Parameterized joint reconstruction of the initial pressure and sound speed distributions for photoacoustic computed tomography.

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SIAM Journal on Imaging Sciences Pub Date : 2018-01-01 Epub Date: 2018-06-05 DOI:10.1137/17M1153649
Thomas P Matthews, Joemini Poudel, Lei Li, Lihong V Wang, Mark A Anastasio
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引用次数: 0

Abstract

Accurate estimation of the initial pressure distribution in photoacoustic computed tomography (PACT) depends on knowledge of the sound speed distribution. However, the sound speed distribution is typically unknown. Further, the initial pressure and sound speed distributions cannot both, in general, be stably recovered from PACT measurements alone. In this work, a joint reconstruction (JR) method for the initial pressure distribution and a low-dimensional parameterized model of the sound speed distribution is proposed. By employing a priori information about the structure of the sound speed distribution, both the initial pressure and sound speed can be accurately recovered. The JR problem is solved by use of a proximal optimization method that allows constraints and non-smooth regularization functions for the initial pressure distribution. The gradients of the cost function with respect to the initial pressure and sound speed distributions are calculated by use of an adjoint state method that has the same per-iteration computational cost as calculating the gradient with respect to the initial pressure distribution alone. This approach is evaluated through 2D computer-simulation studies for a small animal imaging model and by application to experimental in vivo measurements of a mouse.

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用于光声计算机断层扫描的初始压力和声速分布的参数化联合重建。
光声计算机断层扫描(PACT)中初始压力分布的精确估计取决于声速分布的知识。然而,声速分布通常是未知的。此外,初始压力和声速分布通常不能单独从PACT测量中稳定地恢复。在这项工作中,提出了一种初始压力分布的联合重建(JR)方法和声速分布的低维参数化模型。通过采用关于声速分布的结构的先验信息,可以准确地恢复初始压力和声速。JR问题通过使用近似优化方法来解决,该方法允许初始压力分布的约束和非光滑正则化函数。成本函数相对于初始压力和声速分布的梯度是通过使用伴随状态方法来计算的,该方法具有与单独计算相对于初始压力分布的梯度相同的每次迭代计算成本。该方法通过小动物成像模型的2D计算机模拟研究以及应用于小鼠的体内实验测量进行了评估。
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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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