Algebraic determination of back-projection operators for optoacoustic tomography

The influences of finite aperture size in photoacoustic computed tomography

In photoacoustic computed tomography (PACT), the "finite aperture effect" is often characterized as a tangential resolution that increases proportionally with the distance from the rotation center. However, this conclusion is based on the inaccurate point-detector assumption used in image reconstruction. In this study, we appropriately modeled the finite size of the acoustic detector in the back-projection (BP) based image reconstruction to improve the accuracy of the time delay calculation and systematically investigated its effects. Our results showed that the main effect of the finite aperture size is the creation of a limited high-quality imaging region (HQIR) around the scanning center, due to the directional sensitivity of the detector. We also demonstrated that the "finite aperture effect" can reduce the optimal number of detectors required for spatial anti-aliasing. These new findings provide novel perspectives for optimizing PACT systems and corresponding reconstruction methods.

Learned regularization for image reconstruction in sparse-view photoacoustic tomography

Constrained data acquisitions, such as sparse view measurements, are sometimes used in photoacoustic computed tomography (PACT) to accelerate data acquisition. However, it is challenging to reconstruct high-quality images under such scenarios. Iterative image reconstruction with regularization is a typical choice to solve this problem but it suffers from image artifacts. In this paper, we present a learned regularization method to suppress image artifacts in model-based iterative reconstruction in sparse view PACT. A lightweight dual-path network is designed to learn regularization features from both the data and the image domains. The network is trained and tested on both simulation and in vivo datasets and compared with other methods such as Tikhonov regularization, total variation regularization, and a U-Net based post-processing approach. Results show that although the learned regularization network possesses a size of only 0.15% of a U-Net, it outperforms other methods and converges after as few as five iterations, which takes less than one-third of the time of conventional methods. Moreover, the proposed reconstruction method incorporates the physical model of photoacoustic imaging and explores structural information from training datasets. The integration of deep learning with a physical model can potentially achieve improved imaging performance in practice.

Approximate back-projection method for improving lateral resolution in circular-scanning-based photoacoustic tomography

PURPOSE

In circular-scanning-based photoacoustic tomography (PAT), the effect of finite transducer aperture has not been effectively resolved. The goal of this paper is to propose a practical reconstruction method that accounts for the finite transducer aperture to improve the lateral resolution.

METHODS

We for the first time propose to calculate the spatial-temporal response (STR) of the employed finite-sized transducer in a forward model, and then compensate the time delay and the directional sensitivity of the transducer in the framework of the back-projection method. Both simulation and phantom experiments were carried out to evaluate the lateral resolution improvement with the proposed method. The performance of this new method for imaging complicated targets was also assessed by calculating the mean image gradient.

RESULTS

Simulation results showed that with this new method the lateral resolution for off-center targets can be as good as that for the center targets. Phantom experimental results showed that this new method can improve the lateral resolution more than two times for a point target about 5 mm far from the rotation center. Phantom experimental results also showed that many blurred fine structures of a piece of leaf veins at the off-center regions were well restored with the new method, and the mean image gradient improved about 1.3 times.

CONCLUSION

The proposed new method can effectively account for the effect of finite transducer aperture for circular-scanning-based PAT in homogenous acoustic media. This new method also features its robustness and computational efficiency, so that it is a worthy replacement to the conventional back-projection algorithm in circular-scanning-based PAT. This new method can be of great importance to the design of circular-scanning or spherical-scanning-based PAT systems.

Simulation of x-ray-induced acoustic imaging for absolute dosimetry: Accuracy of image reconstruction methods

PURPOSE

Three-dimensional in-vivo dose verification is one of the standing challenges in radiation therapy. X-ray-induced acoustic tomography has recently been proposed as an imaging method for use in in-vivo dosimetry. The aim of this study was to investigate the accuracy of reconstructing three-dimensional (3D) absolute dose using x-ray-induced acoustic tomography. We performed this investigation using two different tomographic dose reconstruction techniques.

METHODS

Two examples of 3D dose reconstruction techniques for x-ray acoustic imaging are investigated. Dose distributions are calculated for varying field sizes using a clinical treatment planning system. The induced acoustic pressure waves which are generated by the increase in temperature due to the absorption of pulsed MV x-rays are simulated using an advanced numerical modeling package for acoustic wave propagation in the time domain. Two imaging techniques, back projection and iterative time reversal, are used to reconstruct the 3D dose distribution in a water phantom with open fields. Image analysis is performed and reconstructed depth dose curves from x-ray acoustic imaging are compared to the depth dose curves calculated from the treatment planning system. Calculated field sizes from the reconstructed dose profiles by back projection and time reversal are compared to the planned field size to determine their accuracy. The iterative time reversal imaging technique is also used to reconstruct dose in an example clinical dose distribution. Image analysis of this clinical test case is performed using the gamma passing rate. In addition, gamma passing rates are used to validate the stopping criteria in the iterative time reversal method.

RESULTS

Water phantom simulations showed that back projection does not adequately reconstruct the shape and intensity of the depth dose. When compared to the depth of maximum dose calculated by a treatment planning system, the maximum dose depth by back projection is shifted deeper by 55 and 75 mm for 4 × 4 cm and 10 × 10 cm field sizes, respectively. The reconstructed depth dose by iterative time reversal accurately agrees with the planned depth dose for a 4 × 4 cm field size and is shifted deeper by 12 mm for the 10 × 10 cm field size. When reconstructing field sizes, the back projection method leads to 18% and 35% larger sizes for the 4 × 4 cm and 10 × 10 cm fields, respectively, whereas the iterative time reversal method reconstructs both field sizes with < 2% error. For the clinical dose distribution, we were able to reconstruct the dose delivered by a 1 degree sub-arc with a good accuracy. The reconstructed and planned doses were compared using gamma analysis, with> 96% gamma passing rate at 3%/2 mm.

CONCLUSIONS

Our results show that the 3D x-ray acoustic reconstructed dose by iterative time reversal is considerably more accurate than the dose reconstructed by back projection. Iterative time reversal imaging has a potential for use in 3D absolute dosimetry.

Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L ₁-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L ₁ scheme in enhancing image contrast.

The Impulse Response of Negatively Focused Spherical Ultrasound Detectors and Its Effect on Tomographic Optoacoustic Reconstruction

In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulas exist for optoacoustic image reconstruction with negatively focused detectors, image reconstruction in such cases is based on using the virtual-detector approximation, in which it is assumed that the response of the negatively focused detector is identical, up to a constant time delay, to that of a point-like detector positioned in the detector's center of curvature. In this paper, we analyze the response of negatively focused spherical ultrasound detectors in three dimensions and demonstrate how their properties affect the optoacoustic reconstruction. Our analysis sheds new light on commonly reported experimental reconstruction artifacts in optoacoustic systems that employ negatively focused detectors. Based on our analysis, we introduce a simple correction to the virtual-detector approximation that significantly enhances image contrast and reduces artifacts.