3Din vivodose verification in prostate proton therapy with deep learning-based proton-acoustic imaging

4Din vivodosimetry for a FLASH electron beam using radiation-induced acoustic imaging

Objective. The primary goal of this research is to demonstrate the feasibility of radiation-induced acoustic imaging (RAI) as a volumetric dosimetry tool for ultra-high dose rate FLASH electron radiotherapy (FLASH-RT) in real time. This technology aims to improve patient outcomes by accurate measurements ofin vivodose delivery to target tumor volumes.Approach. The study utilized the FLASH-capable eRT6 LINAC to deliver electron beams under various doses (1.2 Gy pulse-1to 4.95 Gy pulse-1) and instantaneous dose rates (1.55 × 105Gy s-1to 2.75 × 106Gy s-1), for imaging the beam in water and in a rabbit cadaver with RAI. A custom 256-element matrix ultrasound array was employed for real-time, volumetric (4D) imaging of individual pulses. This allowed for the exploration of dose linearity by varying the dose per pulse and analyzing the results through signal processing and image reconstruction in RAI.Main Results. By varying the dose per pulse through changes in source-to-surface distance, a direct correlation was established between the peak-to-peak amplitudes of pressure waves captured by the RAI system and the radiochromic film dose measurements. This correlation demonstrated dose rate linearity, including in the FLASH regime, without any saturation even at an instantaneous dose rate up to 2.75 × 106Gy s-1. Further, the use of the 2D matrix array enabled 4D tracking of FLASH electron beam dose distributions on animal tissue for the first time.Significance. This research successfully shows that 4Din vivodosimetry is feasible during FLASH-RT using a RAI system. It allows for precise spatial (∼mm) and temporal (25 frames s-1) monitoring of individual FLASH beamlets during delivery. This advancement is crucial for the clinical translation of FLASH-RT as enhancing the accuracy of dose delivery to the target volume the safety and efficacy of radiotherapeutic procedures will be improved.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue.Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification.Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy.Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool forinvivo3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poor signal-to-noise ratios (SNRs), thus requiring measuring hundreds or even thousands of frames for averaging to achieve satisfactory quality. This repetitive measurement increases ionizing radiation dose and degrades the temporal resolution of RA imaging, limiting its clinical utility. In this study, we developed a general deep inception convolutional neural network (GDI-CNN) to denoise RA signals to substantially reduce the number of frames needed for averaging. The network employs convolutions with multiple dilations in each inception block, allowing it to encode and decode signal features with varying temporal characteristics. This design generalizes GDI-CNN to denoise acoustic signals resulting from different radiation sources. The performance of the proposed method was evaluated using experimental data of x-ray-induced acoustic, protoacoustic, and electroacoustic signals both qualitatively and quantitatively. Results demonstrated the effectiveness of GDI-CNN: it achieved x-ray-induced acoustic image quality comparable to 750-frame-averaged results using only 10-frame-averaged measurements, reducing the imaging dose of x-ray-acoustic computed tomography (XACT) by 98.7%; it realized proton range accuracy parallel to 1500-frame-averaged results using only 20-frame-averaged measurements, improving the range verification frequency in proton therapy from 0.5 to 37.5 Hz; it reached electroacoustic image quality comparable to 750-frame-averaged results using only a single frame signal, increasing the electric field monitoring frequency from 1 fps to 1k fps. Compared to lowpass filter-based denoising, the proposed method demonstrated considerably lower mean-squared-errors, higher peak-SNR, and higher structural similarities with respect to the corresponding high-frame-averaged measurements. The proposed deep learning-based denoising framework is a generalized method for few-frame-averaged acoustic signal denoising, which significantly improves the RA imaging's clinical utilities for low-dose imaging and real-time therapy monitoring.

Single pulse protoacoustic range verification using a clinical synchrocyclotron

Objective.Proton therapy as the next generation radiation-based cancer therapy offers dominant advantages over conventional radiation therapy due to the utilization of the Bragg peak; however, range uncertainty in beam delivery substantially mitigates the advantages of proton therapy. This work reports using protoacoustic measurements to determine the location of proton Bragg peak deposition within a water phantom in real time during beam delivery.Approach.In protoacoustics, proton beams have a definitive range, depositing a majority of the dose at the Bragg peak; this dose is then converted to heat. The resulting thermoelastic expansion generates a 3D acoustic wave, which can be detected by acoustic detectors to localize the Bragg peak.Main results.Protoacoustic measurements were performed with a synchrocyclotron proton machine over the exhaustive energy range from 45.5 to 227.15 MeV in clinic. It was found that the amplitude of the acoustic waves is proportional to proton dose deposition, and therefore encodes dosimetric information. With the guidance of protoacoustics, each individual proton beam (7 pC/pulse) can be directly visualized with sub-millimeter (<0.7 mm) resolution using single beam pulse for the first time.Significance.The ability to localize the Bragg peak in real-time and obtain acoustic signals proportional to dose within tumors could enable precision proton therapy and hope to progress towardsin vivomeasurements.