A New Method for Improved Targeting in Image-guided Abdominal Interventions

描述(由申请人提供)： 这是一份R01拨款申请，为期五年，用于应用新的先进图像分析技术，并引入将改善CT引导干预的靶向性的技术。CT目前在美国是用于指导活检和肿瘤消融的最常见的成像方式。大多数肝肿瘤，如肝细胞癌，在手术前获得的增强CT或MRI上均可见。然而，在手术过程中获得的平扫CT图像上，这些肿瘤可能看不到或边缘不明显。这可能会增加手术时间，和/或导致非诊断性细胞病理学评估，需要重复活检或次理想的消融敷贴器位置。我们的目标是通过将高分辨率的术前图像与低质量的术中图像配准来增强介入放射科医生当前可用的信息。术前图像上可见的解剖细节(包括肿瘤边缘)将叠加在术中图像上。因此，消融敷贴器和活检针的准确位置将根据术前扫描显示的肿瘤的真实轮廓进行估计。非刚性配准被证明是一种即使在存在器官运动的情况下也能以良好的精度实现图像对齐的技术。然而，到目前为止，它还没有被用于临床套件中CT引导的介入治疗过程中的术前和术中数据融合，因为它需要大量的计算基础设施，而且通常这些方法不够健壮。我们开发了一种新的基于生物力学模型的非刚性配准方法，并在图像引导神经外科领域的前瞻性研究中得到了验证。最近，这项新技术也在RFA患者的回顾数据中得到了验证。术前MRI和术中平扫CT图像在5分钟内对齐，精度为2 mm。这项提议旨在证明这项技术在介入放射学套件中的可行性，在不影响医疗决策的情况下进行图像引导的腹部介入治疗。我们对活检和消融的预测将与手术后MRI上的组织学结果和消融面积进行比较。在评估结果时，将采用数学方法和由两名独立放射科医生对结果进行目测检查。非刚性配准算法的准确性、健壮性、执行时间将决定配准的成败。这项建议将通过开发和评估关键技术来增强图像引导活检和消融期间的导航，从而使公众健康受益。在整个手术过程中显示肿瘤和肿瘤边缘的能力将使介入放射科医生能够更好地获得更好的诊断，并对整个肿瘤肿块进行凝固性坏死，而不会出现意外的部位影响。 公共卫生相关性： 这项工作将为CT引导的介入治疗带来精确靶向的新技术。这些技术将有助于更好地诊断和治疗肝脏肿瘤，使介入放射科医生能够更准确地放置(I)活检针和(Ii)消融敷贴器，因此，它们可以(I)提高即使是微小肿瘤的检测特异性，(Ii)实现肿瘤完全消融，而切除邻近肿瘤的健康肝组织的风险较小。

DESCRIPTION (provided by applicant): This is an R01 grant application for five years of funding to apply novel advanced image analysis techniques and introducing technology that would improve the targeting in CT guided interventions. CT is currently in the United States the most common imaging modality used to guide biopsy and tumor ablation. The majority of liver tumors such as hepatocellular carcinomas are visible on contrast-enhanced CT or MRI obtained prior to the procedure. Yet, these tumors may not be seen or may have poor margin conspicuity on unenhanced CT images acquired during the procedure. This may increase the procedure time, and/or lead to non-diagnostic cytopathologic assessment, requiring repeat biopsy or sub-optimal ablation applicator placement. We aim to enhance the current information available to the interventional radiologist, by registering the high-resolution pre-procedural images with low quality intra-procedural images. Anatomical details (including the tumor margin) visible on the pre-procedural images will be superimposed on the intra- procedural images. The precise position of ablation applicator and biopsy needle would be thus estimated with respect to the real contour of the tumor as appearing on the pre-procedural scan. Non-rigid registration is demonstrated as a technology to achieve alignment of images with good accuracy, even in the presence of organ motion. However, up to date it has not been used for fusing pre- and intra- procedural data during CT guided interventions in a clinical suite, since it requires significant computational infrastructure and often these methods are not sufficient robust. We have developed a new non-rigid registration method based on biomechanical model, validated in a prospective study in the area of image-guide neurosurgery. Recently, this new technique is also validated on retrospective data from RFA patients. Pre-procedural MRI and unenhanced intra-procedural CT images are aligned within 5 minutes, with 2mm accuracy. This proposal aims to demonstrate the feasibility of this technology in an interventional radiology suite during image-guided abdominal interventions, without impact on the medical decision. Our predictions for biopsy and ablation will be compared with the histological findings and ablated area on post- procedural MRI. Both mathematical methods and visual inspection by two independent radiologists of results will be employed in assessing the results. The success will be determined by the accuracy, robustness, execution time of the non-rigid registration algorithms. This proposal will benefit public health by developing and assessing key technologies to enable enhanced navigation during the image guided biopsy and ablation. The capacity to visualize the tumor and tumor margin throughout the procedure will better enable the interventional radiologists to achieve better diagnosis, and coagulation necrosis for the entire tumor mass, without unexpected site effects. PUBLIC HEALTH RELEVANCE: This work will lead to new technologies for precise targeting in CT guided interventions. These technologies will facilitate better diagnosis and treatment of liver neoplasms, by enabling the interventional radiologists to more accurately place (i) the biopsy needle and (ii) the ablation applicator, and so, they can (i) improve the specificity in detecting even small tumors and (ii) achieve complete tumor ablation with less risk of removing healthy liver tissue located adjacent to the tumor.