3-D X-Ray-Induced Acoustic Computed Tomography With a Spherical Array: A Simulation Study on Bone Imaging

X-ray free-electron laser induced acoustic microscopy (XFELAM)

The X-ray free-electron laser (XFEL) has remarkably advanced X-ray imaging technology and enabled important scientific achievements. The XFEL's extremely high power, short pulse width, low emittance, and high coherence make possible such diverse imaging techniques as absorption/emission spectroscopy, diffraction imaging, and scattering imaging. Here, we demonstrate a novel XFEL-based imaging modality that uses the X-ray induced acoustic (XA) effect, which we call X-ray free-electron laser induced acoustic microscopy (XFELAM). Initially, we verified the XA effect by detecting XA signals from various materials, then we validated the experimental results with simulation outcomes. Next, in resolution experiments, we successfully imaged a patterned tungsten target with drilled various-sized circles at a spatial resolution of 7.8 ± 5.1 µm, which is the first micron-scale resolution achieved by XA imaging. Our results suggest that the novel XFELAM can expand the usability of XFEL in various areas of fundamental scientific research.

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.

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.

In situ x-ray-induced acoustic computed tomography with a contrast agent: a proof of concept

X-ray-induced acoustic computed tomography (XACT) has shown great potential as a hybrid imaging modality for real-time non-invasive x-ray dosimetry and low-dose three-dimensional (3D) imaging. While promising, one drawback of the XACT system is the underlying low signal-to-noise ratio (SNR), limiting its in vivo clinical use. In this Letter, we propose the first use of a conventional x-ray computed tomography contrast agent, Gastrografin, for improving the SNR of in situ XACT imaging. We obtained 3D volumetric XACT images of a mouse's stomach with orally injected Gastrografin establishing the proposal's feasibility. Thus, we believe, in the future, our proposed technique will allow in vivo imaging and expand or complement conventional x-ray modalities, such as radiotherapy and accelerators.

Reconstruction Algorithm-Based Ultrasonic and Spiral CT Images in Evaluating the Effects of Dexmedetomidine Anesthesia for Acute Abdomen

OBJECTIVE

The study focused on the application value of iteration reconstruction algorithm-based ultrasound and spiral computed tomography (CT) examinations, and the safety of dexmedetomidine anesthesia in acute abdominal surgery.

METHODS

80 cases having the acute abdomen surgery were selected as the research subjects. They were divided into group A (40 cases) and group B (40 cases) according to the anesthetic drugs used in the later period. The experimental group was injected with propofol, remifentanil, and atracurium combined with dexmedetomidine; the control group was injected with propofol, remifentanil, and atracurium only. After the operation, the patient was for observed for the pain, agitation, adverse reactions, heart rate (HR), and blood pressure. All patients received ultrasound and spiral CT examinations, and based on the characteristics of the back-projection algorithm, an accelerated algorithm was established and used to process the image, and according to which, the patient's condition and curative effects were evaluated.

RESULTS

After image reconstruction, the ultrasound and spiral CT images were clearer with less noise and more prominent lesions than before reconstruction. Before image reconstruction, the accuracy rates of ultrasound and spiral CT in diagnosing acute abdomen were 92.3% and 91.1%, respectively. After reconstruction, the corresponding numbers were 96.3% and 98.1%, respectively. After reconstruction, the accuracy of the two methods in diagnosing acute abdomen was significantly improved compared with that before reconstruction, and the difference was statistically significant (P < 0.05). The Ramsay score of the experimental group was significantly higher than that of the control group at each time period, P < 0.05; the agitation score and visual analogue scale (VAS) score of the experimental group were significantly lower than the control group at each time period after waking up, P < 0.05.

CONCLUSION

Reconstruction algorithm-based ultrasound and spiral CT images have high application value in the diagnosis of patients with acute abdomen, and dexmedetomidine has good safety in anesthesia surgery.

Model-Based X-Ray-Induced Acoustic Computed Tomography

X-ray-induced acoustic computed tomography (XACT) provides X-ray absorption-based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR)-based minimization scheme and a noniterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this article. The proposed algorithms have been numerically validated and then used to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection (BP) algorithm produces limited-view and noisy artifacts in the region of interest (ROI), model-based LSQR minimization overcomes these issues. The model-based algorithms also reduce the ring artifacts caused due to the nonuniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the ROI. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard BP. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited-view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable), and hence pave the way for future clinical translation.

A simulation study of ionizing radiation acoustic imaging (iRAI) as a real-time dosimetric technique for ultra-high dose rate radiotherapy (UHDR-RT)

PURPOSE

Electron-based ultra-high dose rate radiation therapy (UHDR-RT), also known as Flash-RT, has shown the ability to improve the therapeutic index in comparison to conventional radiotherapy (CONV-RT) through increased sparing of normal tissue. However, the extremely high dose rates in UHDR-RT have raised the need for accurate real-time dosimetry tools. This work aims to demonstrate the potential of the emerging technology of Ionized Radiation Acoustic Imaging (iRAI) through simulation studies and investigate its characteristics as a promising relative in vivo dosimetric tool for UHDR-RT.

