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

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.

Dual-energy CT evaluation of 3D printed materials for radiotherapy applications.

Objective. There is a continuous increase in 3D printing applications in several fields including medical imaging and radiotherapy. Although there are numerous advantages of using 3D printing for the development of customized phantoms, bolus, quality assurance devices and other clinical applications, material properties are not well known and printer settings can affect considerably the properties (e.g. density, isotropy and homogeneity) of the printed parts. This study aims to evaluate several materials and printer properties to identify a range of tissue-mimicking materials.Approach. Dual-energy CT was used to obtain the effective atomic number (Zeff) and relative electron density (RED) for thirty-one different materials including different colours of the same filament from the same manufacturer and the same type of filament from different manufacturers. In addition, a custom bone equivalent filament was developed and evaluated since a high-density filament with a composition similar to bone is not commercially available. Printing settings such as infill density, infill pattern, layer height and nozzle size were also evaluated.Main results. Large differences were observed for HU (288), RED (>10%) andZeff(>50%) for different colours of the same filament due to the colour pigment. Results show a wide HU variation (-714 to 1104), RED (0.277 to 1.480) andZeff(5.22 to 12.39) between the printed samples with some materials being comparable to commercial tissue-mimicking materials and good substitutes to a range of materials from lung to bone. Printer settings can result in directional dependency and significantly affect the homogeneity of the samples.Significance. The use of DECT to extract RED, andZeffallows for quantitative imaging and dosimetry using 3D printed materials equivalent to certified tissue-mimicking tissues.

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

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.

Use of a Luciferase-Expressing Orthotopic Rat Brain Tumor Model to Optimize a Targeted Irradiation Strategy for Efficacy Testing with Temozolomide.

Glioblastoma multiforme (GBM) is a common and aggressive malignant brain cancer with a mean survival time of approximately 15 months after initial diagnosis. Currently, the standard-of-care (SOC) treatment for this disease consists of radiotherapy (RT) with concomitant and adjuvant temozolomide (TMZ). We sought to develop an orthotopic preclinical model of GBM and to optimize a protocol for non-invasive monitoring of tumor growth, allowing for determination of the efficacy of SOC therapy using a targeted RT strategy combined with TMZ. A strong correlation (r = 0.80) was observed between contrast-enhanced (CE)-CT-based volume quantification and bioluminescent (BLI)-integrated image intensity when monitoring tumor growth, allowing for BLI imaging as a substitute for CE-CT. An optimized parallel-opposed single-angle RT beam plan delivered on average 96% of the expected RT dose (20, 30 or 60 Gy) to the tumor. Normal tissue on the ipsilateral and contralateral sides of the brain were spared 84% and 99% of the expected dose, respectively. An increase in median survival time was demonstrated for all SOC regimens compared to untreated controls (average 5.2 days, p < 0.05), but treatment was not curative, suggesting the need for novel treatment options to increase therapeutic efficacy.