Suppression and Analysis of Ultrasonic Clutter During Liver Focal Lesion Biopsy

 描述（由申请人提供）：超声成像是美国使用最广泛的先进成像模式，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，超声图像通常是次优的，并且一些研究显示高达98%的患者的退化。临床超声的退化率和徒手性质意味着图像质量比其他先进模式更依赖于用户技能，但即使在最熟练的从业人员手中，超声成像也有11-64%的临床任务完全失败。当在引导任务（如肝局灶性病变活检）期间遇到无效超声时，问题尤其严重。超声通常被认为是常规活检引导的最佳方式，但当超声成像失败时，临床医生必须使用其他效率较低且更昂贵的方式。最后， 肿瘤活检几乎对每个患者都是诊断性的;然而，超过72%的活检需要多次穿刺，单个诊断样本需要4或5次穿刺并不罕见。更高质量的超声成像可以改善这一过程，这对公共卫生非常重要，因为它可以使临床工作更有效，更安全，并支持关键的 个性化癌症治疗的临床研究。为了解决病变活检超声引导过程中可视化差的挑战，我们引入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。具体来说，我们对超声波在转换成图像之前从多个结构反射的退化以及从极端反射结构反射的波的退化进行了建模。当我们对这些源进行建模并将其删除时，所产生的图像在数量和质量上都优于未校正的图像。我们最初的图像退化模型有一些缺点，我们作为本提案的一部分来解决。我们还在开发我们算法的实时实现，并在一项小型临床研究中评估我们方法的实用性。最后，我们把重点放在肝脏超声活检引导这项建议，但我们的方法是广泛有用的超声成像一般，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.