College of Electronic Information Engineering, Hebei University, Baoding 071000, China.
Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, China.
J Healthc Eng. 2017;2017:9296354. doi: 10.1155/2017/9296354. Epub 2017 Jun 27.
Motion and deformation are common in prostate diffusion-weighted magnetic resonance imaging (DWI) during acquisition. These misalignments lead to errors in estimating an apparent diffusion coefficient (ADC) map fitted with DWI. To address this problem, we propose an image registration algorithm to align the prostate DWI and improve ADC map. First, we apply affine transformation to DWI to correct intraslice motions. Then, nonrigid registration based on free-form deformation (FFD) is used to compensate for intraimage deformations. To evaluate the influence of the proposed algorithm on ADC values, we perform statistical experiments in three schemes: no processing of the DWI, with the affine transform approach, and with FFD. The experimental results show that our proposed algorithm can correct the misalignment of prostate DWI and decrease the artifacts of ROI in the ADC maps. These ADC maps thus obtain sharper contours of lesions, which are helpful for improving the diagnosis and clinical staging of prostate cancer.
运动和变形在前列腺磁共振扩散加权成像(DWI)采集过程中很常见。这些不匹配会导致拟合 DWI 的表观扩散系数(ADC)图的估计出现误差。为了解决这个问题,我们提出了一种图像配准算法,用于对齐前列腺 DWI 并改善 ADC 图。首先,我们对 DWI 应用仿射变换以校正层内运动。然后,使用基于自由形态变形(FFD)的非刚性配准来补偿图像内变形。为了评估所提出算法对 ADC 值的影响,我们在三种方案中进行了统计实验:不对 DWI 进行处理、使用仿射变换方法和使用 FFD。实验结果表明,我们提出的算法可以纠正前列腺 DWI 的错位,并减少 ADC 图中 ROI 的伪影。这些 ADC 图因此获得了更清晰的病变轮廓,有助于提高前列腺癌的诊断和临床分期。