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TGV-regularized inversion of the Radon transform for photoacoustic tomography

机译:用于光声层析成像的Radon变换的TGV规则反演

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摘要

We propose and study a reconstruction method for photoacoustic tomography (PAT) based on total generalized variation (TGV) regularization for the inversion of the slice-wise 2D-Radon transform in 3D. The latter problem occurs for recently-developed PAT imaging techniques with parallelized integrating ultrasound detection where projection data from various directions is sequentially acquired. As the imaging speed is presently limited to 20 seconds per 3D image, the reconstruction of temporally-resolved 3D sequences of, e.g., one heartbeat or breathing cycle, is very challenging and currently, the presence of motion artifacts in the reconstructions obstructs the applicability for biomedical research. In order to push these techniques forward towards real time, it thus becomes necessary to reconstruct from less measured data such as few-projection data and consequently, to employ sophisticated reconstruction methods in order to avoid typical artifacts. The proposed TGV-regularized Radon inversion is a variational method that is shown to be capable of such artifact-free inversion. It is validated by numerical simulations, compared to filtered back projection (FBP), and performance-tested on real data from phantom as well as in-vivo mouse experiments. The results indicate that a speed-up factor of four is possible without compromising reconstruction quality.
机译:我们提出并研究一种基于总广义变异(TGV)正则化的光声层析成像(PAT)的重建方法,用于在3D中反演逐层2D-Radon变换。对于最近开发的具有并行积分超声检测的PAT成像技术,出现后一个问题,在该技术中,顺序地采集了来自各个方向的投影数据。由于当前将成像速度限制为每个3D图像20秒,因此,例如一个心跳或呼吸周期的时间分辨3D序列的重建非常具有挑战性,目前,重建过程中运动伪影的存在阻碍了生物医学研究。为了将这些技术推向实时,因此变得有必要从较少测量的数据(例如少投影数据)进行重建,因此,为了避免典型的伪像,采用复杂的重建方法。提出的TGV规范化Radon反演是一种变分方法,被证明能够进行这种无伪影的反演。它通过数值模拟进行了验证,并与过滤后向投影(FBP)进行了比较,并对来自幻影以及体内小鼠实验的真实数据进行了性能测试。结果表明,在不影响重建质量的前提下,可以将加速因子提高到4。

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