Feasibility of two-dimensional dose distribution deconvolution using convolution neural networks

Feasibility study of a scintillation sheet-based detector for fluence monitoring during external photon beam radiotherapy

PURPOSE

This study evaluated the properties of a scintillation sheet-based dosimetry system for beam monitoring with high spatial resolution, including the effects of this system on the treatment beam. The dosimetric characteristics and feasibility of this system for clinical use were also evaluated.

METHODS

The effects of the dosimetry system on the beam were evaluated by measuring the percentage depth doses, dose profiles, and transmission factors. Fifteen treatment plans were created, and the influence of the dosimetry system on these clinical treatment plans was evaluated. The performance of the system was assessed by determining signal linearity, dose rate dependence, and reproducibility. The feasibility of the system for clinical use was evaluated by comparing intensity distributions with reference intensity distributions verified by quality assurance.

RESULTS

The spatial resolution of the dosimetry system was found to be 0.43 mm/pixel when projected to the isocenter plane. The dosimetry system attenuated the intensity of 6 MV beams by about 1.1%, without affecting the percentage depth doses and dose profiles. The response of the dosimetry system was linear, independent of the dose rate used in the clinic, and reproducible. Comparison of intensity distributions of evaluation treatment fields with reference intensity distributions showed that the 1%/1 mm average gamma passing rate was 99.6%.

CONCLUSIONS

The dosimetry system did not significantly alter the beam characteristics, indicating that the system could be implemented by using only a transmission factor. The dosimetry system is clinically suitable for monitoring treatment beam delivery with higher spatial resolution than other transmission detectors.

Deep learning-augmented radioluminescence imaging for radiotherapy dose verification

PURPOSE

We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.

METHODS

The CRIS consists of a cylindrical chamber coated with scintillator material on the inner surface of the cylinder, coupled with a hemispherical mirror and a digital camera at the two ends. After training, the deep learning model is used for image-to-dose conversion to provide absolute dose prediction at multiple depths of a specific water phantom from a single CRIS image under the assumption of a good consistency between the TPS setting and actual beam energy. The model was trained using a set of captured radioluminescence images and the corresponding dose maps from the clinical treatment planning system (TPS) for the sake of acceptable data collection. To overcome the latent error and inconsistency that exists between the TPS calculation and the corresponding measurement, the model was trained in an unsupervised manner. Validation experiments were performed on five square fields (ranging from 2 × 2 to 10 × 10 cm² ) and three clinical intensity-modulated radiation therapy (IMRT) cases. The results were compared to the TPS calculations in terms of gamma index at 1.5, 5, and 10 cm depths.

RESULTS

The mean 2%/2 mm gamma pass rates were 100% for square fields and 97.2% (range from 95.5% to 99.5%) for the IMRT fields. Further validations were performed by comparing the CRIS results with measurements on various regular fields. The results show a mean gamma pass rate of 91% (1%/1 mm) for cross-profiles and a mean percentage deviation of 1.15% for percentage depth doses (PDDs).

CONCLUSIONS

The system is capable of converting the irradiated radioluminescence image to corresponding water-based dose maps at multiple depths with a spatial resolution comparable to the TPS calculations.

Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy

PURPOSE

The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).

METHODS

A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations.

RESULTS

Validation experiments were performed using five regular fields and three clinical intensity-modulated radiation therapy (IMRT) cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2 mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to the corresponding TPS calculation in terms of the 3D gamma index. The mean 2%/2 mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields.

CONCLUSIONS

The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring.

Development of a time-resolved mirrorless scintillation detector

Purpose

We developed a compact and lightweight time-resolved mirrorless scintillation detector (TRMLSD) employing image processing techniques and a convolutional neural network (CNN) for high-resolution two-dimensional (2D) dosimetry.

Methods

The TRMLSD comprises a camera and an inorganic scintillator plate without a mirror. The camera was installed at a certain angle from the horizontal plane to collect scintillation from the scintillator plate. The geometric distortion due to the absence of a mirror and camera lens was corrected using a projective transform. Variations in brightness due to the distance between the image sensor and each point on the scintillator plate and the inhomogeneity of the material constituting the scintillator were corrected using a 20.0 × 20.0 cm² radiation field. Hot pixels were removed using a frame-based noise-reduction technique. Finally, a CNN-based 2D dose distribution deconvolution model was applied to compensate for the dose error in the penumbra region and a lack of backscatter. The linearity, reproducibility, dose rate dependency, and dose profile were tested for a 6 MV X-ray beam to verify dosimeter characteristics. Gamma analysis was performed for two simple and 10 clinical intensity-modulated radiation therapy (IMRT) plans.

Results

The dose linearity with brightness ranging from 0.0 cGy to 200.0 cGy was 0.9998 (R-squared value), and the root-mean-square error value was 1.010. For five consecutive measurements, the reproducibility was within 3% error, and the dose rate dependency was within 1%. The depth dose distribution and lateral dose profile coincided with the ionization chamber data with a 1% mean error. In 2D dosimetry for IMRT plans, the mean gamma passing rates with a 3%/3 mm gamma criterion for the two simple and ten clinical IMRT plans were 96.77% and 95.75%, respectively.

Conclusion

The verified accuracy and time-resolved characteristics of the dosimeter may be useful for the quality assurance of machines and patient-specific quality assurance for clinical step-and-shoot IMRT plans.

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate the optical image that suffers from mirror glare and edge blurring caused by photon scattering, a deep learning model is proposed and trained to learn from an on-board electronic portal imaging device (EPID). Beyond the typical purposes of an on-board EPID, the developed system maintains independent measurement with co-planar detection ability by involving a cylindrical receptor. Three task-aware modules are integrated into the network design to enhance its robustness against the artifacts that exist in an EPID running at the cine mode for efficient image acquisition. The training data consists of various designed beam fields that were modulated via the multi-leaf collimator (MLC). Validation experiments are performed for five regular fields ranging from 2 × 2 cm² to 10 × 10 cm² and three clinical IMRT cases. The captured CRIS images are compared to the high-quality images collected from an EPID running at the integration-mode, in terms of gamma index and other typical similarity metrics. The mean 2%/2 mm gamma pass rate is 99.14% (range between 98.6% and 100%) and 97.1% (ranging between 96.3% and 97.9%), for the regular fields and IMRT cases, respectively. The CRIS is further applied as a tool for MLC leaf-end position verification. A rectangular field with introduced leaf displacement is designed, and the measurements using CRIS and EPID agree within 0.100 mm ± 0.072 mm with maximum of 0.292 mm. Coupled with its simple system design and low-cost nature, the technique promises to provide viable choice for routine quality assurance in radiation oncology practice.