Borges Lucas R, Oliveira Helder C R de, Nunes Polyana F, Bakic Predrag R, Maidment Andrew D A, Vieira Marcelo A C
Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, 400 Trabalhador São-Carlense Avenue, São Carlos 13566-590, Brazil.
Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, Pennsylvania 19104.
Med Phys. 2016 Jun;43(6):2704-2714. doi: 10.1118/1.4948502.
This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose.
The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose.
The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%.
A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.
本研究提出一种精确方法，用于从标准剂量采集的临床图像开始模拟数字乳腺摄影中的剂量降低。
本研究开发的方法包括对在标准辐射剂量下采集的乳腺X线照片进行缩放，并添加与信号相关的噪声。该算法考虑了数字乳腺摄影图像中相关的特定问题，如各向异性噪声、像素增益的空间变化以及剂量降低对探测量子效率的影响。缩放过程考虑了系统的线性和探测器元件的偏移。通过在标准辐射剂量和模拟剂量下采集平板模体的图像来获得插入噪声。使用安斯库姆变换，在计算出的噪声掩模和缩放后的图像之间建立关系，从而得到具有与低辐射剂量采集的图像相同噪声和灰度特征的临床乳腺X线照片。
使用在四个不同剂量下用仿真人体乳房模体采集的真实图像对所提出算法的性能进行验证，每个剂量有五次曝光，从每个图像中提取256个不重叠的感兴趣区域（ROI），并使用均匀图像。作者模拟了低剂量图像并将其与真实图像进行比较。作者评估了模拟图像和相同剂量下采集的真实图像的归一化噪声功率谱（NNPS）和功率谱（PS）之间的相似性。每个ROI的最大相对误差小于2.5%。还通过测量真实图像和模拟图像中的局部方差来评估添加的噪声。局部方差的相对平均误差小于1%。
提出了一种模拟临床乳腺X线照片中剂量降低的新方法。在该方法中，使用安斯库姆变换的新应用解决了图像噪声与图像信号之间的相关性问题。NNPS、PS和局部噪声指标证实该方法能够精确模拟各种剂量降低情况。
