Hashemi SayedMasoud, Song William Y, Sahgal Arjun, Lee Young, Huynh Christopher, Grouza Vladimir, Nordström Håkan, Eriksson Markus, Dorenlot Antoine, Régis Jean Marie, Mainprize James G, Ruschin Mark
Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Phys Med Biol. 2017 Apr 7;62(7):2521-2541. doi: 10.1088/1361-6560/aa5ed2. Epub 2017 Mar 1.
One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm which was increased to 1.2 mm by SDIR, at half maximum.
锥束CT（CBCT）图像质量的限制因素之一是系统模糊，这是由探测器响应、X射线源焦点尺寸、方位模糊和重建算法引起的。在这项工作中，我们开发了一种新颖的迭代重建算法，该算法通过在重建公式中明确考虑不同因素引起的图像不清晰来提高空间分辨率。虽然基于模型的迭代重建技术使用有关探测器响应和X射线源的先验信息，但我们提出的技术使用简单的可测量模糊模型。在我们的重建算法中，称为同时去模糊和迭代重建（SDIR），可以使用CatPhan体模或任何其他MTF体模（如线体模）的调制传递函数（MTF）切片来估计模糊核。提出的图像重建公式包括两个正则化项：（1）总变差（TV）和（2）非局部正则化，通过分裂Bregman增广拉格朗日迭代方法求解。SDIR公式保留了边缘，简化了参数调整以实现高空间分辨率和低噪声方差，并减少了常规TV惩罚迭代算法引起的阶梯效应。所提出的算法针对用于图像引导放射外科的床边头部CBCT单元进行了优化，并使用CatPhan体模、拟人化头部体模和6例临床脑立体定向放射外科病例进行了测试。我们的实验表明，在MTF和线对分辨率方面，SDIR优于传统的滤波反投影和TV惩罚同时代数重建技术方法（以自适应最速下降POCS算法，ASD-POCS为代表），并保留了基于标准TV的迭代重建算法在提高对比度和减少重建伪影方面的良好特性。它提高了骨区域和脑软组织中高对比度细节的可见性。例如，结果表明，在SDIR重建图像中脑室和一些脑沟变得可见，并且可见病变的对比度得到有效改善。线对分辨率从FBP中的12线对/厘米提高到SDIR中的14线对/厘米。将ASD-POCS的参数调整到14线对/厘米会导致噪声方差高于SDIR。使用这些ASD-POCS参数时，FBP和ASD-POCS的MTF非常接近，等于0.7毫米，在半最大值处通过SDIR增加到1.2毫米。
