SignificancePhotoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms.AimIn this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method.ApproachWe leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform.ResultsNotably, our framework can achieve an effective rate of ∼871 times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub.ConclusionsOur approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.
SignificanceVarious peripheral vascular diseases (PVD) in extremities, such as arterial atherosclerosis or venous occlusion in arm or legs, are a serious global health threat. Noninvasive vascular imaging is of great value for both diagnosis and assessment of PVD.ApproachBy scanning a one-dimensional non-focusing linear array, an equivalent large two-dimensional (2D) matrix array with hundreds of thousands or more ultrasound elements is formed, thereby achieving a wide signal reception angle as well as large imaging area for three-dimensional (3D) imaging of peripheral extremities.AimTo provide a feasible bedside and noninvasive imaging method for vascular imaging in extremities.ResultsOur system can achieve high-quality photoacoustic (PA) peripheral vessel imaging. The 3D subcutaneous vascular imaging results of the palms and arms of healthy volunteers demonstrate the superior performance of the system.ConclusionsThis work proposes a clinically oriented PA 3D subcutaneous vascular imaging system for human extremities. The system employs a synthetic matrix array via scanning a one-dimensional non-focusing linear probe, providing noninvasive, high-resolution, and high-contrast images of human extremities. It has potential application value in the diagnosis and monitoring of vascular diseases.
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