Evaluation of a neural network‐based photon beam profile deconvolution method

A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans

BACKGROUND

Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming. Independent calculation-based methods leveraging advanced dose algorithms provide a reduced workload but cannot account for machine performance during delivery.

PURPOSE

This study introduces a novel unified deep-learning (DL) framework to enhance PSQA. The framework can combine the strengths of measurement- and calculation-based approaches.

METHODS

A comprehensive artificial training dataset, comprising 400,000 samples, was generated based on a rigorous mathematical model that describes the physical processes of radiation transport and interaction within both the medium and detector. This artificial data was used to pretrain the DL models, which were subsequently fine-tuned with a measured dataset of 400 IMRT segments to capture the machine-specific characteristics. Additional measurements of five IMRT plans were used as the unseen test dataset. Within the unified framework, a forward prediction model uses plan parameters to predict the measured dose distributions, while the backward prediction model reconstructs these parameters from actual measurements. The former enables a detailed control point (CP)-wise analysis. At the same time, the latter facilitates the reconstruction of treatment plans from the measurements and, subsequently, dose recalculation in the treatment planning system (TPS), as well as an independent second check software (VERIQA). This method has been tested with an OD 1600 SRS and an OD 1500 detector array with distinct spatial resolution and detector arrangement in combination with a dedicated upsampling model for the latter.

RESULTS

The final models could deliver highly accurate predictions of the measurements in the forward direction and the actual delivered plan parameters in the backward direction. In the forward direction, the test plans reached median gamma passing rates better than 94% for the OD 1600 SRS measurements. The upsampled OD 1500 measurements show similar performance with similar median gamma passing rates but a slightly higher variability. The 3D gamma passing rates from the comparisons between the original and reconstructed dose distributions in patients lie between 95.4% and 98.2% for the OD 1600 SRS and 94.7% and 98.5% for the interpolated OD 1500 measurements. The dose volume histograms (DVH) of the original and the reconstructed plans, recalculated in both the TPS and VERIQA, were evaluated for the organs at risk and targets based on clinical protocols and showed no clinically relevant deviations.

CONCLUSIONS

The flexibility of the implemented model architecture allows its adaptability to other delivery techniques and measurement modalities. Its utilization also reduces the requirements of the measurement devices. The proposed unified framework could play a decisive role in automating QA workflow, especially in the context of real-time adaptive radiation therapy (ART).

Comparison and estimation of photoneutron dose produce between 10 MV flattened and unflattened beam in Elekta Versa HD™ medical linac

BACKGROUND

In a high-energy medical linear accelerator (linac), if the interaction of photon energy is higher than the neutron binding energy of high atomic material, it emits a neutron field through photonuclear (γ, n) reaction.

AIM

The current study, evaluates the photoneutron dose equivalent (PNDE) produced between the 10 MV flattened and unflattened beams as a function of field sizes in the Elekta Versa HD™ linac.

MATERIALS AND METHODS

The PNDE produced from Versa linac was recorded along the patient plane using the bubble detector personal neutron dosimeter and from the measured PNDE values, the theoretical PNDE values were simulated for various field sizes using nonlinear least-squares curve-fitting as a function of a polynomial. The percentage of deviation (PoD) and Chi-square (χ2) tests were performed between the measured and simulated PNDE values to study the reliability and validity.

RESULTS

The results show that the mean PoD between the measured and simulated PNDE values for respective positions of a field size of FF beam was found to be -1.99% for 0.3×0.3, -4.39% for 5×5, -3.868% for 10×10, 0.590% for 15×15, 9.18% for 20×20, -4.133% for 25×25, and 0.467% for 30×30 cm2. Similarly, the mean PoD between the measured and simulated PNDE values for flattening filter-free (FFF) beam was found to be 1.36% for 0.3×0.3, -1.39% for 5×5, -5.38% for 10×10, 4.41% for 15×15, 3.84% for 20×20, 5.69% for 25×25, and -1.75% for 30×30 cm2. The maximum deviation between the measured and simulated PNDE values lies within the range ± 5%.

CONCLUSIONS

From the study, it is observed that the FFF beam produces lesser neutron contamination than the FF beam.

Model-based machine learning for the recovery of lateral dose profiles of small photon fields in magnetic field

Objective. To investigate the feasibility to train artificial neural networks (NN) to recover lateral dose profiles from detector measurements in a magnetic field. Approach. A novel framework based on a mathematical convolution model has been proposed to generate measurement-less training dataset. 2D dose deposition kernels and detector lateral fluence response functions of two air-filled ionization chambers and two diode-type detectors have been simulated without magnetic field and for magnetic field B = 0.35 and 1.5 T. Using these convolution kernels, training dataset consisting pairs of dose profiles D x , y and signal profiles M x , y were computed for a total of 108 2D photon fluence profiles ψ ( x , y ) (80% training/20% validation). The NN were tested using three independent datasets, where the second test dataset has been obtained from simulations using realistic phase space files of clinical linear accelerator and the third test dataset was measured at a conventional linac equipped with electromagnets. Main results. The convolution kernels show magnetic field dependence due to the influence of the Lorentz force on the electron transport in the water phantom and detectors. The NN show good performance during training and validation with mean square error reaching a value of 1e-6 or smaller. The corresponding correlation coefficients R reached the value of 1 for all models indicating an excellent agreement between expected D x , y and predicted D pred x , y . The comparisons between D x , y and D pred x , y using the three test datasets resulted in gamma indices (1 mm/1% global) <1 for all evaluated data points. Significance. Two verification approaches have been proposed to warrant the mathematical consistencies of the NN outputs. Besides offering a correction strategy not existed so far for relative dosimetry in a magnetic field, this work could help to raise awareness and to improve understanding on the distortion of detector’s signal profiles by a magnetic field.

