Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.
Department of Physics & Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
Phys Med Biol. 2020 Dec 17;65(24):245023. doi: 10.1088/1361-6560/ab9e57.
The main goal of this work is to improve the previously proposed patient-specific pixel-based dual-energy (PP-DE) algorithm by developing an adaptive anti-correlated noise reduction (ACNR) method, resulting in reduced image noise.
Theoretical models of contrast-to-noise (CNR) and signal-to-noise (SNR) ratio were developed as functions of weighting factors for DE bone ω or soft tissue ω cancellation. These analytical expressions describe CNR and SNR properties of dual-energy (DE) images, obtained with both simple log subtraction (SLS) and ACNR algorithms, and allow for a direct comparison between experimental and theoretical results. The theoretical models demonstrate the importance of ACNR weighting factor (ω ) optimization leading to the maximization of the SNR of the final image. A step phantom was constructed, which consisted of overlapping slabs of solid water (0-30 cm) and bone-mimicking material (0-6 cm), resulting in a total of 7 × 7 regions. High-energy (HE) and low-energy (LE) images were acquired at 140 kVp and 60 kVp with a clinical ExacTrac imaging system. The CNR and SNR were obtained for the DE images as functions of ω and noise reduction weighting factor ω for different combinations of thicknesses. Weighting factors for bone cancellation were optimized for each region of interest (ROI) by finding zeros of CNR function for DE images between soft tissue only and soft tissue plus bone regions (and vice versa for soft tissue cancellation). The weighting factor for the ACNR algorithm ω was then optimized by maximizing the SNR function for each ROI. HE and LE images for an anthropomorphic Rando phantom were obtained with the same acquisition parameters as for the step phantom. DE images for bone only and soft tissue only were obtained with three algorithms: SLS and PP-DE with conventional ACNR (uniform ω ), and PP-DE with adaptive ACNR (region-varying ω ). Weighting factor maps for PP-DE and adaptive ACNR methods were obtained for Rando phantom geometry (which was determined from its CT scans) by interpolation (or extrapolation) of weighting factors for the step phantom. CNR values were calculated for different regions.
The CNR and SNR characteristics as functions of material cancellation and noise reduction weighting factors were obtained from theoretical models and experimental data from the step phantom. This showed a good qualitative validation of the models. For the ANCR algorithm, both the theory and experiment demonstrated that the material cancellation weighting factors (ω ) can be optimized independently of the noise cancellation weighting factors (ω ), which can be optimized by maximizing SNR. For each ROI (with different overlapping bone and soft tissue thicknesses) the weighting factors ω were determined as well as corresponding optimal weighting factors ω for noise reduction. For the Rando phantom, CNR values for regions representing different anatomical structures (ribs, spine, and tumor) were evaluated. It was shown that the proposed adaptive ACNR further improves image quality, compared to the conventional ACNR algorithm. The improvement is maximized for regions with bones (ribs or spine), where the largest attenuation is observed.
The ACNR weighting factors are dependent on the material thicknesses due to varying beam attenuation leading to different levels of quantum noise. This was shown with the derived theoretical expressions of the CNR and SNR functions and was validated by experimental data. The adaptive ANCR DE algorithm was developed, which allows for an increase in image quality by spatially varying weighting factors for noise reduction. This algorithm complements the previously developed PP-DE algorithm to obtain better quality DE images.
本研究旨在通过开发自适应相关降噪（ACNR）方法来改进先前提出的基于像素的双能（PP-DE）算法，从而降低图像噪声。
针对 DE 骨 ω 或软组织 ω 消除的加权因子，建立了对比度噪声比（CNR）和信噪比（SNR）比的理论模型。这些解析表达式描述了使用简单对数减法（SLS）和 ACNR 算法获得的双能（DE）图像的 CNR 和 SNR 特性，并允许在实验和理论结果之间进行直接比较。理论模型表明，通过优化 ACNR 加权因子（ω ）以最大化最终图像的 SNR 是很重要的。构建了一个阶梯式体模，由固体水（0-30 cm）和骨模拟材料（0-6 cm）的重叠板组成，总共包含 7×7 个区域。使用临床 ExacTrac 成像系统在 140 kVp 和 60 kVp 下获取高能（HE）和低能（LE）图像。针对不同厚度组合，获得了 DE 图像的 CNR 和 SNR 作为 ω 和噪声降低加权因子 ω 的函数。对于每个感兴趣区域（ROI），通过在仅软组织和软组织加骨区域之间寻找 DE 图像的 CNR 函数的零点（反之亦然软组织的消除），优化了骨消除的加权因子。然后通过对每个 ROI 进行 SNR 函数最大化来优化 ACNR 算法的 ω 加权因子。使用与阶梯式体模相同的采集参数获取了人体 Rando 体模的 HE 和 LE 图像。仅获得了骨骼和软组织的 DE 图像，使用了三种算法：SLS 和具有常规 ACNR（均匀 ω ）的 PP-DE，以及具有自适应 ACNR（区域变化 ω ）的 PP-DE。通过对 Rando 体模几何形状（由其 CT 扫描确定）进行插值（或外推），获得了 PP-DE 和自适应 ACNR 方法的加权因子图。为不同的区域计算了 CNR 值。
从理论模型和阶梯式体模的实验数据中获得了作为材料消除和噪声降低加权因子函数的 CNR 和 SNR 特性。这很好地验证了模型的定性。对于 ANCR 算法，理论和实验都表明可以独立优化材料消除加权因子（ω ），并且可以通过最大化 SNR 来优化噪声消除加权因子（ω ）。对于每个 ROI（具有不同重叠的骨骼和软组织厚度），确定了加权因子 ω 以及相应的最佳噪声降低加权因子 ω 。对于 Rando 体模，评估了代表不同解剖结构（肋骨、脊柱和肿瘤）的区域的 CNR 值。结果表明，与传统的 ACNR 算法相比，所提出的自适应 ACNR 进一步提高了图像质量。在具有骨骼（肋骨或脊柱）的区域中，观察到最大衰减，因此改善效果最大。
ACNR 加权因子取决于材料厚度，这是由于束衰减导致量子噪声水平不同。这是通过 CNR 和 SNR 函数的推导理论表达式和实验数据得到的。开发了自适应 ANCR DE 算法，通过空间变化的噪声降低加权因子来提高图像质量。该算法补充了先前开发的 PP-DE 算法，以获得更好质量的 DE 图像。
