Discrete Wavelet Transformation for the Sensitive Detection of Ultrashort Radiation Pulse with Radiation-induced Acoustics.

Radiation-induced acoustics (RIA) shows promise in advancing radiological imaging and radiotherapy dosimetry methods. However, RIA signals often require extensive averaging to achieve reasonable signal-to-noise ratios, which increases patient radiation exposure and limits real-time applications. Therefore, this paper proposes a discrete wavelet transform (DWT) based filtering approach to denoise the RIA signals and avoid extensive averaging. The algorithm was benchmarked against low-pass filters and tested on various types of RIA sources, including low-energy X-rays, high-energy X-rays, and protons. The proposed method significantly reduced the required averages (1000 times less averaging for low-energy X-ray RIA, 32 times less averaging for high-energy X-ray RIA, and 4 times less averaging for proton RIA) and demonstrated robustness in filtering signals from different sources of radiation. The coif5 wavelet in conjunction with the sqtwolog threshold selection algorithm yielded the best results. The proposed DWT filtering method enables high-quality, automated, and robust filtering of RIA signals, with a performance similar to low-pass filtering, aiding in the clinical translation of radiation-based acoustic imaging for radiology and radiation oncology.

Toward real-time, volumetric dosimetry for FLASH-capable clinical synchrocyclotrons using protoacoustic imaging.

BACKGROUND: Fast low angle shot hyperfractionation (FLASH) radiotherapy (RT) holds promise for improving treatment outcomes and reducing side effects but poses challenges in radiation delivery accuracy due to its ultra-high dose rates. This necessitates the development of novel imaging and verification technologies tailored to these conditions. PURPOSE: Our study explores the effectiveness of proton-induced acoustic imaging (PAI) in tracking the Bragg peak in three dimensions and in real time during FLASH proton irradiations, offering a method for volumetric beam imaging at both conventional and FLASH dose rates. METHODS: We developed a three-dimensional (3D) PAI technique using a 256-element ultrasound detector array for FLASH dose rate proton beams. In the study, we tested protoacoustic signal with a beamline of a FLASH-capable synchrocyclotron, setting the distal 90% of the Bragg peak around 35 mm away from the ultrasound array. This configuration allowed us to assess various total proton radiation doses, maintaining a consistent beam output of 21 pC/pulse. We also explored a spectrum of dose rates, from 15 Gy/s up to a FLASH rate of 48 Gy/s, by administering a set number of pulses. Furthermore, we implemented a three-dot scanning beam approach to observe the distinct movements of individual Bragg peaks using PAI. All these procedures utilized a proton beam energy of 180 MeV to achieve the maximum possible dose rate. RESULTS: Our findings indicate a strong linear relationship between protoacoustic signal amplitudes and delivered doses (R2 = 0.9997), with a consistent fit across different dose rates. The technique successfully provided 3D renderings of Bragg peaks at FLASH rates, validated through absolute Gamma index values. CONCLUSIONS: The protoacoustic system demonstrates effectiveness in 3D visualization and tracking of the Bragg peak during FLASH proton therapy, representing a notable advancement in proton therapy quality assurance. This method promises enhancements in protoacoustic image guidance and real-time dosimetry, paving the way for more accurate and effective treatments in ultra-high dose rate therapy environments.

Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.

