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Suppression and analysis of ultrasonic clutter during liver focal lesion biopsy
Title:
Suppression and analysis of ultrasonic clutter during liver focal lesion biopsy
Project Number:
5R01EB020040-04
Contract PI/Project Lead:
Brett Byram
Award Organization:
National Institutes of Health
Abstract:
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.