Learning the Manifold of Quality Ultrasound Acquisition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Noha El-Zehiry , Michelle Yan , Sara Good , Tong Fang , S. Kevin Zhou , Leo Grady

ABSTRACT

Ultrasound acquisition is a challenging task that requires simultaneous adjustment of several acquisition parameters (the depth, the focus, the frequency and its operation mode). If the acquisition parameters are not properly chosen, the resulting image will have a poor quality and will degrade the patient diagnosis and treatment workflow. Several hardware-based systems for autotuning the acquisition parameters have been previously proposed, but these solutions were largely abandoned because they failed to properly account for tissue inhomogeneity and other patient-specific characteristics. Consequently, in routine practice the clinician either uses population-based parameter presets or manually adjusts the acquisition parameters for each patient during the scan. In this paper, we revisit the problem of autotuning the acquisition parameters by taking a completely novel approach and producing a solution based on image analytics. Our solution is inspired by the autofocus capability of conventional digital cameras, but is significantly more challenging because the number of acquisition parameters is large and the determination of “good quality” images is more difficult to assess. Surprisingly, we show that the set of acquisition parameters which produce images that are favored by clinicians comprise a 1D manifold, allowing for a real-time optimization to maximize image quality. We demonstrate our method for acquisition parameter autotuning on several live patients, showing that our system can start with a poor initial set of parameters and automatically optimize the parameters to produce high quality images. More... »

PAGES

122-130

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-40811-3_16

DOI

http://dx.doi.org/10.1007/978-3-642-40811-3_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049687045

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24505657


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