Automated patient motion detection and correction in dynamic renal scintigraphy.
Automated patient motion detection and correction in dynamic renal scintigraphy.
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DOI：
10.2967/jnmt.110.081893
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发表时间：
2011-06
影响因子：
1.3
通讯作者：
Taylor AT
中科院分区：
文献类型：
--
作者：
Folks RD;Manatunga D;Garcia EV;Taylor AT
肾动态核素扫描过程中的肾脏运动会导致计算的肾功能参数出现误差。我们的目标是开发和验证检测和纠正患者运动的算法。我们回顾收集了86例临床肾脏研究(女性42例，男性44例)的动态图像，采用以下方法进行99mTcMertiatide(MAG3)显像：80帧128×128图像帧(24帧2秒，16帧15秒，40帧30秒，128×128，3.2 mm/像素)。我们在每个患者研究中模拟了10种类型的垂直运动，产生了860个图像集。运动由每帧0.25像素到4像素的幅度上移或下移组成，并且要么是A)在多个帧上逐渐增加移位，要么是B)一个或多个连续帧的突然移位，随后返回到开始位置。添加附加的水平运动以测试其对垂直运动检测的影响。原始文件和移动文件被提交给运动检测算法。应用校正移位，并在逐个像素的基础上比较校正后的图像和原始的、未移位的图像。对在原始未移位数据中检测到的运动进行校正之前和之后，也对在移位数据中检测到的运动进行列表。如果检测到的漂移在模拟震级的0.25像素以内，则认为它是正确的。开发了软件，以便于对所有图像进行视觉检查，并使用线条图总结肾脏运动和运动校正。当原始图像中的现有运动首次被校正时，对模拟位移的总体检测为99%(3068/3096帧)。当原始运动未被校正时，总体移位检测为76%(2345/3096帧)。对于其中没有添加移位的图像帧(并且原始运动没有被校正)，87%(27142/31132帧)被正确检测为没有移位。当校正后的图像与原始图像进行比较时，计算的计数恢复对于所有像素级的移位都是100%。对于分数像素移位，百分比计数回收率在52-73%之间。视觉检查表明，一些原始的、未移位的画面显示出真实的患者运动。该算法准确地检测到了小到0.25像素的运动。可以高精度地检测和校正整个像素的运动。分数像素运动可以被检测和校正，但精度较低。重要的是，该算法准确地识别了未移位的帧，这有助于防止在运动校正过程中引入错误。
Kidney motion during dynamic renal scintigraphy can cause errors in calculated renal function parameters. Our goal was to develop and validate algorithms to detect and correct patient motion. We retrospectively collected dynamic images from 86 clinical renal studies (42 females, 44 males), acquired using the following protocol for 99m Tc Mertiatide (MAG3) imaging: 80 128×128 image frames (24 2-second frames, 16 15-second frames, 40 30-second frames, 128×128, 3.2 mm/pixel). We simulated ten types of vertical motion in each patient study, resulting in 860 image sets. Motion consisted of up or down shifts of magnitude 0.25 pixels to 4 pixels per frame, and were either A) gradual shift additive over multiple frames or B) abrupt shift of one or more consecutive frames, with a later return to the start position. Additional horizontal motion was added to test its effect on detection of vertical motion. Original and shifted files were submitted to a motion detection algorithm. Corrective shifts were applied, and corrected and original, unshifted images were compared on a pixel by pixel basis. Motion detected in the shifted data was also tabulated before and after correcting for motion detected in the original unshifted data. A detected shift was considered correct if it was within 0.25 pixel of the simulated magnitude. Software was developed to facilitate visual review of all images, and to summarize kidney motion and motion correction using linograms. Overall detection of simulated shifts was 99% (3068/3096 frames) when the existing motion in the original images was first corrected. When the original motion was not corrected, overall shift detection was 76% (2345/3096 frames). For image frames in which no shift was added, (and original motion was not corrected) 87% (27142/31132 frames) were correctly detected as having no shift. When corrected images were compared to original, calculated count recovery was 100% for all shifts that were whole pixel magnitudes. For fractional pixel shifts, percent count recovery varied from 52–73%. Visual review suggested that some original, unshifted frames exhibited true patient motion. The algorithm accurately detected motion as small as 0.25 pixels. Whole pixel motion can be detected and corrected with high accuracy. Fractional pixel motion can be detected and corrected but with less accuracy. Importantly, the algorithm accurately identified unshifted frames, which helps to prevent the introduction of errors during motion correction.