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An Ultrasound Image-Based Dynamic Fusion Modeling Method for Predicting the Quantitative Impact of In Vivo Liver Motion on Intraoperative HIFU Therapies: Investigations in a Porcine Model

机译:基于超声图像的动态融合建模方法用于预测体内肝脏运动对术中HIFU治疗的量化影响:在猪模型中的研究

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摘要

Organ motion is a key component in the treatment of abdominal tumors by High Intensity Focused Ultrasound (HIFU), since it may influence the safety, efficacy and treatment time. Here we report the development in a porcine model of an Ultrasound (US) image-based dynamic fusion modeling method for predicting the effect of in vivo motion on intraoperative HIFU treatments performed in the liver in conjunction with surgery. A speckle tracking method was used on US images to quantify in vivo liver motions occurring intraoperatively during breathing and apnea. A fusion modeling of HIFU treatments was implemented by merging dynamic in vivo motion data in a numerical modeling of HIFU treatments. Two HIFU strategies were studied: a spherical focusing delivering 49 juxtapositions of 5-second HIFU exposures and a toroidal focusing using 1 single 40-second HIFU exposure. Liver motions during breathing were spatially homogenous and could be approximated to a rigid motion mainly encountered in the cranial-caudal direction (f = 0.20Hz, magnitude >13mm). Elastic liver motions due to cardiovascular activity, although negligible, were detectable near millimeter-wide sus-hepatic veins (f = 0.96Hz, magnitude <1mm). The fusion modeling quantified the deleterious effects of respiratory motions on the size and homogeneity of a standard “cigar-shaped” millimetric lesion usually predicted after a 5-second single spherical HIFU exposure in stationary tissues (Dice Similarity Coefficient: DSC<45%). This method assessed the ability to enlarge HIFU ablations during respiration, either by juxtaposing “cigar-shaped” lesions with spherical HIFU exposures, or by generating one large single lesion with toroidal HIFU exposures (DSC>75%). Fusion modeling predictions were preliminarily validated in vivo and showed the potential of using a long-duration toroidal HIFU exposure to accelerate the ablation process during breathing (from 0.5 to 6 cm3·min-1). To improve HIFU treatment control, dynamic fusion modeling may be interesting for assessing numerically focusing strategies and motion compensation techniques in more realistic conditions.
机译:器官运动是通过高强度聚焦超声(HIFU)治疗腹部肿瘤的关键因素,因为它可能影响安全性,疗效和治疗时间。在这里,我们报告基于超声(美国)图像的动态融合建模方法在猪模型中的发展,该方法用于预测体内运动对肝脏结合手术进行的术中HIFU治疗的影响。在US图像上使用斑点跟踪方法来量化呼吸和呼吸暂停期间术中发生的体内肝脏运动。通过在HIFU治疗的数值模型中合并动态体内运动数据,实现了HIFU治疗的融合模型。研究了两种HIFU策略:球形聚焦可提供49个并置的5秒HIFU曝光,以及使用1次单个40秒HIFU曝光的环形聚焦。呼吸过程中的肝脏运动在空间上是同质的,可以近似于主要在颅尾方向上遇到的刚性运动(f = 0.20Hz,幅度> 13mm)。尽管可以忽略不计,但由于心血管活动引起的弹性肝运动可在毫米宽的苏肝静脉附近检测到(f = 0.96Hz,幅度<1mm)。融合模型量化了呼吸运动对标准“雪茄形”毫米病变的大小和均匀性的有害影响,这种病变通常在固定组织中单次球形HIFU暴露5秒后预测(骰子相似系数:DSC <45%)。该方法通过将“雪茄状”病灶与球形HIFU暴露并置,或通过产生一个大的单个病灶与环形HIFU暴露(DSC> 75%)来评估在呼吸过程中扩大HIFU消融的能力。融合模型的预测已在体内得到初步验证,并显示了使用长时间环形HIFU暴露来加速呼吸过程中消融过程(从0.5 cm减至6 cm 3 ·min -1 < / sup>)。为了改善HIFU治疗控制,动态融合建模可能对于在更现实的条件下评估数值聚焦策略和运动补偿技术很有用。

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