Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.
Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.
复制标题
来自硅光子计数原型的头部 CT 数据的约束一步材料分解重建。
DOI：
10.1002/mp.16649
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发表时间：
2023
期刊：
影响因子：
3.8
通讯作者：
Danielsson,Mats
中科院分区：
文献类型：
--
作者：
Schmidt,TalyGilat;Sidky,EmilY;Pan,Xiaochuan;Barber,RinaFoygel;Grönberg,Fredrik;Sjölin,Martin;Danielsson,Mats
背景能谱CT材料分解提供定量信息，但受到反演到基础材料的不稳定性的挑战。我们之前已经提出了约束一步谱CT图像重建（cOSSCIR）算法，通过直接从谱CT数据估计基础材料图像来稳定材料分解反演。cOSSCIR先前研究了体模data.PurposeThis研究调查cOSSCIR的性能，使用头部CT数据集采集的临床光子计数CT（PCCT）原型。这是首次针对大规模、解剖学复杂的临床PCCT数据进行cOSSCIR研究。cOSSCIR分解之前的频谱估计和非线性计数校正校准步骤，以解决非理想的检测器effects.MethodsHead CT数据采集的早期原型临床PCCT系统使用边缘上的硅探测器与8个能量箱。还获得了一个阶梯楔形体模的校准数据，并用于训练光谱模型，以考虑源光谱和探测器光谱响应，还训练了一个非线性计数校正模型，以考虑脉冲堆积效应。cOSSCIR算法直接从光子计数数据优化骨骼和脂肪基础图像，同时对基础图像施加分组总变差（TV）约束。为了进行比较，还通过最大似然估计（MLE）的两步投影域方法重建基础图像，用于分解基础正弦图，然后通过滤波反投影（MLE + FBP）或TV最小化算法（MLE + TVmin）重建基础图像。我们假设，与两步法相比，cOSSCIR方法将提供更稳定的基础图像反演。为了研究这一假设，在重建图像中的骨和软组织感兴趣区域（ROI）的噪声标准差进行了比较cOSSCIR和两步法之间的一系列正则化约束settings.ResultscOSSCIR减少了基础图像的噪声标准差的2至6倍相比，MLE + TVmin，当两种算法被约束为产生相同的电视图像。cOSSCIR图像表现出定性改善的空间分辨率和精细解剖细节的描述。MLE + TVmin算法在较高能量水平和约束设置下的虚拟单能图像（VMI）的噪声标准差低于cOSSCIR，而cOSSCIR VMI在较低能量水平下的噪声标准差较低，总体定性空间分辨率较高。研究算法重建的图像骨区域内的平均值无统计学显著差异。重建图像的软组织区域内的平均值存在统计学显著差异，cOSSCIR产生的平均值更接近预期值。结论cOSSCIR算法，结合我们以前提出的谱模型估计和非线性计数校正方法，成功地估计了骨和脂肪基础图像的高分辨率，大规模的患者数据从临床PCCT原型。与MLE + TVmin两步法相比，cOSSCIR基础图像能够描绘精细的解剖细节，噪声标准差降低2 - 6倍。
BackgroundSpectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One‐Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data.PurposeThis study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon‐counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large‐scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects.MethodsHead CT data were acquired on an early prototype clinical PCCT system using an edge‐on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two‐step projection‐domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two‐step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft‐tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two‐step methods for a range of regularization constraint settings.ResultscOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin, when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVminalgorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft‐tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values.ConclusionsThe cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large‐scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmintwo‐step approach.