Generalized waveform methods for breast viscoelasticity mapping

乳腺癌的早期诊断对于良好的临床结果至关重要。许多传统的成像方法 对乳腺癌检测的灵敏度不理想，特别是在乳腺致密的女性中。方法采用 高灵敏度，如磁共振成像（MRI），具有低特异性，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， 导致不必要的活检对良性疾病进行活组织检查可能会导致巨大的成本和巨大的费用。 情绪困扰假阳性的任何减少都可以在患者护理和医疗保健方面发挥重要作用 成本因此，重要的是开发具有高灵敏度和低成本的新的乳腺成像技术。 的特异性该项目的长期目标是开发一种基于超声的乳腺成像技术， 提高乳腺癌的诊断特异性。为了提高特异性，需要测量 与组织病理学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 几乎所有的情况下，这些方法都是基于组织中平面剪切波传播的假设。 然而，在生物软组织的复杂结构中，这一假设可能被违反，从而导致错误。 弹性估计和图像伪影，这可能产生假阳性或假阴性。基于这个理由， 开发不依赖于该假设的弹性映射技术具有重要价值。 该研究的短期目标是发展一种新的乳腺粘弹性成像方法 它可以处理任何类型的波，而不限于平面剪切波。所提出的方法称为 辐射力计算弹性成像（RFCE）产生组织粘弹性的定量图。这 方法使用超声辐射力（URF）在组织中引起振动，然后测量所得的 议案这种运动不一定是平面剪切波或任何其他特定的波模式。RFCE是 预期产生比其他粘弹性成像方法更可靠和准确的结果， 基于平面剪切波假设。RFCE将粘弹性估计视为随机问题， 在条件不明确的情况下，这是一种稳健的方法。该项目的目标是在3个具体目标实现： (1)一种新的粘弹性反问题计算框架的扩展与实现 使用URF映射，（2）使用实验室模型和组织验证反问题框架 样本，和（3）初步研究-在人类乳房肿块的粘弹性参数的估计。第一个目标 着重于发展从运动估计物体粘弹性图的计算方法 数据第二个目标是优化体模和组织样本的RFCE，并为人体研究做好准备。 第三个目标。目的3：评价射频增强超声心动图在诊断乳腺肿块中的性能， 临床环境。该项目的成功完成将对乳腺癌成像产生重大影响。

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 significant impact in breast cancer imaging.