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Title: | A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting | Authors: | REZAEIFAR, Behzad Wolfs, Cecile J. A. Lieuwes, Natasja G. Biemans, Rianne RENIERS, Brigitte Dubois, Ludwig J. Verhaegen , Frank |
Issue Date: | 2022 | Publisher: | IOP Publishing Ltd | Source: | Physics in medicine and biology (Print), 67 (14) (Art N° 144003) | Abstract: | Objective. Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon scattering and lack of spatial information. Attempts at reconstructing bioluminescence tomography (BLT) using mathematical models of light propagation show limited progress. Approach. This paper employed a different approach by using a deep convolutional neural network (CNN) to predict the tumor's center of mass (CoM). Transfer-learning with a sizeable artificial database is employed to facilitate the training process for, the much smaller, target database including Monte Carlo (MC) simulations of real orthotopic glioblastoma models. Predicted CoM was then used to estimate a BLI-based planning target volume (bPTV), by using the CoM as the center of a sphere, encompassing the tumor. The volume of the encompassing target sphere was estimated based on the total number of photons reaching the skin surface. Main results. Results show sub-millimeter accuracy for CoM prediction with a median error of 0.59 mm. The proposed method also provides promising performance for BLI-based tumor targeting with on average 94% of the tumor inside the bPTV while keeping the average healthy tissue coverage below 10%. Significance. This work introduced a framework for developing and using a CNN for targeted radiation studies for GBM based on BLI. The framework will enable biologists to use BLI as their main image-guidance tool to target GBM tumors in rat models, avoiding delivery of high x-ray imaging dose to the animals. | Notes: | Verhaegen, F (corresponding author), Maastricht Univ, Med Ctr, GROW Sch Oncol & Reprod, Dept Radiat Oncol Maastro, Maastricht, Netherlands. frank.verhaegen@maastro.nl |
Keywords: | small animal precision radiotherapy;bioluminescence tomography reconstruction;deep learning;3D convolutional neural network;center of mass;transfer learning;Monte Carlo simulation | Document URI: | http://hdl.handle.net/1942/37897 | ISSN: | 0031-9155 | e-ISSN: | 1361-6560 | DOI: | 10.1088/1361-6560/ac79f8 | ISI #: | 000825719100001 | Rights: | 2022 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd. Open access | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.pdf | Published version | 3.07 MB | Adobe PDF | View/Open |
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