Sra Jasbir S, Soubelet Elisabeth, Vaillant Regis, Krum David, Hare John, Belanger Barry, Choudhuri Indrajit, Dhala Anwer, Nangia Vikram, Mortada M Eyman, Bhatia Atul, Blanck Zalmen, Cooley Ryan, Akhtar Masood
Aurora Cardiovascular Services, Aurora Sinai/Aurora St. Luke's Medical Centers, University of Wisconsin School of Medicine and Public Health, Milwaukee, WI.
GE Healthcare, Buc, France.
J Atr Fibrillation. 2011 Feb 22;3(5):250. doi: 10.4022/jafib.250. eCollection 2011 Feb-Apr.
Dynamic motion of the heart due to cardiac and respiratory cycles, and rotation from varying patient positions between imaging modalities, can cause errors during cardiac image registration. This study used phantom, patient and animal models to assess and correct these errors. Rotational errors were identified and corrected using different phantom orientations. ECG-gated fluoro images were aligned with similarly gated CT images in 9 patients, and accuracy assessed during atrial fibrillation (AF) and sinus rhythm. A tracking algorithm corrected errors due to respiration; 4 independent observers compared 25 respiration sequences to an automated method. Following correction of these errors, target registration error was assessed. At 20 mm and 30 mm from the phantom model's center point with an in-plane rotation of 8 degrees, measured error was 2.94 mm and 5.60 mm, respectively, and the main error identified. A priori method accurately predicted ECG location in only 38% (p=0.0003) of 313 R-R intervals in AF. A posteriori method accurately gated the ECG during AF and sinus rhythm in 97% and 98% of 375 beats evaluated, respectively (p=NS). Tracking algorithm for ECG-gated motion compensation was identified as good or fair 96% of the time, with no difference between observers and automated method (chi-square=25; p=NS). Target registration error in phantom and animal models was 1.75±1.03 mm and 0 to 0.5 mm, respectively. Errors during cardiac image registration can be identified and corrected. Cardiac image stabilization can be achieved using ECG gating and respiration.
由于心脏和呼吸周期导致的心脏动态运动,以及成像模态之间患者不同体位的旋转,可能会在心脏图像配准过程中产生误差。本研究使用体模、患者和动物模型来评估并校正这些误差。通过不同的体模方向识别并校正旋转误差。对9例患者的心电图门控荧光图像与同样门控的CT图像进行配准,并在房颤(AF)和窦性心律期间评估准确性。一种跟踪算法校正了呼吸引起的误差;4名独立观察者将25个呼吸序列与一种自动化方法进行比较。校正这些误差后,评估目标配准误差。在体模模型中心点20毫米和30毫米处,面内旋转8度时,测量误差分别为2.94毫米和5.60毫米,这是识别出的主要误差。先验方法在房颤的313个R-R间期内仅38%(p = 0.0003)能准确预测心电图位置。后验方法在房颤和窦性心律期间分别在97%和98%的375次心跳评估中准确门控心电图(p = 无显著性差异)。心电图门控运动补偿的跟踪算法在96%的时间内被判定为良好或尚可,观察者与自动化方法之间无差异(卡方检验=25;p = 无显著性差异)。体模和动物模型中的目标配准误差分别为1.75±1.03毫米和0至0.5毫米。心脏图像配准过程中的误差可以被识别并校正。使用心电图门控和呼吸可以实现心脏图像稳定。