Molecular Tomography Techniques in Biological Imaging

Molecular tomography comprises imaging modalities that reconstruct three-dimensional distributions of molecular probes or light‐emitting sources inside living tissues. By modelling the transport of photons or other energy emissions through scattering and absorbing media, these techniques overcome surface‐weighted biases inherent to planar optical methods. Key modalities include fluorescence molecular tomography (FMT), which maps the spatial distribution of fluorescent markers, and bioluminescence tomography (BLT), which localises self-illuminating biological emitters. Emerging approaches such as X-ray luminescence computed tomography (XLCT) combine high-energy excitation with optical read-out to achieve deeper penetration and higher spatial resolution. Integration with structural imaging—such as computed tomography or magnetic resonance—provides anatomical priors that enhance quantitative accuracy. Advances in computational algorithms, regularisation strategies and machine-learning reconstructions continue to expand sensitivity, resolution and speed, enabling non-invasive in vivo studies of disease processes, drug delivery and biomarker dynamics.

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Molecular Tomography Techniques in Biological Imaging publication trend

The graph below shows the total number of articles in molecular tomography techniques in biological imaging across all publications each year (not limited to Nature Index journals).

Technical terms

Tomographic reconstruction: Computational process of recovering a 3D internal distribution from external measurements.

Inverse problem: Ill-posed challenge of inferring source distributions from scattered and absorbed signal data.

Fluorescence molecular tomography (FMT): Technique that reconstructs the three-dimensional distribution of fluorescent probes within biological tissues.

Bioluminescence tomography (BLT): Modality that images self-emitting biological sources by solving an inverse photon transport problem.

X-ray luminescence computed tomography (XLCT): Hybrid method combining X-ray excitation and optical detection to map luminescent nanoprobes at depth.

Near-infrared (NIR) window: Spectral bands (NIR-I and NIR-II) where tissue absorption and scattering are minimised for deep optical imaging.

Sparsity regularisation: Mathematical constraint that promotes solutions with few non-zero elements, enhancing stability of tomographic inversions.

Locally connected network: Neural network architecture that refines image features by restricting connections to spatially local neighbourhoods.

References

1. Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation. Bioengineering (2024).
2. NIR-II/NIR-I Fluorescence Molecular Tomography of Heterogeneous Mice Based on Gaussian Weighted Neighborhood Fused Lasso Method. IEEE Transactions on Medical Imaging (2020).
3. K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography. IEEE Transactions on Medical Imaging (2020).