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Matthew Bruce

Principal Scientist/Engineer






B.S. Electrical and Computer Engineering, Michigan Technological University, 1991

M.S. Electrical and Computer Engineering, Virginia Polytechnic University, 1993

Ph.D. Bioengineering, University of Washington, 2004

Matthew Bruce's Website



2000-present and while at APL-UW

Sonographic features of abscess maturation in a porcine model

Lotta, D.F., M. Bruce, Y.-N. Wang, J. Kucewicz, T.K. Khokhlova, K. Chan, W. Monsky, and T.J. Matula, "Sonographic features of abscess maturation in a porcine model," Ultrasound Med. Biol., 47, 1920-1930, doi:10.1016/j.ultrasmedbio.2021.03.011, 2021.

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1 Jul 2021

Abscesses are walled-off collections of infected fluids that often develop as complications in the setting of surgery and trauma. Treatment is usually limited to percutaneous catheterization with a course of antibiotics. As an alternative to current treatment strategies, a histotripsy approach was developed and tested in a novel porcine animal model. The goal of this article is to use advanced ultrasound imaging modes to extract sonographic features associated with the progression of abscess development in a porcine model. Intramuscular or subcutaneous injections of a bi-microbial bacteria mixture plus dextran particles as an irritant led to identifiable abscesses over a 2 to 3 wk period. Selected abscesses were imaged at least weekly with B-mode, 3-D B-mode, shear-wave elastography and plane-wave Doppler imaging. Mature abscesses were characterized by a well-defined core of varying echogenicity surrounded by a hypoechoic capsule that was highly vascularized on Doppler imaging. 3-D imaging demonstrated the natural history of abscess morphology, with the abscess becoming less complex in shape and increasing in volume. Furthermore, shear-wave elastography demonstrated variations in stiffness as phlegmon becomes abscess and then liquefies, over time. These ultrasound features potentially provide biomarkers to aid in selection of treatment strategies for abscesses.

Super-resolution ultrasound localization microscopy through deep learning

van Sloun, R.J.G., O. Solomon, M. Bruce, Z.Z. Khaing, H. Wijkstra, Y.C. Eldar, and M. Mischi, "Super-resolution ultrasound localization microscopy through deep learning," IEEE Trans. Med. Imaging, 40, 829-838, doi:10.1109/TMI.2020.3037790, 2021.

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1 Mar 2021

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultrasound imaging data. This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios, learning the nonlinear image-domain implications of overlapping RF signals originating from such sets of closely spaced microbubbles. Deep-ULM is trained effectively using realistic on-line synthesized data, enabling robust inference in-vivo under a wide variety of imaging conditions. We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is suitable for real-time applications, resolving about 70 high-resolution patches (128 x 128 pixels) per second on a standard PC. Exploiting GPU computation, this number increases to 1250 patches per second.

Treating porcine abscesses with histotripsy: A pilot study

Matula, T.J., Y.-N. Wang, T. Khokhlova, D.F. Leotta, J. Kucewicz, A.A. Brayman, M. Bruce, A.D. Maxwell, B.E. MacConaghy, G. Thomas, V.P. Chernikov, S.V. Buravkov, V.A. Khokhlova, K. Richmond, K. Chan, W. Monsky, "Treating porcine abscesses with histotripsy: A pilot study," Ultrasound Med. Biol., 47, 603-619, doi:10.1016/j.ultrasmedbio.2020.10.011, 2021.

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1 Mar 2021

Infected abscesses are walled-off collections of pus and bacteria. They are a common sequela of complications in the setting of surgery, trauma, systemic infections and other disease states. Current treatment is typically limited to antibiotics with long-term catheter drainage, or surgical washout when inaccessible to percutaneous drainage or unresponsive to initial care efforts. Antibiotic resistance is also a growing concern. Although bacteria can develop drug resistance, they remain susceptible to thermal and mechanical damage. In particular, short pulses of focused ultrasound (i.e., histotripsy) generate mechanical damage through localized cavitation, representing a potential new paradigm for treating abscesses non-invasively, without the need for long-term catheterization and antibiotics. In this pilot study, boiling and cavitation histotripsy treatments were applied to subcutaneous and intramuscular abscesses developed in a novel porcine model. Ultrasound imaging was used to evaluate abscess maturity for treatment monitoring and assessment of post-treatment outcomes. Disinfection was quantified by counting bacteria colonies from samples aspirated before and after treatment. Histopathological evaluation of the abscesses was performed to identify changes resulting from histotripsy treatment and potential collateral damage. Cavitation histotripsy was more successful in reducing the bacterial load while having a smaller treatment volume compared with boiling histotripsy. The results of this pilot study suggest focused ultrasound may lead to a technology for in situ treatment of acoustically accessible abscesses.

More Publications


Improved Detection of Kidney Stones with Ultrasound

Record of Invention Number: 47629

Matthew Bruce


19 Feb 2016

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center