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Estimation of Cardiac Respiratory-Motion by Semi-Automatic Segmentation and Registration of Non-Contrast-Enhanced 4D-CT Cardiac Datasets

机译:通过半自动分割和非对比增强4D-CT心脏数据集的登记估计心脏呼吸运动

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摘要

The goal of this work is to investigate, for a large set of patients, the motion of the heart with respiration during free-breathing supine medical imaging. For this purpose we analyzed the motion of the heart in 32 non-contrast enhanced respiratory-gated 4D-CT datasets acquired during quiet unconstrained breathing. The respiratory-gated CT images covered the cardiac region and were acquired at each of 10 stages of the respiratory cycle, with the first stage being end-inspiration. We devised a 3-D semi-automated segmentation algorithm that segments the heart in the 4D-CT datasets acquired without contrast enhancement for use in estimating respiratory motion of the heart. Our semi-automated segmentation results were compared against interactive hand segmentations of the coronal slices by a cardiologist and a radiologist. The pairwise difference in segmentation among the algorithm and the physicians was on the average 11% and 10% of the total average segmented volume across the patient, with a couple of patients as outliers above the 95% agreement limit. The mean difference among the two physicians was 8% with an outlier above the 95% agreement limit. The 3-D segmentation was an order of magnitude faster than the Physicians\u27 manual segmentation and represents significant reduction of Physicians\u27 time. The segmented first stages of respiration were used in 12 degree-of-freedom (DOF) affine registration to estimate the motion at each subsequent stage of respiration. The registration results from the 32 patients indicate that the translation in the superior-inferior direction was the largest component motion, with a maximum of 10.7 mm, mean of 6.4 mm, and standard deviation of 2.2 mm. Translation in the anterior-posterior direction was the next largest component of motion, with a maximum of 4.0 mm, mean of 1.7 mm, and standard deviation of 1.0 mm. Rotation about the right-left axis was on average the largest component of rotation observed, with a maximum of 4.6 degrees, mean of 1.6 degrees, and standard deviation of 2.1 degrees. The other rotation and shear parameters were all close to zero on average indicting the motion could be reasonably well approximated by rigid-body motion. However, the product of the three scale factors averaged about 0.97 indicating the possibility of a small decrease in heart volume with expiration. The motion results were similar whether we used the Physician\u27s segmentation or the 3-D algorithm.
机译:这项工作的目的是针对大量患者研究自由呼吸仰卧位医学成像过程中伴随呼吸的心脏运动。为此,我们分析了在安静无拘束呼吸过程中获得的32个非对比增强型呼吸门控4D-CT数据集中的心脏运动。呼吸门控CT图像覆盖了心脏区域,并在呼吸周期的10个阶段中的每个阶段获取,第一阶段是结束吸气。我们设计了一种3D半自动分割算法,该算法可在没有对比增强的情况下将4D-CT数据集中的心脏进行分割,以用于估计心脏的呼吸运动。我们的半自动分割结果与心脏科医生和放射科医生对冠状切片的交互式手部分割进行了比较。该算法和医生之间的分割成对差异平均是整个患者平均总分割体积的11%和10%,其中一些患者的离群值高于95%协议限制。两位医生的平均差异为8%,离群值高于95%的协议限制。 3-D分割比Physicians \ u27手动分割快一个数量级,并且表示Physicians \ u27的时间显着减少。在12个自由度(DOF)仿射配准中使用分段的呼吸的第一阶段,以估计每个后续呼吸阶段的运动。来自32例患者的登记结果表明,上下方向的平移是最大的运动分量,最大为10.7 mm,平均为6.4 mm,标准偏差为2.2 mm。前后方向的平移是运动的第二大组成部分,最大为4.0毫米,平均为1.7毫米,标准偏差为1.0毫米。平均而言,绕左右轴旋转是最大的旋转分量,最大旋转4.6度,平均旋转1.6度,标准偏差为2.1度。平均而言,其他旋转参数和剪切参数均接近于零,表明该运动可以通过刚体运动合理地很好地近似。但是,这三个比例因子的乘积平均约为0.97,表明随着年龄的增长,心脏容量可能会略有下降。无论我们使用Physician分割还是3-D算法,运动结果都相似。

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