Artificial intelligence based deconvolving on megavoltage photon beam profiles for radiotherapy applications

Objective. The aim of this work is an AI based approach to reduce the volume effect of ionization chambers used to measure high energy photon beams in radiotherapy. In particular for profile measurements, the air-filled volume leads to an inaccurate measurement of the penumbra. Approach. The AI-based approach presented in this study was trained with synthetic data intended to cover a wide range of realistic linear accelerator data. The synthetic data was created by randomly generating profiles and convolving them with the lateral response function of a Semiflex 3D ionization chamber. The neuronal network was implemented using the open source tensorflow.keras machine learning framework and a U-Net architecture. The approach was validated on three accelerator types (Varian TrueBeam, Elekta VersaHD, Siemens Artiste) at FF and FFF energies between 6 MV and 18 MV at three measurement depths. For each validation, a Semiflex 3D measurement was compared against a microDiamond measurement, and the AI processed Semiflex 3D measurement was compared against the microDiamond measurement. Main results. The AI approach was validated with dataset containing 306 profiles measured with Semiflex 3D ionization chamber and microDiamond. In 90% of the cases, the AI processed Semiflex 3D dataset agrees with the microDiamond dataset within 0.5 mm/2% gamma criterion. 77% of the AI processed Semiflex 3D measurements show a penumbra difference to the microDiamond of less than 0.5 mm, 99% of less than 1 mm. Significance. This AI approach is the first in the field of dosimetry which uses synthetic training data. Thus, the approach is able to cover a wide range of accelerators and the whole specified field size range of the ionization chamber. The application of the AI approach offers an quality improvement and time saving for measurements in the water phantom, in particular for large field sizes.

Corrections of photon beam profiles of small fields measured with ionization chambers using a three-layer neural network

The purpose of this work is to study the feasibility of photon beam profile deconvolution using a feedforward neural network (NN) in very small fields (down to 0.56 × 0.56 cm²). The method's independence of the delivery and scanning system is also investigated. Lateral beam profiles of photon fields between 0.56 × 0.56 cm² and 4.03 × 4.03 cm² were collected on a Siemens Artiste linear accelerator. Three scanning ionization chambers (SNC 125c, PTW 31021, and PTW 31022) of sensitive volumes ranging from 0.016 cm³ to 0.108 cm³ were used with a PTW MP3 water phantom. A reference dataset was also collected with a PTW 60019 microDiamond detector to train and test individual NNs for each ionization chamber. Further testing of the trained NNs was performed with additional test data collected on an Elekta Synergy linear accelerator using a Sun Nuclear 3D Scanner. The results were evaluated with a 1D gamma analysis (0.5 mm/0.5%). After the deconvolution, the gamma passing rates increased from 54.79% to 99.58% for the SNC 125c, from 57.09% to 99.83% for the PTW 31021, and from 91.03% to 96.36% for the PTW 31022. The delivery system, the scanning system, the scanning mode (continuous vs. step‐by‐step), and the electrometer had no significant influence on the results. This study successfully demonstrated the feasibility of using NN to correct the beam profiles of very small photon fields collected with ionization chambers of various sizes. Its independence of the delivery and scanning system was also shown.

Reconstruction of volume averaging effect-free continuous photon beam profiles from discrete ionization chamber array measurements using a machine learning technique

PURPOSE

The use of the ionization chamber array ICProfiler (ICP) is limited by its relatively poor detector spatial resolution and the inherent volume averaging effect (VAE). The purpose of this work is to study the feasibility of reconstructing VAE-free continuous photon beam profiles from ICP measurements with a machine learning technique.

METHODS

In- and cross-plane photon beam profiles of a 6 MV beam from an Elekta linear accelerator, ranging from 2 × 2 to 10 × 10 cm² at 1.5 cm, 5 cm, and 10 cm depth, were measured with an ICP. The discrete measurements were interpolated with a Makima method to obtain continuous beam profiles. Artificial neural networks (ANNs) were trained to restore the penumbra of the beam profiles. Plane-specific (in- and cr-plane) ANNs and a combined ANN were separately trained. The performance of the ANNs was evaluated using the penumbra width difference (PWD, the difference between the penumbra widths of the reconstructed and the reference profile). The plane-specific and the combined ANNs were compared to study the feasibility of using a single ANN for both in- and cross-plane.

RESULTS

The profiles reconstructed with all the ANNs had excellent agreement with the reference. For in-plane, the ANNs reduced the PWD from 1.6 ± 0.7 mm at 1.5 cm depth to 0.1 ± 0.1 mm, from 1.8 ± 0.6 mm at 5.0 cm depth to 0.1 ± 0.1 mm, and from 2.4 ± 0.1 mm at 10.0 cm depth to 0.0 ± 0.0 mm; for cross-plane, the ANNs reduced the PWD from 1.2 ± 0.4 mm at 1.5 cm depth, 1.2 ± 0.3 mm at 5.0 cm depth, and 1.6 ± 0.1 mm at 10.0 cm depth, to 0.1 ± 0.1 mm.

CONCLUSIONS

This study demonstrated the feasibility of using simple ANNs to reconstruct VAE-free continuous photon beam profiles from discrete ICP measurements. A combined ANN can restore the penumbra of in- and cross-plane beam profiles of various fields at different depths.