Development of Motion-Model Ultrasound Localization Microscopy to Support Breast Cancer Diagnosis and Therapy Monitoring in Patients

开发运动模型超声定位显微镜以支持乳腺癌诊断和患者治疗监测

• 批准号: 233312120
• 负责人: Professor Dr. Fabian Kiessling
• 金额: --
• 依托单位: Institut für Experimentelle Molekulare Bildgebung (ExMI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/233312120?language=en
• 关键词: Development; Motion; Model; Ultrasound; Localization

Ultrasound imaging is routinely applied in breast cancer screening and for the dignity assessment of suspect lesions. Supported by the DFG, we developed a super-resolution contrast-enhanced ultrasound method called motion model ultrasound localization microscopy (mULM), which tracks individual microbubbles in video sequences and generates images of the vasculature at a resolution beyond the diffraction. The mULM method enables an accurate assessment of the vascular architecture in tumors, individual blood vessel velocities and flow directions, and the quantification of relative blood volume and perfusion. Recently, we showed in mouse xenograft tumors that this method allows to extract various morphometric parameters and combined with a radiomics analysis enables the automated discrimination of different tumor types. In this project, we aim to adapt and develop mULM and ultrasound-based radiomics for the application in patients and to advance the technique to 3D acquisition and tracking. In a preliminary analysis of clinical datasets, we were already able to extract super-resolution data of vascular tracks.However, several technical challenges for the robust application in a clinical setting emerged: in particular, patient motion and the lower image resolution together with increased slice thickness and non-optimized injection protocols allowed only to use short image sequences and to extract a reduced number of tracks. Thus, in an initial step, the mULM methodology will be refined and adapted for its application in breast imaging on clinical ultrasound devices. Subsequently, the 2D mULM method will be applied to monitor breast cancers’ responses to neoadjuvant chemotherapy. Its accuracy will be compared to conventional contrast-enhanced ultrasound data analyses. Furthermore, we strive to advance the method from 2D to full 3D acquisition with a real-time volumetric scanner and compare both approaches in a small cohort of patients with breast tumors of different dignity. To exploit the full potential of mULM data analysis, algorithms will be developed and implemented that allow the automated extraction and clustered analysis of the vascular features. Measurements of similarity as well as cluster analysis will be performed to automatically group the patients. Thus, this project will be a first contribution to the clinical translation of multiparametric super-resolution ultrasound and open the way for ultrasound-based radiomics analysis.

超声成像通常用于乳腺癌筛查和可疑病变的尊严评估。在DFG的支持下，我们开发了一种称为运动模型超声定位显微镜（MULM）的超分辨率对比增强超声方法，该方法在视频序列中跟踪单个微泡，并以衍射范围以外的分辨率生成脉管系统的图像。 MULM方法可以准确评估肿瘤，单个血管速度和流动方向的血管结构以及相对血容量和灌注的定量。最近，我们在小鼠异种移植肿瘤中表明，该方法允许提取各种形态学参数，并与放射线分析结合使用，从而可以自动歧视不同的肿瘤类型。在这个项目中，我们旨在适应和开发基于MULM和超声的放射组，以用于患者的应用，并将该技术推向3D获取和跟踪。在对临床数据集的初步分析中，我们已经能够提取血管轨道的超分辨率数据。但是，在出现的临床环境中，出现了鲁棒应用的几个技术挑战：特别是，尤其是患者运动和下图像分辨率以及更高的切片厚度和不可限制的点火方案，仅允许使用短图像序列并仅允许使用短图像序列并提取轨道数量的短图像。在第一步中，MULM方法将被完善并适应其在乳房成像中在临床超声设备上的应用。随后，将使用2D MULM方法来监测乳腺癌对新辅助化疗的反应。它的精度将与常规增强的超声数据分析进行比较。此外，我们努力通过实时体积扫描仪将该方法从2D提高到完整的3D获取，并比较了一小部分具有不同乳房乳腺肿瘤的患者中的两种方法。为了探索MULM数据分析的全部潜力，将开发和实施算法，以允许对血管特征进行自动提取和聚类分析。将进行相似性和聚类分析的测量，以自动分组患者。这是对多组合超分辨率超声的临床翻译的第一贡献，并为基于超声的放射组学分析开辟了道路。