The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed image quality. Recently, as prior information, a high-quality spectral mean image was introduced into the prior image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the image gradient L₁-norm is employed, and it can cause blurred edge structures in the reconstructed images. Encouraged by the advantages in edge preservation and finer structure recovering, the image gradient L₀-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise images, leading to an L₀-norm based adaptive SPICCS (L₀-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.

基于光子计数检测器的光谱计算机断层扫描（CT）有望用于病变检测，组织表征和材料分解。但是，多能量投影数据集中较低的信噪比可能导致重建的图像质量较差。近来，作为先验信息，将高质量的光谱均值图像引入到先验图像约束压缩感测（PICCS）框架中以抑制噪声，从而导致产生光谱PICCS（SPICCS）。在原始SPICCS模型中，采用了图像梯度L _1-范数，它会导致重建图像中的边缘结构模糊。受到边缘保留和更精细的结构恢复的优势的鼓舞，图像梯度L ₀-norm已合并到PICCS模型中。此外，由于在不同的通道能量谱的不同，加权因子被引入并自适应地调整用于不同的信道逐图像，导致的L ₀范数的自适应SPICCS（L ₀ -ASPICCS）算法进行低剂量的光谱CT重建。使用split-Bregman方法来最小化目标函数。进行了广泛的数值模拟和物理幻象实验，以评估该方法。通过与最新算法（例如同时代数重构技术，总变异最小化和SPICCS）进行比较，我们的方法在定性和定量评估结果方面均展示了其优势。