Joint Department of Biomedical Engineering, University of North Carolina/North Carolina State University, Chapel Hill, NC 27599, USA.
Mol Imaging. 2011 Dec;10(6):460-8.
Molecular imaging (MI) with ultrasonography relies on microbubble contrast agents (MCAs) adhering to a ligand-specific target for applications such as characterizing tumor angiogenesis. It is projected that ultrasonic (US) MI can provide information about tumor therapeutic response before the detection of phenotypic changes. One of the limitations of preclinical US MI is that it lacks a comprehensive field of view. We attempted to improve targeted MCA visualization and quantification by performing three-dimensional (3D) MI of tumors expressing αvβ3 integrin. Volumetric acquisitions were obtained with a Siemens Sequoia system in cadence pulse sequencing mode by mechanically stepping the transducer elevationally across the tumor in 800-micron increments. MI was performed on rat fibrosarcoma tumors (n = 8) of similar sizes using MCAs conjugated with a cyclic RGD peptide targeted to αvβ3 integrin. US MI and immunohistochemical analyses show high microbubble targeting variability, suggesting that individual two-dimensional (2D) acquisitions risk misrepresenting more complex heterogeneous tissues. In 2D serial studies, where it may be challenging to image the same plane repeatedly, misalignments as small as 800 microns can introduce substantial error. 3D MI, including volumetric analysis of inter- and intra-animal targeting, provides a thorough way of characterizing angiogenesis and will be a more robust assessment technique for the future of MI.
超声分子成像(MI)依赖于微泡对比剂(MCAs)与配体特异性靶标结合,用于表征肿瘤血管生成等应用。据预测,超声(US)MI 可以在检测到表型变化之前提供关于肿瘤治疗反应的信息。临床前 US MI 的局限性之一是缺乏全面的视野。我们试图通过对表达 αvβ3 整合素的肿瘤进行三维(3D)MI 来改善靶向 MCA 的可视化和定量。通过在西门子 Sequoia 系统中以机械方式在肿瘤上方以 800 微米的增量进行逐行升高来获得容积采集,采用连续脉冲序列模式。使用靶向 αvβ3 整合素的环状 RGD 肽偶联的 MCA 对大小相似的大鼠纤维肉瘤肿瘤(n = 8)进行 US MI 和免疫组织化学分析显示出高微泡靶向变异性,这表明单个二维(2D)采集有风险会错误表示更复杂的异质组织。在二维系列研究中,由于可能难以重复成像相同的平面,即使是小至 800 微米的不对准也会引入大量误差。3D MI,包括对动物间和动物内靶向的容积分析,为血管生成提供了一种全面的特征描述方法,并且将成为 MI 未来更强大的评估技术。