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

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 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.

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.

Low energy X-ray dosimeter based on LYSO:Ce fluorescent powder

Cerium-doped lutetium yttrium orthosilicate (LYSO:Ce) powder has been synthesized by the co-precipitation method. The influence of the Ce3+ doping concentration on the lattice structure and luminescence characteristics of LYSO:Ce powder was investigated by X-ray diffraction (XRD) and photoluminescence (PL). The XRD measurement indicates that the lattice structure of LYSO:Ce powder was not changed by doping ions. PL results show that LYSO:Ce powder has better luminescence performance when the Ce doping concentration is 0.3 mol%. In addition, the fluorescence lifetime of the samples was measured, and the results show that LYSO:Ce has a short decay time. The radiation dosimeter was prepared by LYSO:Ce powder with a Ce doping concentration of 0.3 mol%. Radioluminescence properties of the radiation dosimeter also were studied under X-ray irradiation at doses from 0.03 to 0.76 Gy, with dose rate from 0.09 to 2.284 Gy/min. The results show that the dosimeter has a certain linear relationship response and stability. The radiation responses of the dosimeter at different energies were obtained under X-ray irradiation with X-ray tube voltages ranging from 20 to 80 kV. The results show that the dosimeter has a certain linear relationship response in the low energy range of radiotherapy. These results indicate the potential application of LYSO:Ce powder dosimeters in remote radiotherapy and online radiation monitoring.

Absolute Grüneisen parameter measurement in deep tissue based on X-ray-induced acoustic computed tomography

The Grüneisen parameter is a primary parameter of the initial sound pressure signal in the photoacoustic effect, which can provide unique biological information and is related to the temperature change information of an object. The accurate measurement of this parameter is of great significance in biomedical research. Combining X-ray-induced acoustic tomography and conventional X-ray computed tomography, we proposed a method to obtain the absolute Grüneisen parameter. The theory development, numerical simulation, and biomedical application scenarios are discussed. The results reveal that our method not only can determine the Grüneisen parameter but can also obtain the body internal temperature distribution, presenting its potential in the diagnosis of a broad range of diseases.

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.