BACKGROUND: Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individual patients. PURPOSE: In this study, we developed a patient-specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individual patients. METHODS: Our method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre-trained group model using patient-specific dataset derived from a novel data augmentation method to tune it into a patient-specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t-test with the p-value threshold of 0.05 compared with the results from the group model. RESULTS: The patient-specific model achieved an average RMSE of 0.014 ( p < 0.05 ${{{p}}}<{0.05}$ ), and an average SSIM of 0.981 ( p < 0.05 ${{{p}}}<{0.05}$ ), out-performing the group model. Qualitative results also demonstrated that our patient-specific approach acquired better imaging quality with more details reconstructed when comparing with the group model. Dose verification achieved an average RMSE of 0.011 ( p < 0.05 ${{{p}}}<{0.05}$ ), and an average SSIM of 0.995 ( p < 0.05 ${{{p}}}<{0.05}$ ). Gamma index evaluation demonstrated a high agreement (97.4% [ p < 0.05 ${{{p}}}<{0.05}$ ] and 97.9% [ p < 0.05 ${{{p}}}<{0.05}$ ] for 1%/3  and 1%/5 mm) between the predicted and the ground truth dose maps. Our approach approximately took 6 s to reconstruct PA images for each patient, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. CONCLUSIONS: Our method demonstrated the feasibility of achieving 3D high-precision PA-based dose verification using patient-specific deep-learning approaches, which can potentially be used to guide the treatment to mitigate the impact of range uncertainty and improve the precision. Further studies are needed to validate the clinical impact of the technique.

Resolution limits for radiation-induced acoustic imaging forin vivoradiation dosimetry.

Objective.Radiation-induced acoustic (RA) computed tomographic (RACT) imaging is being thoroughly explored for radiation dosimetry. It is essential to understand how key machine parameters like beam pulse, size, and energy deposition affect image quality in RACT. We investigate the intricate interplay of these parameters and how these factors influence dose map resolution in RACT.Approach.We first conduct an analytical assessment of time-domain RA signals and their corresponding frequency spectra for certain testcases, and computationally validate these analyses. Subsequently, we simulated a series of x-ray-based RACT (XACT) experiments and compared the simulations with experimental measurements.In-silicoreconstruction studies have also been conducted to demonstrate the resolution limits imposed by the temporal pulse profiles on RACT. XACT experiments were performed using clinical machines and the reconstructions were analyzed for resolution capabilities.Main results.Our paper establishes the theory for predicting the time- and frequency-domain behavior of RA signals. We illustrate that the frequency content of RA signal is not solely dependent on the spatial energy deposition characteristics but also on the temporal features of radiation. The same spatial energy deposition through a Gaussian pulse and a rectangular pulse of equal pulsewidths results in different frequency spectra of the RA signals. RA signals corresponding to the rectangular pulse exhibit more high-frequency content than their Gaussian pulse counterparts and hence provide better resolution in the reconstructions. XACT experiments with ∼3.2 us and ∼4 us rectangular radiation pulses were performed, and the reconstruction results were found to correlate well with thein-silicoresults.Significance.Here, we discuss the inherent resolution limits for RACT-based radiation dosimetric systems. While our study is relevant to the broader community engaged in research on photoacoustics, x-ray-acoustics, and proto/ionoacoustics, it holds particular significance for medical physics researchers aiming to set up RACT for dosimetry and radiography using clinical radiation machines.

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.

Ionic-resolution protoacoustic microscopy: A feasibility study.

Visualizing micro- and nano-scale biological entities requires high-resolution imaging and is conventionally achieved via optical microscopic techniques. Optical diffraction limits their resolution to ∼200 nm. This limit can be overcome by using ions with ∼1 MeV energy. Such ions penetrate through several micrometers in tissues, and their much shorter de Broglie wavelengths indicate that these ion beams can be focused to much shorter scales and hence can potentially facilitate higher resolution as compared to the optical techniques. Proton microscopy with ∼1 MeV protons has been shown to have reasonable inherent contrast between sub-cellular organelles. However, being a transmission-based modality, it is unsuitable for in vivo studies and cannot facilitate three-dimensional imaging from a single raster scan. Here, we propose proton-induced acoustic microscopy (PrAM), a technique based on pulsed proton irradiation and proton-induced acoustic signal collection. This technique is capable of label-free, super-resolution, 3D imaging with a single raster scan. Converting radiation energy into ultrasound enables PrAM with reflection mode detection, making it suitable for in vivo imaging and probing deeper than proton scanning transmission ion microscopy (STIM). Using a proton STIM image of HeLa cells, a coupled Monte Carlo+k-wave simulations-based feasibility study has been performed to demonstrate the capabilities of PrAM. We demonstrate that sub-50 nm lateral (depending upon the beam size and energy) and sub-micron axial resolution (based on acoustic detection bandwidth and proton beam pulse width) can be obtained using the proposed modality. By enabling visualization of biological phenomena at cellular and subcellular levels, this high-resolution microscopic technique enhances understanding of intricate cellular processes.

