Generalized waveform methods for breast viscoelasticity mapping

描述（由申请人提供）：乳腺癌的早期诊断对于良好的临床结局至关重要。许多传统的成像方法对乳腺癌检测的灵敏度不理想，特别是在致密乳房的女性中。具有高灵敏度的方法，例如磁共振成像（MRI），具有低特异性率，导致不必要的活检。在正常的临床实践中，大多数活检病例都是假阳性，导致不必要的活检。为良性疾病进行活检可能会导致巨额费用和巨大的情绪困扰。假阳性的任何减少在患者护理和医疗保健成本方面都是重要的。因此，重要的是开发新的低成本的乳腺成像技术，具有高灵敏度和特异性。这个项目的长期目标是 是开发一种基于超声的乳腺成像技术，以提高乳腺癌诊断的特异性。为了提高特异性，需要测量与组织病理高度相关的组织参数。乳房的一个这样的参数是剪切弹性模量。迄今为止，已经开发了几种方法来映射组织的剪切弹性。在几乎所有情况下，这些方法都是基于平面剪切波在组织中传播的假设。然而，在生物软组织的复杂结构中，这一假设可能被违反，导致弹性估计和图像伪影中的错误，这可能产生假阳性或假阴性。因此，开发一种不依赖于这种假设的弹性映射技术具有重要价值。的短期目标 所提出的研究是开发一种用于乳房粘弹性成像的新方法，该方法可以与任何类型的波一起工作，并且不限于平面剪切波。所提出的方法，称为辐射力计算弹性成像（RFCE），产生定量地图的组织粘弹性。该方法使用超声辐射力（URF）在组织中引起振动，然后测量所产生的运动。这种运动不一定是平面剪切波或任何其他特定的波模式。与基于平面剪切波假设的粘弹性成像方法相比，RFCE有望产生更可靠和准确的结果。RFCE将粘弹性估计视为一个随机问题，当条件没有很好地定义时，这是一种鲁棒的方法。该项目的目标在3个具体目标中实现：（1）使用URF将一种新的计算逆问题框架扩展和实施到粘弹性映射，（2）使用实验室模型和组织样本验证逆问题框架，以及（3）初步研究-估计人体乳房肿块的粘弹性参数。第一个目标是集中在发展的计算方法估计粘弹性地图的对象从运动数据。第二个目标是优化体模和组织样本的RFCE，并为第三个目标中的人体研究做好准备。目标3的重点是评价RFCE在临床环境中识别乳腺肿块的性能。该项目的成功完成将对乳腺癌成像产生重大影响。

DESCRIPTION (provided by applicant): Early diagnosis of breast cancer is critical for favorable clinical outcomes. Many traditional imaging methods have suboptimal sensitivity for breast cancer detection, particularly in women with dense breasts. Methods with high sensitivity, such as magnetic resonance imaging (MRI), suffer from low specificity rates, resulting in unnecessary biopsies. In normal clinical practice, majority of biopsy cases turn out to be false positives, leading to unnecessary biopsies. Undergoing biopsy for benign disease can result in significant cost and great emotional distress. Any reduction in false positives can be significant in terms of patient care, and healthcare cost. It is, therefore, important to develop new low-cost breast imaging techniques with high sensitivity and specificity. The long-term goal of this project is to develop an ultrasound-based breast imaging technique to improve the diagnostic specificity in breast cancer. To improve the specificity, one needs to measure a parameter of tissue that is highly correlated to tissue pathology. One such parameter for breast is the shear elastic modulus. To date, several methods have been developed for mapping the shear elasticity of tissue. In almost all cases, these methods are based on the assumption of plane shear wave propagation in tissue. However, this assumption can be violated in the complex structure of biological soft tissues, leading to errors in elasticity estimation and image artifacts, which may produce false positives or false negatives. For this reason, developing an elasticity mapping technique that does not rely on this assumption is of significant value. The short-term goal of the proposed research is to develop a new method for viscoelasticity imaging of breast that can work with any type of wave, and not restricted to plane shear waves. The proposed method, called radiation force computed elastography (RFCE), produces quantitative map of tissue viscoelasticity. This method uses ultrasound radiation force (URF) to induce a vibration in tissue, and then measures the resulting motion. Such motion does not have to be a plane shear wave or any other particular wave mode. RFCE is anticipated to produce more reliable and accurate results than other viscoelasticity imaging methods that are based on the assumption of plane shear wave. RFCE treats viscoelasticity estimation as a stochastic problem, a robust approach when conditions are not well defined. Goal of this project are achieved in 3 Specific Aims: (1) Extension and implementation of a novel computational inverse problem framework to viscoelasticity mapping using URF, (2) Validation of the inverse problem framework using laboratory phantoms and tissue samples, and (3) Pilot Studies- Estimation of viscoelastic parameters of masses in human breast. The first Aim is focused on developing the computational method for estimating viscoelasticity map of the object from motion data. The second aim optimizes RFCE on phantoms and tissue sample and prepares it for the human study in the third aim. Aim 3 is focused on evaluating the performance of RFCE in identifying masses in breast in clinical settings. Successful completion of this project will have a significan impact in breast cancer imaging.