论文标题
3D动态对比增强超声成像的运动校正无解剖BMODE图像
Motion Correction of 3D Dynamic Contrast-Enhanced Ultrasound Imaging without Anatomical Bmode Images
论文作者
论文摘要
在常规的2D DCE-US中，运动校正算法利用并排的解剖Bmode图像，其中包含时间稳定功能。但是，当前3D DCE-US的商业模型不提供并排的BMODE图像，这使运动校正具有挑战性。这项工作引入了3D DCE-US的新型运动校正（MC）算法，并在处理临床数据集时评估其功效。简而言之，该算法使用了一种锥体方法，从而创建了由3-6个连续帧组成的短颞窗来执行本地注册，然后将其注册为从所有帧的加权平均值中得出的主参考。我们使用飞利浦X6-1基质换能器以1-3 Hz的速率评估了8例肝脏转移性病变患者的算法。 We assessed improvements in original vs. motion corrected 3D DCE-US cine using: i) frame-to-frame volumetric overlap of segmented lesions, ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2) quality-of-fit from fitted time-intensity curves （TIC）从分段病变中提取。总体而言，结果表明，应用所提出的算法后，3D DCE-US运动的显着降低。我们注意到所有患者的框架到框病变重叠的重大改善，从没有校正的68％到运动校正后的83％（p = 0.023）。 NCC评估的框架到框架相似性也从0.694（原始CINE）到0.862（相应的MC Cine）和0.723至0.886的两组时间点上也显着提高。 TIC分析显示，RMSE显着降低（P = 0.018），患者队列的R2拟合优度（P = 0.029）显着增加。
In conventional 2D DCE-US, motion correction algorithms take advantage of accompanying side-by-side anatomical Bmode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side Bmode images, which makes motion correction challenging. This work introduces a novel motion correction (MC) algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets. In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of 3-6 consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We evaluated the algorithm in 8 patients with metastatic lesions in the liver using the Philips X6-1 matrix transducer at a frame rate of 1-3 Hz. We assessed improvements in original vs. motion corrected 3D DCE-US cine using: i) frame-to-frame volumetric overlap of segmented lesions, ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion. Overall, results demonstrate significant decreases in 3D DCE-US motion after applying the proposed algorithm. We noted significant improvements in frame-to-frame lesion overlap across all patients, from 68% without correction to 83% with motion correction (p = 0.023). Frame-to-frame similarity as assessed by NCC also significantly improved on two different sets of time points from 0.694 (original cine) to 0.862 (corresponding MC cine) and 0.723 to 0.886. TIC analysis displayed a significant decrease in RMSE (p = 0.018) and a significant increase in R2 goodness-of-fit (p = 0.029) for the patient cohort.