Simulation study of protoacoustics as a real-time in-line dosimetry tool for FLASH proton therapy.

BACKGROUND: Applying ultra-high dose rates to radiation therapy, otherwise known as FLASH, has been shown to be just as effective while sparing more normal tissue compared to conventional radiation therapy. However, there is a need for a dosimeter that is able to detect such high instantaneous dose, particularly in vivo. To fulfill this need, protoacoustics is introduced, which is an in vivo range verification method with submillimeter accuracy. PURPOSE: The purpose of this work is to demonstrate the feasibility of using protoacoustics as a method of in vivo real-time monitoring during FLASH proton therapy and investigating the resulting protoacoustic signal when dose per pulse and pulsewidth are varied through multiple simulation studies. METHODS: The dose distribution of a proton pencil beam was calculated through a Monte Carlo toolbox, TOPAS. Next, the k-Wave toolbox in MATLAB was used for performing protoacoustic simulations, where the initial proton dose deposition was inputted to model acoustic propagations, which were also used for reconstructions. Simulations involving the manipulation of the dose per pulse and pulsewidth were performed, and the temporal and spatial resolution for protoacoustic reconstructions were investigated as well. A 3D reconstruction was performed with a multiple beam spot profile to investigate the spatial resolution as well as determine the feasibility of 3D imaging with protoacoustics. RESULTS: Our results showed consistent linearity in the increasing dose-per-pulse, even up to rates considered for FLASH. The simulations and reconstructions were performed for a range of pulsewidths from 0.1 to 10 µs. The results show the characteristics of the proton beam after convolving the protoacoustic signal with the varying pulsewidths. 3D reconstruction was successfully performed with each beam being distinguishable using an 8 cm × 8 cm planar array. These simulation results show that measurements using protoacoustics has the potential for in vivo dosimetry in FLASH therapy during patient treatments in real time. CONCLUSION: Through this simulation study, the use of protoacoustics in FLASH therapy was verified and explored through observations of varying parameters, such as the dose per pulse and pulsewidth. 2D and 3D reconstructions were also completed. This study shows the significance of using protoacoustics and provides necessary information, which can further be explored in clinical settings.

Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography.

X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.

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.

3-D Protoacoustic Imaging Through a Planar Ultrasound Array: A Simulation Workflow.

Bragg peak range uncertainties are a persistent constraint in proton therapy. Pulsed proton beams generate protoacoustic emissions proportional to absorbed proton energy, thereby encoding dosimetry information in a detectable acoustic wave. Here, we seek to derive and model 3D protoacoustic imaging with an ultrasound array and examine the frequency characteristics of protoacoustic emissions. A formalism is presented through which protoacoustic signals can be characterized considering transducer bandwidth as well as pulse duration of the incident beam. We have also collected an experimental proton beam intensity signal from a Mevion S250 clinical machine to analyze our formalism. We also show that proton-acoustic image reconstruction is possible even when the noise amplitude is larger than the signal amplitude on individual transducers. We find that a 4µ s Gaussian proton pulse can generate a signal in the range of MHz as long as the spatial heating function has sufficiently high temperature gradients.

Hybrid-Supervised Deep Learning for Domain Transfer 3D Protoacoustic Image Reconstruction.

