI) and temporal frequency domain imaging (TFDI) are combined to observe superficial and deep tissues simultaneously using a time-resolving CMOS image sensor. SFDI is an established non-invasive wide-field imaging method for superficial or shallow tissues. On the other hand, time-resolved spectroscopy based near-infrared spectroscopy (TRS-NIRS) is suitable for deep tissue measurement while it is based on point or multipoint measurement. Recently, time-resolving CMOS image sensors based on the single-photon avalanche diode and charge modulators have emerged. To take advantage of their area-imaging capability, we propose a spatio-temporal frequency domain imaging (STFDI=SFDI+TFDI) method, where pulsed binary stripe patterns with a pitch, p, and a duty ratio of 1/N are sequentially projected onto the tissue. While the projected pattern is shifted N times with a step of p/N, time-resolved images are captured for every shift. SFDI is conducted by performing fast Fourier transform (FFT) at each pixel after integrating them in time for each shift. For TFDI, the detected light in the middle of the stripes is analyzed by FFT in time for each shift. Based on the obtained amplitude and phase for specific harmonics orders, the absorption and scattering coefficients are estimated. This concept was verified by a GPU-based Monte Carlo simulator, MCX, with a two-layer skin model. We also experimentally confirmed the difference in the measured reflectance and phase for SFDI and TFDI when the thickness of the first layer was changed.
Diffuse optical tomography (DOT) images the distribution of the optical properties, such as the absorption and scattering coefficients, via the image reconstruction from the light intensities measured at the surface of the biological medium. The changes in the optical properties reflect the conditions of the tissues. Therefore, DOT image can provide the information which is not obtained from the other modalities and is useful for medical diagnoses. In this study, the application of the DOT to thyroid cancer diagnosis was investigated. The ultrasound technique is usually carried out for the thyroid cancer diagnosis. It is, however, difficult to distinguish follicular carcinoma from adenoma of thyroid. The optical properties may be helpful for the diagnosis. The image reconstruction algorithm employing the regularization minimizing lp-norm (0 < p < 2) of the reconstructed image was developed. The image was reconstructed from the timeresolved measurement data. The numerical simulations of the image reconstruction were tried. The numerical simulation demonstrated that the developed algorithm was able to image the changes in the optical properties in the medium. Additionally, the image reconstruction of the numerical neck phantom was simulated. The thyroid cancer region was reconstructed successfully. It was demonstrated that the developed algorithm had the possibility to image thyroid cancer.
It has been reported that the use of illumination with sinusoidal intensity pattern is efficient in noncontact optical tomography. Although this imaging method of using spatial frequency is promising, most research so far has relied on the diffusion approximation to the radiative transport equation. Here, a numerical algorithm of optical tomography with spatially sinusoidal illumination for the radiative transport equation are proposed. With the help of the technique of rotated reference frames, the forward problem, i.e., the three-dimensional radiative transport equation is solved by the three-dimensional FN method. Then the inverse problem is solved by making use of the Green’s function, which is the solution to the forward problem.
Diffuse optical tomography (DOT) has been employed to derive spatial maps of physiologically important chromophores in the human breast, but the fidelity of these images is often compromised by boundary effects such as those due to the chest wall. We explore the image quality in fast, data-intensive analytic and algebraic linear DOT reconstructions of phantoms with subcentimeter target features and large absorptive regions mimicking the chest wall. Experiments demonstrate that the chest wall phantom can introduce severe image artifacts. We then show how these artifacts can be mitigated by exclusion of data affected by the chest wall. We also introduce and demonstrate a linear algebraic reconstruction method well suited for very large data sets in the presence of a chest wall.
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