Quantitative Bioluminescence Tomography for In Vivo Volumetric-Guided Radiotherapy

. Significance: Bioluminescence imaging and tomography (BLT) are used to study biologically relevant activity, typically within a mouse model. A major limitation is that the underlying optical properties of the volume are unknown, leading to the use of a “best” estimate approach often compromising quantitative accuracy. Aim: An optimization algorithm is presented that localizes the spatial distribution of bioluminescence by simultaneously recovering the optical properties and location of bioluminescence source from the same set of surface measurements. Approach: Measured data, using implanted self-illuminating sources as well as an orthotopic glioblastoma mouse model, are employed to recover three-dimensional spatial distribution of the bioluminescence source using a multi-parameter optimization algorithm. Results: The proposed algorithm is able to recover the size and location of the bioluminescence source while accounting for tissue attenuation. Localization accuracies of are obtained in all cases, which is similar if not better than current “gold standard” methods that predict optical properties using a different imaging modality. Conclusions: Application of this approach, using in-vivo experimental data has shown that quantitative BLT is possible without the need for any prior knowledge about optical parameters, paving the way toward quantitative molecular imaging of exogenous and indigenous biological tumor functionality.

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“…Details of both systems utilized in this work, together with system characterization and evaluation have been outlined previously. 18 , 21 , 23 …”

Section: Methodsmentioning

confidence: 99%

Quantitative molecular bioluminescence tomography

Bentley

Deng

et al. 2022

J. Biomed. Opt.

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Image-guided radiation therapy of tumors in preclinical models

Kraynak¹,

Marciscano²

2023

Methods in Cell Biology

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A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma

et al. 2023

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Objective: A novel solution is required for accurate 3D Bioluminescence Tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the X-ray imaging dose imposed by high-resolution micro cone-beam CT.Approach: A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models.Significance: Bioluminescence Imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI’s value in preclinical radiobiology research. Results: The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases.Conclusion: The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.

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Quantitative Bioluminescence Tomography for In Vivo Volumetric-Guided Radiotherapy

Cited by 3 publications

References 28 publications

Quantitative molecular bioluminescence tomography

Quantitative molecular bioluminescence tomography

Image-guided radiation therapy of tumors in preclinical models

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma

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