Latent diffusion-based image reconstruction for near-infrared spectral tomography.

Near infrared spectral tomography (NIRST) is a promising non-invasive imaging technique that uses near-infrared light to probe the optical properties of biological tissue. However, due to light scattering in tissue, NIRST image reconstruction is inherently ill-posed and highly sensitive to noise. To address this challenge, an image reconstruction algorithm (Diff-NIRST), which integrated a conditional latent diffusion model, an image autoencoding module, and a conditional signal encoder, was developed. The image autoencoding module reduced image dimensionality to ensure compatibility with the latent space, while the conditional signal encoder transformed NIRST measurements into conditioning inputs for the generative process. The conditional latent diffusion model then reconstructed images of chromophore concentrations. Validation was performed through simulations. The method was subsequently applied to data from a patient case. In numerical simulations, 100 phantoms were reconstructed containing one or two inclusions with diameters of 4-14 mm and varying contrast ratios (CR) of total hemoglobin (HbT) and water content between inclusions and background. Diff-NIRST consistently outperformed conventional Tikhonov regularization and two other deep learning approaches, yielding an average improvement >17.1% in peak signal-to-noise ratio (PSNR), >48.3% and >23.5% reduction in CR error and size error (SE) for HbT, respectively. For water, it achieved an average >11% increase in PSNR, >16.7% and >57.1% reduction in CR error and SE, respectively. Application to breast cancer patient data further demonstrated that Diff-NIRST, trained solely on simulation data, successfully reconstructed clinical images, underscoring its translation potential.