Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation

A crucial limitation of current high-resolution 3D photoacoustic tomography (PAT) devices that employ sequential scanning is their long acquisition time. In previous work, we demonstrated how to use compressed sensing techniques to improve upon this: images with good spatial resolution and contrast can be obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning systems if sparsity-constrained image reconstruction techniques such as total variation regularization are used. Now, we show how a further increase of image quality can be achieved for imaging dynamic processes in living tissue (4D PAT). The key idea is to exploit the additional temporal redundancy of the data by coupling the previously used spatial image reconstruction models with sparsity-constrained motion estimation models. While simulated data from a two-dimensional numerical phantom will be used to illustrate the main properties of this recently developed joint-image-reconstruction-and-motion-estimation framework, measured data from a dynamic experimental phantom will also be used to demonstrate their potential for challenging, large-scale, real-world, three-dimensional scenarios. The latter only becomes feasible if a carefully designed combination of tailored optimization schemes is employed, which we describe and examine in more detail

Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method

Toward Real-Time Giga-Voxel Optoacoustic/Photoacoustic Microscopy: GPU-Accelerated Fourier Reconstruction with Quasi-3D Implementation

We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) employing truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time as compared to CPU-based SAFT. We provide an empirical model that can be used to predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system

Automatic Search for Photoacoustic Marker Using Automated Transrectal Ultrasound

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [19]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53 degree average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29 degree and 0.48 degree average errors after 28 data acquisitions.Comment: 13 pages, 9 figure

Image Reconstruction of the Speed of Sound and Initial Pressure Distributions in Ultrasound Computed Tomography and Photoacoustic Computed Tomography

Ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT) are two emerging imaging modalities that have a wide range of potential applications from pre-clinical small animal imaging to cancer screening in human subjects. USCT is typically employed to measure acoustic contrasts, including the speed of sound (SOS) distribution, while PACT typically measures optical contrasts or some related quantity such as the initial pressure distribution. Their complementary contrasts and similar implementations make USCT and PACT a natural fit for a hybrid imaging system. Still, much work remains to realize this promise. First, USCT image reconstruction methods based on the acoustic wave equation, known as waveform inversion methods, are computationally burdensome, limiting their widespread use. Instead, image reconstruction methods based on geometric acoustics are often employed. These methods do not model higher-order diffraction effects and consequentially have poor resolution. In this dissertation, use of a novel stochastic optimization method, which overcomes much of the computational burden of waveform inversion, is proposed. Second, most traditional PACT image reconstruction algorithms assume a constant SOS distribution. For many biological applications, this is a poor assumption that can result in reduced resolution, reduced contrast, and an increase in the number of imaging artifacts. More recent image reconstruction algorithms can compensate for a known heterogeneous SOS distribution; however, in practice, the SOS distribution is not known. Further, in general, the joint reconstruction (JR) of the SOS and initial pressure distributions from PACT measurements is unstable. Two methods are proposed to overcome this problem. In the first, a parameterized JR method is employed. Under this approach, the SOS distribution is assumed to have a known low-dimensional representation. By constraining the form of the SOS distribution, the JR problem can be made more stable. In the second method, few-view USCT measurements are added to the PACT data, and the initial pressure and SOS distributions are jointly estimated from the combined measurements. This approach effectively exploits acoustic information present in the PACT data, allowing both the initial pressure and SOS distributions to be more accurately reconstructed