Brian W. Anthony

B. W. Anthony is with the Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA, and also the Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA. Member, IEEE.

Associated articles

TBME, Featured Articles
Real-Time Blood Pressure Estimation From Force-Measured Ultrasound
Common techniques to measure blood pressure include an arterial catheter, an oscillometric pressure cuff, or an auscultatory pressure cuff. Oscillometric and auscultatory cuffs have an associated error, cannot be used continuously, and fully collapse the brachial artery. An auscultatory cuff... Read more
TNSRE, Featured Articles
3D Ultrasound Imaging of Residual Limbs With Camera-Based Motion Compensation
      Ultrasound is a cost-effective, readily available, and non-ionizing modality for musculoskeletal imaging. Though some research groups have pursued methods that involve submerging the transducer and imaged body segment into a water bath, many limitations remain in regards to acquiring an... Read more
TBME, Featured Articles
A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound
Abnormalities in the tissue’s mechanical properties and structure, as well as their spatial arrangement, are useful in disease diagnosis and monitoring of disease progression. To this end, ultrasound shear wave elastography is gaining traction as a useful diagnostic tool for... Read more
TBME,
A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound
Shear wave elastography constitutes the deployed state of the art for assessing mechanical properties of tissue, with numerous diagnostic applications. It is however limited to high-end hardware due to power requirements, is sensitive to sonographer and patient motion, and suffers from low frame rates. The longitudinal speed of sound maps enable an alternate approach for assessing the elastic properties of tissue. In this work, we present a single-sided speed of sound inversion solution using a fully convolutional deep neural network. We show that this presents a viable solution for inverting raw ultrasound channel data capable of interactive frame rates... Read more