Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Department of Radiology, Stanford University, Stanford, California, USA.
Med Phys. 2024 Jan;51(1):224-238. doi: 10.1002/mp.16590. Epub 2023 Jul 3.
Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store.
This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors.
We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density (0-40 g/cm water, 0-2.16 g/cm calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively.
The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively.
We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.
光子计数探测器（PCD）提供更高的空间分辨率、改进的对比噪声比（CNR）和能量分辨能力。然而，光子计数计算机断层扫描（PCCT）系统中大量增加的投影数据通过滑环传输、处理和存储变得具有挑战性。
本研究提出并评估了一种经验优化算法，以获得用于能量-bin 数据压缩的最佳能量权重。该算法普遍适用于光谱成像任务，包括 2 和 3 材料分解（MD）任务和虚拟单能量图像（VMI）。该方法实现简单，同时保留了全厚度范围内的光谱信息，适用于不同的 PCD，例如硅探测器和 CdTe 探测器。
我们使用现实的探测器能量响应模型来模拟不同 PCD 的光谱响应，并使用经验校准方法为每个 PCD 拟合半经验正向模型。我们通过最小化由于能量加权-bin 压缩导致的平均相对克拉美罗下限（CRLB），对 MD 和 VMI 任务的最优能量权重进行数值优化，材料面积密度范围为 0-40 g/cm 水，0-2.16 g/cm 钙。我们使用阶梯楔体和人体头部模型的蒙特卡罗模拟分别在投影域和图像域评估这种能量-bin 压缩方法的性能。
结果表明，对于 2 MD，能量-bin 压缩方法可以将 PCCT 数据大小减少 75%和 60%，硅和 CdTe 探测器的平均方差惩罚分别小于 17%和 3%。对于具有 K 边材料（碘）的 3 MD 任务，该方法可以将数据大小减少 62.5%和 40%，硅和 CdTe 探测器的平均方差惩罚分别小于 12%和 13%。
我们提出了一种能量-bin 压缩方法，该方法广泛适用于不同的 PCCT 系统和物体尺寸，具有较高的数据压缩率和较少的光谱信息损失。
