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Comparison of l_p-regularization-based reconstruction methods for time domain fluorescence molecular tomography on early time gates

机译:基于l_p正则化的重建方法在早期时间门上进行时域荧光分子层析成像的比较

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摘要

Time domain florescence molecular tomography (TD-FMT) allows 3D visualization of multiple fluorophores based on lifetime contrast and provides a unique data set for enhanced quantification and spatial resolution. The time-gate data set can be divided into two groups around the maximum gate, which are early gates and late gates. It is well-established that early gates allow for improved spatial resolution of reconstruction. However, photon counts are inherently very low at early gates due to the high absorption and scattering of tissue. It makes image reconstruction highly susceptible to the effects of noise and numerical errors. Moreover, the inverse problem of FMT is the ill-posed and underdetermined. These factors make reconstruction difficult for early time gates. In this work, l_p (0<p≤1) regularization based reconstruction algorithm was developed within our wide-field mesh-based Monte Carlo reconstruction strategy. The reconstructions performances were validated on a synthetic murine model simulating the fluorophores uptake in the kidneys and with experimental preclinical data. We compared the early time-gate reconstructed results using l_(1/3) l_(1/2) and l_1 regularization methods in terms of quantification and resolution. The regularization parameters were selected by the Lcurve method. The simulation results of a 3D mouse atlas and mouse experiment show that l_p (0<p<1) regularization method obtained more sparse and accurate solutions than l_1 regularization method for early time gates.
机译:时域荧光分子层析成像(TD-FMT)可以基于寿命对比,对多个荧光团进行3D可视化,并提供独特的数据集以增强定量和空间分辨率。时间门数据集可以在最大门附近分为两组,分别是早期门和晚期门。众所周知,早期的门可以提高重建的空间分辨率。然而,由于组织的高吸收和散射,早期门的光子计数本来就很低。它使图像重建极易受到噪声和数值误差的影响。而且,FMT的反问题是不适定的和不确定的。这些因素使重建工作难以尽早进行。在这项工作中,在我们基于广域网格的蒙特卡洛重构策略中,开发了基于l_p(0 <p≤1)正则化的重构算法。重建性能在模拟肾脏中荧光团摄取的合成鼠模型上并通过实验前的临床数据进行了验证。我们在量化和分辨率方面比较了使用l_(1/3)l_(1/2)和l_1正则化方法的早期时间门重建结果。通过Lcurve方法选择正则化参数。 3D鼠标图集和鼠标实验的仿真结果表明,与早期门的l_1正则化方法相比,l_p(0 <p <1)正则化方法获得的稀疏度和准确度更高。

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