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MRI-LINACs combine MRI and LINAC technologies with the potential for image guided radiation therapy with optimal soft-tissue contrast. In this work, we present the advantages and limitations of plastic scintillation dosimeters (PSDs) for relative dosimetry with MRI-LINACs. PSDs possess many desirable qualities, including magnetic field insensitivity and irradiation angle independence, which are expected to make them suitable for dosimetry with MRI-LINACs. An in-house PSD was used to measure field size output factors as well as a percent depth dose distribution and the beam quality index TPR20/10 at a [Formula: see text] cm2 field size. Measurements were repeated with a Scanditronix/Wellhofer FC65-G ionisation chamber and PTW 60019 microDiamond detector for comparison. Relative differences were calculated between the three detectors, where the mean difference in dose was 1.2% between the PSD and ionisation chamber, 1.9% between the PSD and microDiamond detector and 1.3% between the microDiamond detector and the ionisation chamber. The closeness between the three mean differences in doses suggests that PSDs are feasible for relative dosimetry with MRI-LINACs.

Cherenkov radiation is the primary source of unwanted light in a scintillator dosimetry system. In this work we compare two techniques for temporally separating Cherenkov radiation from a slow scintillator signal. These techniques are applicable to a pulsed radiation beam. We found that by analysing the rising edge of the light pulse to identify the fast Cherenkov light only removed 74% of the Cherenkov light. By integrating the tail of the signal where only scintillation light is present a more accurate result is achieved. The average of the results of the two methods provides up to a 90% improvement in the accuracy of the relative dose when compared to ionisation chamber, in certain measurements. This work demonstrates an alternative methodology for the removal of Cherenkov light using signal analysis, while preserving all the scintillation light signal and minimising the bulk of the experimental equipment.

Purpose: The removal of Cherenkov light in an optical dosimetry system is an important process to ensure accurate dosimetry without compromising spatial resolution. Many solutions have been presented in the literature, each with advantages and disadvantages. We present a methodology to remove Cherenkov light from a scintillator fiber optic dosimeter in a pulsed megavoltage x-ray beam using the temporal waveform across the pulse. Methods: A sample waveform of Cherenkov light can be measured by exposing only the fiber to the beam. By assuming that the Cherenkov waveform closely matches the intensity of incident radiation, this waveform can be convoluted with the instantaneous scintillation response function to generate an expected scintillation signal. By finding the least-squares fit between these two functions and the experimental data, the estimated Cherenkov contribution can be subtracted off the net signal. This can be applied for arbitrarily complex Cherenkov waveforms (within the 2 ns timing resolution of the data acquisition), and in fact, the results suggest more fluctuations in the waveforms provide a better fit to data. Results: Four beam profiles for different field sizes and energies were found with this method. They closely matched references data measured with ionization chamber with average differences across the beam no more than 4%. Noisy waveforms are assumed to be the primary cause of differences between the analyzed scintillator and IC results. We propose methods for improving the results and optimizing the data acquisition and analysis processes. Conclusions: These results demonstrate that it is possible and effective with a single probe to use function fitting of expected data to experimental to remove a complicated Cherenkov signal from the net light signal in pulsed-beam optical dosimetry.

Convolutional neural network (CNN) type artificial intelligences were trained to estimate the Cerenkov radiation present in the temporal response of a LINAC irradiated scintillator-fiber optic dosimeter. The CNN estimate of Cerenkov radiation is subtracted from the combined scintillation and Cerenkov radiation temporal response of the irradiated scintillator-fiber optic dosimeter, giving the sole scintillation signal, which is proportional to the scintillator dose. The CNN measured scintillator dose was compared to the background subtraction measured scintillator dose and ionisation chamber measured dose. The dose discrepancy of the CNN measured dose was on average 1.4% with respect to the ionisation chamber measured dose, matching the 1.4% average dose discrepancy of the background subtraction measured dose with respect to the ionisation chamber measured dose. The developed CNNs had an average time of 3 ms to calculate scintillator dose, permitting the CNNs presented to be applicable for dosimetry in real time.

The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to determine the dose deposited in the scintillator volume. Artificial neural networks (ANNs) were trained and applied in a novel single probe method for the temporal separation of scintillation and Cerenkov radiation. Six dose profiles were measured using the ANN, with the dose profiles compared to those measured using background subtraction and an ionisation chamber. The average dose discrepancy of the ANN measured dose was 2.2% with respect to the ionisation chamber dose and 1.2% with respect to the background subtraction measured dose, while the average dose discrepancy of the background subtraction dose was 1.6% with respect to the ionisation chamber dose. The ANNs performance was degraded when compared with background subtraction, arising from an inaccurate model used to synthesise ANN training data.

Real-time target position verification during pancreas stereotactic body radiation therapy (SBRT) is important for the detection of unplanned tumour motions. Fast and accurate fiducial marker segmentation is a requirement of real-time marker-based verification. Deep learning (DL) segmentation techniques are ideal because they don’t require additional learning imaging or prior marker information (e.g., shape, orientation). In this study, we evaluated three DL frameworks for marker tracking applied to pancreatic cancer patient data. The DL frameworks evaluated were 1) a convolutional neural network (CNN) classifier with sliding window, 2) a pretrained you-only-look-once (YOLO) version-4 architecture, and 3) a hybrid CNN-YOLO. Intrafraction kV images collected during pancreas SBRT treatments were used as training data (44 fractions, 2017 frames). All patients had 1-4 implanted fiducial markers. Each model was evaluated on unseen kV images (42 fractions, 2517 frames). The ground truth was calculated from manual segmentation and triangulation of markers in orthogonal paired kV/MV images. The sensitivity, specificity, and area under the precision-recall curve (AUC) were calculated. In addition, the mean-absolute-error (MAE), root-mean-square-error (RMSE) and standard-error-of-mean (SEM) were calculated for the centroid of the markers predicted by the models, relative to the ground truth. The sensitivity and specificity of the CNN model were 99.41% and 99.69%, respectively. The AUC was 0.9998. The average precision of the YOLO model for different values of recall was 96.49%. The MAE of the three models in the left-right, superior-inferior, and anterior-posterior directions were under 0.88±0.11 mm, and the RMSE were under 1.09±0.12 mm. The detection times per frame on a GPU were 48.3, 22.9, and 17.1 milliseconds for the CNN, YOLO, and CNN-YOLO, respectively. The results demonstrate the accuracy and precision of marker position prediction by the DL models from the ground truth was submillimeter, and detection time was fast enough to meet the requirements for real-time application.

For the past few decades, fibre-optic dosimeters (FODs) have been a focus of research for dosimetry with LINACs, owing to a unique set of advantageous qualities: compact dosimeter sizes, an all optical composition (i.e. no wires or electronics around their sensitive volume), real-time response proportional to the absorbed dose-rate in their sensitive volumes and direct water equivalence. Such a set of qualities makes FODs “near-correctionless” for dosimetry with LINACs, such that they have been recommended as in vivo dosimeters and small field dosimeters. Further, their scintillation and luminescence response mechanisms are not affected by magnetic fields. Given this set of qualities, FODs are attractive candidates for dosimetry with MRI-LINACs. This mini-review aims to provide an overview of FODs to the wider medical physics community, and present the current challenges and opportunities for FODs given previous investigations into MRI-LINAC dosimetry.