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

Abstract

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.

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

“…The PMI image reconstruction algorithm uses a gradient descent to minimize the difference between the measured MRT maps and simulated temperature distributions within the medium. The simulated temperature maps are generated by solving PMI forward problem which consists in modeling the propagation of light in the medium using the diffusion equation and the laser-induced increase in temperature using the Pennes bio-heat equation [2]:…”

“…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].…”

“…It uses MRT to dynamically acquire internal temperature maps while the tissue is slightly heated with a NIR laser light [9]. 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].…”

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

“…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]. This conventional FEM-based algorithm performs highly but remains limited by its relatively long computation time.…”