A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic computed tomography.

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SIAM Journal on Imaging Sciences Pub Date : 2017-01-01 Epub Date: 2017-11-16 DOI:10.1137/16M1107619
Kenji Mitsuhashi, Joemini Poudel, Thomas P Matthews, Alejandro Garcia-Uribe, Lihong V Wang, Mark A Anastasio
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引用次数: 25

Abstract

Photoacoustic computed tomography (PACT) is an emerging imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to an inverse source problem in which the initial pressure distribution is recovered from measurements of the radiated wavefield. A major challenge in transcranial PACT brain imaging is compensation for aberrations in the measured data due to the presence of the skull. Ultrasonic waves undergo absorption, scattering and longitudinal-to-shear wave mode conversion as they propagate through the skull. To properly account for these effects, a wave-equation-based inversion method should be employed that can model the heterogeneous elastic properties of the skull. In this work, a forward model based on a finite-difference time-domain discretization of the three-dimensional elastic wave equation is established and a procedure for computing the corresponding adjoint of the forward operator is presented. Massively parallel implementations of these operators employing multiple graphics processing units (GPUs) are also developed. The developed numerical framework is validated and investigated in computer19 simulation and experimental phantom studies whose designs are motivated by transcranial PACT applications.

Abstract Image

Abstract Image

Abstract Image

基于弹性波动方程的前向伴随算子对在经颅光声计算机断层扫描中的应用。
光声计算机断层扫描(PACT)是一种新兴的成像方式,它利用光学对比和超声检测原理来形成光声诱导的组织内初始压力分布的图像。PACT重建问题对应于一个逆源问题,其中从辐射波场的测量中恢复初始压力分布。经颅PACT脑成像的一个主要挑战是补偿由于颅骨存在而导致的测量数据畸变。超声波在颅骨中传播时经历吸收、散射和纵-剪切波模式转换。为了适当地考虑这些影响,应该采用基于波动方程的反演方法来模拟颅骨的非均匀弹性特性。本文建立了基于时域有限差分离散化的三维弹性波动方程正演模型,并给出了正演算子伴随算子的计算方法。还开发了使用多个图形处理单元(gpu)的这些运算符的大规模并行实现。开发的数值框架在计算机模拟和实验幻影研究中得到验证和研究,其设计是由经颅PACT应用驱动的。
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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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