Romadanov Ivan, Abeywardhana Ruwan, Sattarivand Mike
Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, Canada.
Department of Physics & Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
Phys Med Biol. 2022 Apr 21;67(9). doi: 10.1088/1361-6560/ac6201.
To develop a dual-energy (DE) algorithm with spatially varying weighting factors for material selection and noise suppression.Calibration step-phantoms, with overlapping slabs of solid water and bone with different thicknesses, were used to obtain the pre-calibrated material selection and noise reduction weighting factors. The Material selection weighting factors were calculated by finding a zero of contrast-to-noise-ratio (CNR) between regions with two overlapping materials and regions of only target material, while noise suppression weighting factors were determined by maximizing signal-to-noise ratio for overlapping regions. The pre-calibrated weighting factors were fitted with low and high energy radiograph of two Rando phantoms to create maps of material selection and noise suppression weighting factors, which used with DE algorithm and anti-correlated noise reduction (ACNR) algorithm to generate DE images. Three different implementations, including two different sizes of Rando phantoms and two different orientations (oblique and anterior-posterior), were investigated. Soft-tissue and bone only images of Rando phantoms were obtained with five combinations of DE algorithms and CNR, contrast, and noise values of selected regions of interest were compared to evaluate the performance of the novel method: simple log subtraction (SLS), SLS with uniform ACNR, adaptive DE (aDE), aDE with uniform ACNR, and aDE and adaptive ACNR (aACNR).Compared to SLS, the aDE algorithm demonstrated improved image quality in all three orientations. CNR increased with better contrast for both soft-tissue and bone images. Implementation of aACNR algorithm resulted in further reduction of image noise and improvements in CNR at the cost of contrast. However, aACNR algorithm showed better contrast compared to ACNR method.A novel DE algorithm was proposed, which showed improved material selection and noise suppression as compared to the conventional DE techniques and can be easily implemented in a clinical environment for real-time DE image generation.
开发一种具有空间变化加权因子的双能（DE）算法，用于材料选择和噪声抑制。使用校准步骤体模，其包含不同厚度的固体水和骨骼的重叠平板，以获得预校准的材料选择和降噪加权因子。材料选择加权因子通过找到两种重叠材料区域与仅目标材料区域之间的对比度噪声比（CNR）的零点来计算，而噪声抑制加权因子通过最大化重叠区域的信噪比来确定。将预校准的加权因子与两个Rando体模的低能和高能射线照片进行拟合，以创建材料选择和噪声抑制加权因子图，这些图与DE算法和反相关降噪（ACNR）算法一起用于生成DE图像。研究了三种不同的实现方式，包括两种不同尺寸的Rando体模和两种不同的方向（斜位和前后位）。使用DE算法的五种组合获得Rando体模的仅软组织和骨骼图像，并比较所选感兴趣区域的CNR、对比度和噪声值，以评估新方法的性能：简单对数减法（SLS）、具有均匀ACNR的SLS、自适应DE（aDE）、具有均匀ACNR的aDE以及aDE和自适应ACNR（aACNR）。与SLS相比，aDE算法在所有三个方向上均表现出图像质量的改善。软组织和骨骼图像的CNR随着对比度的提高而增加。aACNR算法的实现导致图像噪声进一步降低，CNR提高，但以对比度为代价。然而，与ACNR方法相比，aACNR算法显示出更好的对比度。提出了一种新颖的DE算法，与传统DE技术相比，该算法在材料选择和噪声抑制方面表现出改善，并且可以在临床环境中轻松实现以实时生成DE图像。
