Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.

在面向精准医疗的分子影像领域,荧光分子断层成像(FMT)是当前的研究热点之一。由于FMT逆问题严重的病态性,背景荧光噪声会对重建结果产生严重的负面影响。在深入研究基于有限元的FMT重建方法的基础上,提出利用低秩矩阵填充技术克服背景荧光的方法。该方法将不同激发节点形成的外表面观测组成一个有元素缺失的观测矩阵,利用低秩矩阵填充算法恢复该矩阵的缺失元素,同时抑制观测矩阵含有的背景荧光噪声。利用去噪后的观测矩阵建立了新的FMT逆问题模型,并利用其对荧光目标进行重建。单荧光和双荧光目标重建实验表明:基于去噪后FMT逆问题模型的重建结果获得了显著改善。

荧光分子断层成像是一种高稳定性、低副作用的分子影像技术, 一直是生物光学领域的研究热点, 当激发平面位置与荧光目标位置接近时, 光源的重建结果会更好; 为了确定激发平面的位置, 提出了一种混合高斯方法, 该方法首先使用少量激发光源来获得发射光的生物体外表面分布, 再使用带剪枝策略的混合高斯模型对该分布进行拟合, 最后利用拟合后的峰值自动确定激发平面的个数和位置; 基于新激发平面的激发光源可以获得荧光分子断层成像逆问题, 进而利用该逆问题对荧光目标进行重建。实验结果表明：基于重新定位的激发平面的荧光分子断层成像光源重建结果在定位精度上显著优于原始激发平面对应的重建结果。

With widely availed clinically used radionuclides, Cerenkov luminescence imaging (CLI) has become a potential tool in the field of optical molecular imaging. However, the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly, which affects the accuracy of quantitative analysis, as well as the three-dimensional reconstruction. In this work, a novel denoising framework based on fuzzy clustering and curvature-driven diffusion (CDD) is proposed to remove this kind of impulse noises. To improve the accuracy, the Fuzzy Local Information C-Means algorithm, where spatial information is evolved, is used. We evaluate the performance of the proposed framework systematically with a series of experiments, and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method. We hope this work may provide a useful data pre-processing tool for CLI and its following studies.

有限投影荧光分子断层成像(FMT)可以以较短的数据采集时间在动物体内快速重建出荧光目标的三维分布。然而, 由于较少的投影数据使得有限投影FMT具有严重的病态性。为了降低FMT重建的病态性并提高重建速度, 考虑到FMT中光源稀疏分布的特性, 提出了一种结合平滑l0范数(SL0)和可行区域的有限投影FMT重建方法, 采用一种基于SL0的FMT重建方法, 利用一个连续函数来逼近l0范数, 以实现快速求解, 同时将可行区域作为有效的先验信息, 以提高重建精度。数字鼠模型的重建结果表明, 在3、6、9个激发点下, 重建图像的位置误差都小于1 mm, 重建时间缩短, 3个激发点下的重建时间为8 s。物理实验的重建结果进一步表明了该方法在实际FMT重建上的可行性。

在非匀质成像中,器官形状是影响建模光在生物体内传播过程的重要因素,它能直接影响荧光分子断层成像(FMT)的重建过程。器官图像的手动分割过程较为复杂,且对图像质量要求较高,而边缘检测、区域生长、主动轮廓模型等自动分割方法在处理复杂医学图像时存在很大的局限性。因此,使用基于主动形状模型(ASM)的自动分割方法,对小鼠器官图像进行准确分割,并使用基于L₁范数优化的重建算法实现光源重建。为分析基于ASM的器官图像分割精度与重建精度的关系,采集小鼠计算机断层扫描(CT)数据并进行真实实验,与流行的基于Snake模型的分割算法进行比较。实验结果表明,ASM算法可以替代手动分割,不影响光源的位置重建。

As an emerging molecular imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) uses X-ray-excitable probes to produce near-infrared (NIR) luminescence and then reconstructs three-dimensional (3D) distribution of the probes from surface measurements. A proper photon-transportation model is critical to accuracy of XLCT. Here, we presented a systematic comparison between the common-used Monte Carlo model and simplified spherical harmonics (SPN). The performance of the two methods was evaluated over several main spectrums using a known XLCT material. We designed both a global measurement based on the cosine similarity and a locally-averaged relative error, to quantitatively assess these methods. The results show that the SP3 could reach a good balance between the modeling accuracy and computational e±ciency for all of the tested emission spectrums. Besides, the SP1 (which is equivalent to the diffusion equation (DE)) can be a reasonable alternative model for emission wavelength over 692 nm. In vivo experiment further demonstrates the reconstruction performance of the SP3 and DE. This study would provide a valuable guidance for modeling the photon-transportation in CB-XLCT.

锥束X射线发光断层成像(CB-XLCT)是一种新型分子影像模态, 对疾病的早期检测、靶向治疗以及药物研制等具有重要意义。然而, 通过传统的压缩感知理论反演生物体内纳米目标的三维分布时, 高维系统矩阵的强相关性会直接影响成像质量。基于非凸稀疏L1-2正则子, 将CB-XLCT的成像问题转化为一种新的稀疏重建模型。采用一种凸差分算法来解决非凸泛函最小化问题, 在每一步凸差分子迭代中采用一种带自适应惩罚项的交替方向乘子法进行高效求解。设计了单目标数字鼠仿体、双目标数字鼠仿体以及真实在体老鼠实验验证提出算法的有效性和稳健性, 并与五种常见正则子 (L1/2,L1,L2,TV和L0)进行对比和分析。实验结果表明, L1-2正则子的成像性能最优, 提出方法可以有效解决CB-XLCT的快速成像问题。

采用大规模荧光分子断层成像（FMT）投影数据进行重建需要消耗大量的计算内存，花费较长的计算时间。为降低FMT重建的病态性以及加快重建速度，基于流形学习和压缩感知理论，提出了结合局部保留投影（LPP）和稀疏正则化的重建方法，并对原始的多投影荧光数据进行重建。为评估该方法的重建效果和时间，分别设计了非匀质圆柱单、双目标仿真实验和真实小鼠实验。实验结果表明，在保证FMT重建图像精度和分辨率的同时将重建时间大幅度减少。

利用纳米发光材料的X 射线发光断层成像(XLCT)作为一种新型的成像模态，能够同时进行功能成像以及分子成像。在XLCT 中，光子在组织中的散射效应使得纳米发光目标的重建具有不适定性，因此如何快速、精确地重建目标成为一个难题。针对此问题，选择扩散近似模型描述组织中的光子传输过程，采用基于L1 正则化的分割增广拉格朗日收缩方法进行重建。在数值实验和物理实验中，将其与初始增广拉格朗日方法对比，验证其可行性。实验结果表明，该算法得到的重建结果无论在质量方面还是在收敛速度方面都具有一定优势。