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arXiv 2510.03431physics.med-phcs.AIeess.SP

虚拟成像框架在研究基于深度学习的三维定量光声计算机断层扫描重建方法中的应用

Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography

organization= Department of Electrical \& Computer Engineering, University of Illinois Urbana-Champaign , postcode= 61801 , state= IL , country= USA;organization= Department of Bioengineering, University of Illinois Urbana-Champaign , postcode= 61801 , state= IL , country= USA;organization= Oden Institute for Computational Engineering;Sciences, The University of Texas at Austin , postcode= 78712 , state= TX , country= USA;organization= Department of Biomedical Engineering, The University of Texas at Austin , postcode= 78712 , state= TX , country= USA

Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark A. Anastasio

AI总结:

本研究首次采用高仿真虚拟成像测试平台,评估一种用于乳腺成像的三维基于学习的定量光声计算机断层扫描重建方法,分析受试者差异、测量噪声等因素对方法的影响,明确其优劣势。

摘要:

定量光声计算机断层扫描(qPACT)是一种用于估计血氧饱和度等生理参数的极具前景的成像模态。然而,由于计算需求高、建模难度大以及实验存在不确定性,开发鲁棒的qPACT重建方法仍然颇具挑战。已有研究提出基于学习的方法来解决这些问题,但这些方法在很大程度上仍未得到验证。虚拟成像(VI)研究对于在开展可控性较低的体模或体内研究之前,在开发早期验证此类方法至关重要。有效的VI研究必须采用随机生成的数值体模集合,这些体模需能准确反映相关的解剖结构和生理特征。然而,以往大多数针对qPACT的VI研究都依赖于过于简化的体模。在本研究中,我们首次采用了一个高仿真的VI测试平台,来评估一种代表性的用于乳腺成像的三维基于学习的qPACT重建方法。研究从受试者个体差异以及测量噪声、声学畸变等物理因素维度对该方法进行了评估,为明确其优势与局限性提供了见解。

英文摘要:

Quantitative photoacoustic computed tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing robust qPACT reconstruction methods remains challenging due to computational demands, modeling difficulties, and experimental uncertainties. Learning-based methods have been proposed to address these issues but remain largely unvalidated. Virtual imaging (VI) studies are essential for validating such methods early in development, before proceeding to less-controlled phantom or in vivo studies. Effective VI studies must employ ensembles of stochastically generated numerical phantoms that accurately reflect relevant anatomy and physiology. Yet, most prior VI studies for qPACT relied on overly simplified phantoms. In this work, a realistic VI testbed is employed for the first time to assess a representative 3D learning-based qPACT reconstruction method for breast imaging. The method is evaluated across subject variability and physical factors such as measurement noise and acoustic aberrations, offering insights into its strengths and limitations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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