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 significantly impairs its accuracy for dose verification. In this study, we developed a deep learning method with a Recon- Enhance two-stage strategy for protoacoustic imaging to address the limited view issue. Specifically, in the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from radiofrequency 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. The results evaluated on a dataset of 126 prostate cancer patients achieved an average RMSE of 0.0292, and an average SSIM of 0.9618, significantly 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 seconds, demonstrating its feasibility for online 3D dose verification for prostate proton therapy.

Single-Pulse X-ray Acoustic Computed Tomographic Imaging for Precision Radiation Therapy.

Purpose: High-precision radiation therapy is crucial for cancer treatment. Currently, the delivered dose can only be verified via simulations with phantoms, and an in-tumor, online dose verification is still unavailable. An innovative detection method called x-ray-induced acoustic computed tomography (XACT) has recently shown the potential for imaging the delivered radiation dose within the tumor. Prior XACT imaging systems have required tens to hundreds of signal averages to achieve high-quality dose images within the patient, which reduces its real-time capability. Here, we demonstrate that XACT dose images can be reproduced from a single x-ray pulse (4 µs) with sub-mGy sensitivity from a clinical linear accelerator. Methods and Materials: By immersing an acoustic transducer in a homogeneous medium, it is possible to detect pressure waves generated by the pulsed radiation from a clinical linear accelerator. After rotating the collimator, signals of different angles are obtained to perform a tomographic reconstruction of the dose field. Using 2-stage amplification with further bandpass filtering increases the signal-to-noise ratio (SNR). Results: Acoustic peak SNR and voltage values were recorded for singular and dual-amplifying stages. The SNR for single-pulse mode was able to satisfy the Rose criterion, and the collected signals were able to reconstruct 2-dimensional images from the 2 homogeneous media. Conclusions: By overcoming the low SNR and requirement of signal averaging, single-pulse XACT imaging holds great potential for personalized dose monitoring from each individual pulse during radiation therapy.

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 radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, it requires measuring hundreds or even thousands of averages to achieve satisfactory signal-to-noise ratios (SNRs). 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 averages. The multi-dilation convolutions in the network allow for encoding and decoding signal features with varying temporal characteristics, making the network generalizable to signals from different radiation sources. The proposed method was evaluated using experimental data of X-ray-induced acoustic, protoacoustic, and electroacoustic signals, qualitatively and quantitatively. Results demonstrated the effectiveness and generalizability of GDI-CNN: for all the enrolled RA modalities, GDI-CNN achieved comparable SNRs to the fully-averaged signals using less than 2% of the averages, significantly reducing imaging dose and improving temporal resolution. The proposed deep learning framework is a general method for few-frame-averaged acoustic signal denoising, which significantly improves RA imaging's clinical utilities for low-dose imaging and real-time therapy monitoring.

Towards quantitative in vivo dosimetry using x-ray acoustic computed tomography.

