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Title: | A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma | Authors: | REZAEIFAR, Behzad Wolfs, Cecile J. A. Lieuwes, Natasja G. Biemans, Rianne RENIERS, Brigitte Dubois, Ludwig J. Verhaegen, Frank |
Issue Date: | 2023 | Publisher: | IOP Publishing Ltd | Source: | PHYSICS IN MEDICINE AND BIOLOGY, 68 (15) (Art N° 155013) | Abstract: | 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. | Notes: | Verhaegen, F (corresponding author), Maastricht Univ, GROW Sch Oncol & Reprod, Dept Radiat Oncol Maastro, Med Ctr, Maastricht, Netherlands. frank.verhaegen@maastro.nl |
Keywords: | small animal precision radiotherapy;small animal precision radiotherapy;bioluminescence tomography reconstruction;bioluminescence tomography reconstruction;deep learning;deep learning;3D convolutional neural network;3D convolutional neural network;Monte Carlo simulation;Monte Carlo simulation | Document URI: | http://hdl.handle.net/1942/40770 | ISSN: | 0031-9155 | e-ISSN: | 1361-6560 | DOI: | 10.1088/1361-6560/ace308 | ISI #: | 001034177200001 | Rights: | 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Open access | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.pdf | Published version | 2.19 MB | Adobe PDF | View/Open |
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