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首页> 外文期刊>Physics in medicine and biology. >Spectral diffusion: An algorithm for robust material decomposition of spectral CT data
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Spectral diffusion: An algorithm for robust material decomposition of spectral CT data

机译：光谱扩散：一种用于光谱CT数据的可靠材料分解的算法

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摘要

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL-1), gold (0.9 mg mL-1), and gadolinium (2.9 mg mL-1) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.
机译：双重能量CT的临床成功，能量鉴别X射线探测器的积极开发以及新型，针对特定目标的纳米粒子造影剂有望将光谱CT确立为强大的功能成像方式。所有这些应用的共同点是需要一种在存在噪声的情况下具有鲁棒性的材料分解算法。在这里，我们开发了一种算法，该算法使用频谱联合，分段常数核回归和split Bregman方法迭代地求解梯度稀疏，定量准确且偏差最小的材料分解。我们称此算法为频谱扩散，是因为它在扩散式降噪算法（例如各向异性扩散，总变化，双边过滤）的框架内整合了来自多个光谱通道的结构信息及其对应的材料分解。使用3D，数字条状幻像和经过校准以与多色X射线源配合使用的材料灵敏度矩阵，我们量化了碘的三能材料分解过程中光谱扩散提供的可检测性极限（CNR = 5）（3.1 mg mL-1），金（0.9 mg mL-1）和g（2.9 mg mL-1）浓度。然后，我们将光谱扩散应用于小鼠肾脏，肝脏和脾脏中这三种物质的体内分离。

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