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

Gold nanoparticle-mediated photothermal therapy guidance with multi-wavelength photomagnetic imaging

Difficulty in heating tumors with high spatial selectivity while protecting surrounding healthy tissues from thermal harm is a challenge for cancer photothermal treatment (PTT). To mitigate this problem, PTT mediated by photothermal agents (PTAs) has been established as a potential therapeutic technique to boost selectivity and reduce damage to surrounding healthy tissues. Various gold nanoparticles (AuNP) have been effectively utilized as PTAs, mainly using strategies to target cancerous tissue and increase selective thermal damage. Meanwhile, imaging can be used in tandem to monitor the AuNP distribution and guide the PTT. Mainly, the parameters impacting the induced temperature can be determined using simulation tools before treatment for effective PTT. However, accurate simulations can only be performed if the amount of AuNPs accumulated in the tumor is known. This study introduces Photo-Magnetic Imaging (PMI), which can appropriately recover the AuNP concentration to guide the PTT. Using multi-wavelength measurements, PMI can provide AuNP concentration based on their distinct absorption spectra. Tissue-simulating phantom studies are conducted to demonstrate the potential of PMI in recovering AuNP concentration for PTT planning. The recovered AuNP concentration is used to model the temperature increase accurately in a small inclusion representing tumor using a multiphysics solver that takes into account the light propagation and heat diffusion in turbid media.

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

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.

Multiwavelength photo-magnetic imaging algorithm improved for direct chromophore concentration recovery using spectral constraints

Multiwavelength photo-magnetic imaging (PMI) is a novel combination of diffuse optics and magnetic resonance imaging, to the best of our knowledge, that yields tissue chromophore concentration maps with high resolution and quantitative accuracy. Here, we present the first experimental results, to the best of our knowledge, obtained using a spectrally constrained PMI image reconstruction method, where chromophore concentration maps are directly recovered, unlike the conventional two-step approach that requires an intermediate step of reconstructing wavelength-dependent absorption coefficient maps. The imposition of the prior spectral information into the PMI inverse problem improves the reconstructed image quality and allows recovery of highly quantitative concentration maps, which are crucial for effective cancer detection and characterization. The obtained results demonstrate the higher performance of the direct reconstruction method. Indeed, the reconstructed concentration maps are not only of higher quality but also obtained approximately 2 times faster than the conventional method.