Multi‐Wavelength Photo‐Magnetic Imaging System for Photothermal Therapy Guidance

Abstract

Background and Objectives: In photothermal therapy, cancerous tissue is treated by the heat generated from absorbed light energy. For effective photothermal therapy, the parameters affecting the induced temperature should be determined before the treatment by modeling the increase in temperature via numerical simulations. However, accurate simulations can only be achieved when utilizing the accurate optical, thermal, and physiological properties of the treated tissue. Here, we propose a multiwavelength photo-magnetic imaging (PMI) technique that provides quantitative and spatially resolved tissue optical absorption maps at any wavelength within the nearinfrared (NIR) window to assist accurate photothermal therapy planning. Study Design/Materials and Methods: The study was conducted using our recently developed multi-wavelength PMI system, which operates at four laser wavelengths (760, 808, 860, and 980 nm). An agar tissue-simulating phantom containing water, lipid, and ink was illuminated using these wavelengths, and the slight internal laserinduced temperature rise was measured using magnetic resonance thermometry (MRT). The phantom optical absorption was recovered at the used wavelengths using our dedicated PMI image reconstruction algorithm. These absorption maps were then used to resolve the concentration of the tissue chromophores, and thus deduce its optical absorption spectrum in the NIR region based on the Beer-Lambert law. Results: The optical absorption of the phantom was successfully recovered at the used four wavelengths with an average error of~1.9%. The recovered absorption coefficient was then used to simulate temperature variations inside the phantom. A comparison between the modeled temperature maps and the MRT measured ones showed that these maps are in a good agreement with an average pseudo R 2 statistic of 0.992. These absorption values were used to successfully recover the concentration of the used chromophores. Finally, these concentrations are used to accurately calculate the total absorption spectrum of the phantom in the NIR spectral window with an average error as low as~2.3%. Conclusions:Multi-wavelength PMI demonstrated a great ability to assess the distribution of tissue chromophores, thus providing its total absorption at any wavelength within the NIR spectral range. Therefore, applications of photothermal therapy applied at NIR wavelengths can benefit from the absorption spectrum recovered by PMI to determine important parameters such as laser power as well as the laser exposure time needed to attain a specific increase in temperature prior to treatment. Lasers Surg. Med.

Improving the performance and convergence speed of photo magnetic imaging reconstruction algorithm using convolutional neural networks

“…Diffuse Optical Imaging (DOT) is a non-invasive imaging technique that uses near-infrared (NIR) light to recover tissue chromophore concentrations, enabling the distinction between diseased and normal tissues [1][2][3][4][5]. However, DOT is limited by its inherent poor spatial resolution, hindering its use in clinical practice [6].…”

“…A dedicated finite element method (FEM)based solver is used to model light propagation and temperature diffusion in tissue [2,10,11]. Then, the difference between these modeled temperature maps and the measured MRT maps is minimized to obtain the high resolution optical absorption map of the tissue [3,4]. PMI identifies abnormalities in bio-tissues based on their higher absorption of NIR light.…”

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach

“…PMI is able to achieve higher spatial resolution than conventional DOI methods thanks to the utilization of magnetic resonance thermometry (MRT) for the measurement of the laser-induced internal spatiotemporal temperature [19,20]. Following data acquisition, the high-resolution optical absorption maps are obtained using a dedicated PMI reconstruction algorithm [21,22]. The image reconstruction is based on the minimization of the difference between the measured and synthetic spatiotemporal temperature maps, which are simulated by solving the combined diffusion and Pennes' bio-heat equations using the finite element method (FEM) [23][24][25].…”