呼吸运动对SPECT成像中肺小结节检测的影响。

M S Smyczynski, H C Gifford, A Lehovich, J E McNamara, W P Segars, B M W Tsui, M A King
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引用次数: 7

摘要

本研究的目的是确定呼吸运动对单光子发射计算机断层扫描(SPECT)小孤立性肺结节(SPN)检测的影响。我们之前已经根据志愿者在两个不同的呼吸阶段获得的屏气CT图像所识别的肺内解剖结构位置的变化来模拟SPN的呼吸运动。将肺内呼吸运动的信息与终末呼气和时间平均NCAT幻象相结合,以创建Tc-99m NeoTect正态背景分布的源和衰减图。有了这样定义的源和衰减分布,SIMIND蒙特卡罗程序被用来生成正常背景下的SPECT投影数据,并分别为150个终末和时间平均模拟的1.0 cm肿瘤生成SPECT投影数据。正常和肿瘤SPECT投影集每包含一个病变与临床真实的噪声水平和计数相结合。利用RBI-EM分别采用1)无校正(NC)、2)衰减校正(AC)、3)探测器响应校正(RC)和4)衰减校正、探测器响应校正和散射校正(AC_RC_SC)对这些数据进行重构。通过人观测器的研究,优化了重建后迭代次数参数和三维高斯滤波参数。比较人类观察者LROC研究的病变检测结果表明,呼吸运动降低了所有四种重建策略的肿瘤检测,并且这种影响在NC和RC中最大,在AC_RC_SC中最小。此外,AC_RC_SC策略对病变的检测效果最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of respiratory motion on the detection of small pulmonary nodules in SPECT imaging.

The objective of this investigation is to determine the impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging. We have previously modeled the respiratory motion of SPN based on the change of location of anatomic structures within the lungs identified on breath-held CT images of volunteers acquired at two different stages of respiration. This information on respiratory motion within the lungs was combined with the end-expiration and time-averaged NCAT phantoms to allow the creation of source and attenuation maps for the normal background distribution of Tc-99m NeoTect. With the source and attenuation distribution thus defined, the SIMIND Monte Carlo program was used to produce SPECT projection data for the normal background and separately for each of 150 end-expiration and time-averaged simulated 1.0 cm tumors. Normal and tumor SPECT projection sets each containing one lesion were combined with a clinically realistic noise level and counts. These were reconstructed with RBI-EM using 1) no correction (NC), 2) attenuation correction (AC), 3) detector response correction (RC), and 4) attenuation correction, detector response correction, and scatter correction (AC_RC_SC). The post-reconstruction parameters of number of iterations and 3-D Gaussian filtering were optimized by human-observer studies. Comparison of lesion detection by human-observer LROC studies reveals that respiratory motion degrades tumor detection for all four reconstruction strategies, and that the magnitude of this effect is greatest for NC and RC, and least for AC_RC_SC. Additionally, the AC_RC_SC strategy results in the best detection of lesions.

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