BACKGROUND: Radiation dosimetry is essential for radiation therapy (RT) to ensure that radiation dose is accurately delivered to the tumor. Despite its wide use in clinical intervention, the delivered radiation dose can only be planned and verified via simulation. This makes precision radiotherapy challenging while in-line verification of the delivered dose is still absent in the clinic. X-ray-induced acoustic computed tomography (XACT) has recently been proposed as an imaging tool for in vivo dosimetry. PURPOSE: Most of the XACT studies focus on localizing the radiation beam. However, it has not been studied for its potential for quantitative dosimetry. The aim of this study was to investigate the feasibility of using XACT for quantitative in vivo dose reconstruction during radiotherapy. METHODS: Varian Eclipse system was used to generate simulated uniform and wedged 3D radiation field with a size of 4 cm × $ \times \ $ 4 cm. In order to use XACT for quantitative dosimetry measurements, we have deconvoluted the effects of both the x-ray pulse shape and the finite frequency response of the ultrasound detector. We developed a model-based image reconstruction algorithm to quantify radiation dose in vivo using XACT imaging, and universal back-projection (UBP) reconstruction is used as comparison. The reconstructed dose was calibrated before comparing it to the percent depth dose (PDD) profile. Structural similarity index matrix (SSIM) and root mean squared error (RMSE) are used for numeric evaluation. Experimental signals were acquired from 4 cm × $ \times \ $ 4 cm radiation field created by Linear Accelerator (LINAC) at depths of 6, 8, and 10 cm beneath the water surface. The acquired signals were processed before reconstruction to achieve accurate results. RESULTS: Applying model-based reconstruction algorithm with non-negative constraints successfully reconstructed accurate radiation dose in 3D simulation study. The reconstructed dose matches well with the PDD profile after calibration in experiments. The SSIMs between the model-based reconstructions and initial doses are over 85%, and the RMSEs of model-based reconstructions are eight times lower than the UBP reconstructions. We have also shown that XACT images can be displayed as pseudo-color maps of acoustic intensity, which correspond to different radiation doses in the clinic. CONCLUSION: Our results show that the XACT imaging by model-based reconstruction algorithm is considerably more accurate than the dose reconstructed by UBP algorithm. With proper calibration, XACT is potentially applicable to the clinic for quantitative in vivo dosimetry across a wide range of radiation modalities. In addition, XACT's capability of real-time, volumetric dose imaging seems well-suited for the emerging field of ultrahigh dose rate "FLASH" radiotherapy.

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.

Feasibility of photoacoustic-guided ultrasound treatment for port wine stains.

BACKGROUND AND OBJECTIVES: Port wine birthmark, also known as port wine stain (PWS) is a skin discoloration characterized by red/purple patches caused by vascular malformation. PWS is typically treated by using lasers to destroy abnormal blood vessels. The laser heating facilitates selective photothermolysis of the vessels and attenuates quickly in the tissue due to high optical scattering. Therefore, residual abnormal capillaries deep in the tissue survive and often lead to the resurgence of PWS. Ultrasound (US) has also been proposed to treat PWS, however, it is nonselective with respect to the vasculature but penetrates deeper into the tissue. We aim to study the feasibility of a hybrid PWS treatment modality combining the advantages of both modalities. MATERIALS AND METHODS: In this manuscript, we propose a photoacoustic (PA) guided US focusing methodology for PWS treatment which combines the optical contrast-based selectivity with US penetration to focus the US energy onto the vasculature. The PA signals collected by the transducers, when time-reversed, amplified, and transmitted, converge onto the PWS, thus minimally affecting the neighboring tissue. We performed two- and three-dimensional simulations that mimic realistic transducers and medium properties in this proof of concept study. RESULTS: The time-reversed PA signals when transmitted from the transducers converged onto the vasculature, as expected, thus reducing the heating of the neighboring tissue. We observed that while the US focus is indeed affected due to experimental factors such as limited-view, large detector separation and finite detection bandwidth, and so forth, the US did focus completely or partially onto the vasculature demonstrating the feasibility of the proposed methodology. CONCLUSION: The results demonstrate the potential of the proposed methodology for PWS treatment. This treatment method can destroy the deeper capillaries while minimally heating the neighboring tissue, thus reducing the chances of the resurgence of PWS and as well as cosmetic scarring.

