Estimation of diffuse Cherenkov optical emission from external beam radiation build-up in tissue

Hybrid Monte Carlo model for efficient tissue Cherenkov emission estimation to assess changes from beam size, energy and incidence

PURPOSE

Cherenkov imaging in radiation therapy provides key knowledge of the delivery of treatment plans, but light-tissue interactions alter the emitted spectral signal and cause the modeling of emission relative to dose in highly modulated treatment plans to be complex.

METHODS

A 2-stage Monte Carlo approach to modeling Cherenkov emission was developed that leverages a traditional treatment planning system with an optical Monte Carlo simulation to provide a widely useable and efficient tool for modeling every beam control point for delivery interpretation of highly-modulated treatment plans. The emitted optical spectra were estimated for 6, 10, 15MV photon beams, 6 MeV electron beams, beam incidence in tissue, and square field sizes from 1 cm to 20 cm. The model was validated through comparison of measured Cherenkov emission from a blood and intralipid optical phantom.

RESULTS

The resulting hybrid model provides an efficient method of estimating Cherenkov emission for linac beams, showing a clear trend of decreasing emission intensity with increasing beam energy and strong emission intensity variation with beam type. The largest change in observed intensity was from altering field size, with a 76 % intensity decrease when going from 20 cm down to 1 cm square. The model showed agreement with experimental detected Cherenkov with an average percent difference of 6.2 % with the largest difference at the very smallest beam sizes.

CONCLUSION

The model potentially allows for modeling entire modulated treatment plans with high computational efficiency and is a key step to translate delivered dose and observed Cherenkov in highly modulated situations.

Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning

Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during Radiation Therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However until now, no concerted analysis of this biological feature-based tracking was utilized because of the slow speed and accuracy of conventional image processing for feature segmentation. This study demonstrated the first deep learning framework for such an application, achieving video frame rate processing. To address the challenge of limited annotation of these features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet segmentation framework. Subsequently, a small Cherenkov dataset (1,483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on another 19 patients, including subcutaneous veins, scars, and pigmented skin. Average segmentation by the model achieved Dice score of 0.85 and required less than 0.7 milliseconds processing time per instance. The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.

Color-resolved Cherenkov imaging allows for differential signal detection in blood and melanin content

Significance

High-energy x-ray delivery from a linear accelerator results in the production of spectrally continuous broadband Cherenkov light inside tissue. In the absence of attenuation, there is a linear relationship between Cherenkov emission and deposited dose; however, scattering and absorption result in the distortion of this linear relationship. As Cherenkov emission exits the absorption by tissue dominates the observed Cherenkov emission spectrum. Spectroscopic interpretation of this effects may help to better relate Cherenkov emission to ionizing radiation dose delivered during radiotherapy.

Aim

In this study, we examined how color Cherenkov imaging intensity variations are caused by absorption from both melanin and hemoglobin level variations, so that future Cherenkov emission imaging might be corrected for linearity to delivered dose.

Approach

A custom, time-gated, three-channel intensified camera was used to image the red, green, and blue wavelengths of Cherenkov emission from tissue phantoms with synthetic melanin layers and varying blood concentrations. Our hypothesis was that spectroscopic separation of Cherenkov emission would allow for the identification of attenuated signals that varied in response to changes in blood content versus melanin content, because of their different characteristic absorption spectra.

Results

Cherenkov emission scaled with dose linearly in all channels. Absorption in the blue and green channels increased with increasing oxy-hemoglobin in the blood to a greater extent than in the red channel. Melanin was found to absorb with only slight differences between all channels. These spectral differences can be used to derive dose from measured Cherenkov emission.

Conclusions

Color Cherenkov emission imaging may be used to improve the optical measurement and determination of dose delivered in tissues. Calibration for these factors to minimize the influence of the tissue types and skin tones may be possible using color camera system information based upon the linearity of the observed signals.

Comparison of surface dose during whole breast radiation therapy on Halcyon and TrueBeam using Cherenkov imaging

The emergence of the Halcyon linear accelerator has allowed for increased patient throughput and improved treatment times for common treatment sites in radiation oncology. However, it has been shown that this can lead to increased surface dose in sites like breast cancer compared with treatments on conventional machines with flattened radiation beams. Cherenkov imaging can be used to estimate surface dose by detection of Cherenkov photons emitted in proportion to energy deposition from high energy electrons in tissue. Phantom studies were performed with both square beams in reference conditions and with clinical treatments, and dosimeter readings and Cherenkov images report higher surface dose (25% for flat phantom entrance dose, 5.9% for breast phantom treatment) from Halcyon beam deliveries than for equivalent deliveries from a TrueBeam linac. Additionally, the first Cherenkov images of a patient treated with Halcyon were acquired, and superficial dose was estimated.

Application of Cherenkov radiation in tumor imaging and treatment

Cherenkov radiation (CR) is the characteristic blue glow that is generated during radiotherapy or radioisotope decay. Its distribution and intensity naturally reflect the actual dose and field of radiotherapy and the location of radioisotope imaging agents in vivo. Therefore, CR can represent a potential in situ light source for radiotherapy monitoring and radioisotope-based tumor imaging. When used in combination with new imaging techniques, molecular probes or nanomedicine, CR imaging exhibits unique advantages (accuracy, low cost, convenience and fast) in tumor radiotherapy monitoring and imaging. Furthermore, photosensitive nanomaterials can be used for CR photodynamic therapy, providing new approaches for integrating tumor imaging and treatment. Here the authors review the latest developments in the use of CR in tumor research and discuss current challenges and new directions for future studies.

Performance comparison of quantitative metrics for analysis of in vivo Cherenkov imaging incident detection during radiotherapy

OBJECTIVES

Examine the responses of multiple image similarity metrics to detect patient positioning errors in radiotherapy observed through Cherenkov imaging, which may be used to optimize automated incident detection.

METHODS

An anthropomorphic phantom mimicking patient vasculature, a biological marker seen in Cherenkov images, was simulated for a breast radiotherapy treatment. The phantom was systematically shifted in each translational direction, and Cherenkov images were captured during treatment delivery at each step. The responses of mutual information (MI) and the γ passing rate (%GP) were compared to that of existing field-shape matching image metrics, the Dice coefficient, and mean distance to conformity (MDC). Patient images containing other incidents were analyzed to verify the best detection algorithm for different incident types.

RESULTS

Positional shifts in all directions were registered by both MI and %GP, degrading monotonically as the shifts increased. Shifts in intensity, which may result from erythema or bolus-tissue air gaps, were detected most by %GP. However, neither metric detected beam-shape misalignment, such as that caused by dose to unintended areas, as well as currently employed metrics (Dice and MDC).

CONCLUSIONS

This study indicates that different radiotherapy incidents may be detected by comparing both inter- and intrafractional Cherenkov images with a corresponding image similarity metric, varying with the type of incident. Future work will involve determining appropriate thresholds per metric for automatic flagging.

ADVANCES IN KNOWLEDGE

Classifying different algorithms for the detection of various radiotherapy incidents allows for the development of an automatic flagging system, eliminating the burden of manual review of Cherenkov images.