Suppression and Analysis of Ultrasonic Clutter During Liver Focal Lesion Biopsy

 描述(申请人提供)：超声波成像是美国使用最广泛的高级成像手段，不包括基本的x射线检查，它占所有成像研究的44%。不幸的是，超声图像往往是亚光学的，一些研究表明，高达98%的患者病情恶化。临床超声的退化速度和徒手性质意味着，与其他先进医疗设备相比，图像质量更依赖于用户技能，但即使在最熟练的从业者手中，超声成像也在11-%的临床任务中完全失败。当遇到像肝脏局灶性病变活检这样的指导任务时，无效的超声尤其成问题。超声通常被认为是常规活检指导的最佳方式，但当超声成像失败时，临床医生必须使用其他fi效果较差和更昂贵的方式。最终， 肿瘤活检在几乎每个患者中都是诊断性的；然而，超过72%的活检需要多次穿针，对于单个诊断样本进行四次或fiVe遍并不少见。更高质量的超声成像可以改善这一过程，这对公共健康很重要，因为它使临床工作更有效、更安全，并支持至关重要的 个性化癌症治疗的临床研究。为了解决超声引导下病变活检可视化差的问题，我们介绍了一种新的先进的超声成像方法。我们的方法模拟了几种导致退化的机制，然后从超声信号中去除这些退化成分。具体地说，我们模拟了超声波在将波转变为图像之前反射多个结构所发生的退化，以及反射极强反射结构的波所产生的退化。当我们对这些源进行建模并去除它们时，得到的图像在数量和质量上都比未经校正的图像要好。我们最初的图像退化模型有一些缺陷，我们将其作为本提案的一部分加以解决。我们还在开发我们的算法的实时实现，并在一项小型临床研究中评估我们方法的实用性。最后，我们将重点放在肝脏的超声活检指导上，但我们的方法在总体上对超声成像非常有用，并将对公众健康产生深远的影响。

 DESCRIPTION (provided by applicant): Ultrasonic imaging is the most widely used advanced imaging modality in the United States, and excluding basic x-ray exams, it represents 44% of all imaging studies. Unfortunately, ultrasound images are often subopti- mal, and some studies show degradation in up to 98% of patients. The rate of degradation and freehand nature of clinical ultrasound means that image quality is more dependent on user skill than other advanced modalities, but even in the hands of the most skilled practitioners, ultrasound imaging completely fails in 11-64% of clinical tasks. Ineffective ultrasound is particularly problematic when encountered during guidance tasks like hepatic focal lesion biopsy. Ultrasound is generally considered the best modality for routine biopsy guidance, but when ultrasound imaging fails, clinicians must use other less efficient and more expensive modalities. Ultimately, tumor biopsies are diagnostic in almost every patient; however, more than 72% of biopsies require multiple needle passes, and four or five passes for a single diagnostic sample is not uncommon. Better quality ultrasound imaging can improve this process, which is important for public health because it enables more efficient and safer clinical workflow, and supports crucial clinical studies of personalized cancer treatments. In order to address the challenge of poor visualization during ultrasound guidance of lesion biopsy, we have introduced a new advanced ultrasound image-formation method. Our approach models several mechanisms that causes degradation, and then removes those degrading components from the ultrasound signal. Specifically, we model the degradation that occurs from ultrasound waves that reflect off multiple structures before the waves are turned into an image and degradation from waves that reflect off of extremely reflective structures. When we model for these sources and remove them, the resulting images are quantitatively and qualitatively better than uncorrected images. Our initial model of image degradation had some shortcomings, which we address as part of this proposal. We are also developing a real-time implementation of our algorithm, and assessing the utility of our methods in a small clinical study. Finally, we focus on ultrasound biopsy guidance in the liver for this proposal, but our methods are broadly useful to ultrasound imaging in general and will have far reaching impacts to public health.