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

Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-timein vivo3D dose verification. However, its dosimetry accuracy is limited due to the limited-angle view of the ultrasound transducer. In this study, we developed a deep learning-based method to address the limited-view issue in the PA reconstruction. A deep cascaded convolutional neural network (DC-CNN) was proposed to reconstruct 3D high-quality radiation-induced pressures using PA signals detected by a matrix array, and then derive precise 3D dosimetry from pressures for dose verification in proton therapy. To validate its performance, we collected 81 prostate cancer patients' proton therapy treatment plans. Dose was calculated using the commercial software RayStation and was normalized to the maximum dose. The PA simulation was performed using the open-source k-wave package. A matrix ultrasound array with 64 × 64 sensors and 500 kHz central frequency was simulated near the perineum to acquire radiofrequency (RF) signals during dose delivery. For realistic acoustic simulations, tissue heterogeneity and attenuation were considered, and Gaussian white noise was added to the acquired RF signals. The proposed DC-CNN was trained on 204 samples from 69 patients and tested on 26 samples from 12 other patients. Predicted 3D pressures and dose maps were compared against the ground truth qualitatively and quantitatively using root-mean-squared-error (RMSE), gamma-index (GI), and dice coefficient of isodose lines. Results demonstrated that the proposed method considerably improved the limited-view PA image quality, reconstructing pressures with clear and accurate structures and deriving doses with a high agreement with the ground truth. Quantitatively, the pressure accuracy achieved an RMSE of 0.061, and the dose accuracy achieved an RMSE of 0.044, GI (3%/3 mm) of 93.71%, and 90%-isodose line dice of 0.922. The proposed method demonstrates the feasibility of achieving high-quality quantitative 3D dosimetry in PA imaging using a matrix array, which potentially enables the online 3D dose verification for prostate proton therapy.

The feasibility study of XACT imaging for characterizing osteoporosis.

BACKGROUND: Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and the deterioration in bone microarchitecture, which might be related to age and space travel. An unmet need exists for the development of novel imaging technologies to characterize osteoporosis. PURPOSE: The purpose of our study is to investigate the feasibility of X-ray-induced acoustic computed tomography (XACT) imaging for osteoporosis detection. METHODS: An in-house simulation workflow was developed to assess the ability of XACT for osteoporosis detection. To evaluate this simulation workflow, a three-dimensional digital bone phantom for XACT imaging was created by a series of two-dimensional micro-computed tomography (micro-CT) slices of normal and osteoporotic bones in mice. In XACT imaging, the initial acoustic pressure rise caused by the X-ray induce acoustic (XA) effect is proportional to bone density. First, region growing was deployed for image segmentation of different materials inside the bone. Then k-wave simulations were deployed to model XA wave propagation, attenuation, and detection. Finally, the time-varying pressure signals detected at each transducer location were used to reconstruct the XACT image with a time-reversal reconstruction algorithm. RESULTS: Through the simulated XACT images, cortical porosity has been calculated, and XA signal spectra slopes have been analyzed for the detection of osteoporosis. The results have demonstrated that osteoporotic bones have lower bone mineral density and higher spectra slopes. These findings from XACT images were in good agreement with porosity calculation from micro-CT images. CONCLUSION: This work explores the feasibility of using XACT imaging as a new imaging tool for Osteoporosis detection. Considering that acoustic signals are generated by X-ray absorption, XACT imaging can be combined with traditional X-ray imaging that holds potential for clinical management of osteoporosis and other bone diseases.

Ring artifacts removal in X-ray-induced acoustic computed tomography.

X-ray-induced acoustic computed tomography (XACT) is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission. It facilitates imaging from a single projection X-ray illumination, thus reducing the radiation exposure and improving imaging speed. Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images, which compromises the image quality. We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their efficacy on numerical and experimental measurements. The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

X-ray-induced acoustic computed tomography (XACT) imaging with single-shot nanosecond x-ray.

X-ray-induced acoustic computed tomography (XACT) has emerged as a promising imaging modality with broad applications in both biomedicine and nondestructive testing. The previous XACT imaging systems require thousands of averages to achieve reasonable images. Here, we report the experimental demonstration of single-shot XACT imaging of a metal object using a single-shot 50 ns x-ray pulse. A two-stage dedicated amplification and a 128-channel parallel data acquisition configuration were introduced for XACT imaging to enable sufficient acoustic signal amplification and maintain an overall low noise level for single-shot XACT imaging. Details of the system design are presented; the improved signal-to-noise ratio (>23 dB) and image reconstruction have been demonstrated with a ring ultrasound transducer array imaging system. The study paves the way for realizing real-time XACT imaging and its potential applications in image-guided intervention.