title: Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System.
abstract: A system for real-time ultrasound (US) elastography will advance interventions for the diagnosis and treatment of cancer by advancing methods such as thermal monitoring of tissue ablation. A multi-stream graphics processing unit (GPU) based accelerated normalized cross-correlation (NCC) elastography, with a maximum frame rate of 78 frames per second, is presented in this paper. A study of NCC window size is undertaken to determine the effect on frame rate and the quality of output elastography images. This paper also presents a novel system for Online Tracked Ultrasound Elastography (O-TRuE), which extends prior work on an offline method. By tracking the US probe with an electromagnetic (EM) tracker, the system selects in-plane radio frequency (RF) data frames for generating high quality elastograms. A novel method for evaluating the quality of an elastography output stream is presented, suggesting that O-TRuE generates more stable elastograms than generated by untracked, free-hand palpation. Since EM tracking cannot be used in all systems, an integration of real-time elastography and the da Vinci Surgical System is presented and evaluated for elastography stream quality based on our metric. The da Vinci surgical robot is outfitted with a laparoscopic US probe, and palpation motions are autonomously generated by customized software. It is found that a stable output stream can be achieved, which is affected by both the frequency and amplitude of palpation. The GPU framework is validated using data from in-vivo pig liver ablation; the generated elastography images identify the ablated region, outlined more clearly than in the corresponding B-mode US images.
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Nishikant P
authors.initials: NP
authors.lastname: Deshmukh
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Hyun Jae
authors.initials: HJ
authors.lastname: Kang
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Seth D
authors.initials: SD
authors.lastname: Billings
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Russell H
authors.initials: RH
authors.lastname: Taylor
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Hager
authors.affiliation: Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Radiology, The Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America.
authors.forename: Emad M
authors.initials: EM
authors.lastname: Boctor
mesh_terms: D000465:Algorithms; D000818:Animals; D054459:Elasticity Imaging Techniques / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010535:Laparoscopy / Q000379:methods; D008099:Liver / Q000473:pathology / Q000601:surgery*; D019047:Phantoms, Imaging; D012984:Software; D013552:Swine