METHODS

The detection of induced acoustic waves following a single UHDR pulse of a modified 6 MeV 21EX Varian Clinac in a uniform porcine gelatin phantom that is brain-tissue equivalent was simulated for an ideal ultrasound transducer. The full 3D dose distributions in the phantom for a 1 × 1 cm2 field were simulated using EGSnrc (BEAMnrc∖DOSXYZnrc) Monte Carlo (MC) codes. The relative dosimetry simulations were verified with dose experimental measurements using Gafchromic films. The spatial dose distribution was converted into an initial pressure source spatial distribution using the medium-dependent dose-pressure relation. The MATLAB-based toolbox k-Wave was then used to model the propagation of acoustic waves through the phantom and perform time-reversal (TR)-based imaging reconstruction. The effect of the various linear accelerator (linac) operating parameters, including linac pulse duration and pulse repetition rate (frequency), were investigated as well.

RESULTS

The MC dose simulation results agreed with the film measurement results, specifically at the central beam region up to 80% dose within approximately 5% relative error for the central profile region and a local relative error of <6% for percentage dose depth. IRAI-based FWHM of the radiation beam was within approximately 3 mm relative to the MC-simulated beam FWHM at the beam entrance. The real-time pressure signal change agreed with the dose changes proving the capability of the iRAI for predicting the beam position. IRAI was tested through 3D simulations of its response to be based on the temporal changes in the linac operating parameters on a dose per pulse basis as expected theoretically from the pressure-dose proportionality. The pressure signal amplitude obtained through 2D simulations was proportional to the dose per pulse. The instantaneous pressure signal amplitude decreases as the linac pulse duration increases, as predicted from the pressure wave generation equations, such that the shorter the linac pulse the higher the signal and the better the temporal (spatial) resolutions of iRAI. The effect of the longer linac pulse duration on the spatial resolution of the 3D constructed iRAI images was corrected for linac pulse deconvolution. This correction has improved the passing rate of the 1%/1 mm gamma test criteria, between the pressure-constructed and dosimetric beam characteristics, to as high as 98%.

CONCLUSIONS

A full simulation workflow was developed for testing the effectiveness of iRAI as a promising relative dosimetry tool for UHDR-RT radiation therapy. IRAI has shown the advantage of 3D dose mapping through the dose signal linearity and, hence, has the potential to be a useful dosimeter at depth dose measurement and beam localization and, hence, potentially for in vivo dosimetry in UHDR-RT.

X-Ray-Induced Acoustic Computed Tomography (XACT): Initial Experiment on Bone Sample

X-ray-induced acoustic computed tomography (XACT) is a unique hybrid imaging modality that combines high X-ray absorption contrast with high ultrasonic resolution. X-ray radiography and computerized tomography (CT) are currently the gold standards for 2-D and 3-D imaging of skeletal tissues though there are important properties of bone, such as elasticity and speed of sound (SOS), that these techniques cannot measure. Ultrasound is capable of measuring such properties though current clinical ultrasound scanners cannot be used to image the interior morphology of bones because they fail to address the complicated physics involved for exact image reconstruction; bone is heterogeneous and composed of layers of both cortical and trabecular bone, which violates assumptions in conventional ultrasound imaging of uniform SOS. XACT, in conjunction with the time-reversal algorithm, is capable of generating precise reconstructions, and by combining elements of both X-ray and ultrasound imaging, XACT is potentially capable of obtaining more information than any single of these techniques at low radiation dose. This article highlights X-ray-induced acoustic detection through linear scanning of an ultrasound transducer and the time-reversal algorithm to produce the first-ever XACT image of a bone sample. The results of this study should prove to enhance the potential of XACT imaging in the evaluation of bone diseases for future clinical use.

Synchrotron X-ray induced acoustic imaging

X-ray induced acoustic imaging (XAI) is an emerging biomedical imaging technique that can visualize X-ray absorption contrast at ultrasound resolution with less ionizing radiation exposure than conventional X-ray computed tomography. So far, medical linear accelerators or industrial portable X-ray tubes have been explored as X-ray excitation sources for XAI. Here, we demonstrate the first feasible synchrotron XAI (sXAI). The synchrotron generates X-rays, with a dominant energy of 4 to 30 keV, a pulse-width of 30 ps, a pulse-repetition period of 2 ns, and a bunch-repetition period of 940 ns. The X-ray induced acoustic (XA) signals are processed in the Fourier domain by matching the signal frequency with the bunch-repetition frequency. We successfully obtained two-dimensional XA images of various lead targets. This novel sXAI tool could complement conventional synchrotron applications.