scholarly journals In vivo 3-D vector velocity estimation with continuous data

2015 ◽  
Author(s):  
Simon Holbek ◽  
Michael Johannes Pihl ◽  
Caroline Ewertsen ◽  
Michael Bachmann Nielsen ◽  
Jorgen Arendt Jensen
Keyword(s):  
Velocity Estimation ◽  
Continuous Data ◽  
Vector Velocity

Examples of In Vivo Blood Vector Velocity Estimation

2007 ◽  
Vol 33 (4) ◽  
pp. 541-548 ◽  
Author(s):  
Jesper Udesen ◽  
Michael Bachmann Nielsen ◽  
Kristina Rue Nielsen ◽  
Jorgen Arendt Jensen
Keyword(s):  
Velocity Estimation ◽  
Vector Velocity

scholarly journals Vector velocity estimation of blood flow – A new application in medical ultrasound

Ultrasound ◽  
2017 ◽  
Vol 25 (4) ◽  
pp. 189-199 ◽  
Author(s):  
Kristoffer Lindskov Hansen ◽  
Michael Bachmann Nielsen ◽  
Jørgen Arendt Jensen
Keyword(s):  
Blood Flow ◽  
Clinical Applications ◽  
Velocity Estimation ◽  
Medical Ultrasound ◽  
Clinical Papers ◽  
Pulse Emission ◽  
Vector Velocity ◽  
The Future ◽  
Flow Techniques

Vector flow techniques in the field of ultrasound encompass different pulse emission and estimation strategies. Numerous techniques have been introduced over the years, and recently commercial implementations usable in the clinic have been made. A number of clinical papers using different vector velocity approaches have been published. This review will give an overview of the most significant in vivo results achieved with ultrasound vector flow techniques, and will outline some of the possible clinical applications for vector velocity estimation in the future.

Comparison of Real-Time In Vivo Spectral and Vector Velocity Estimation

2012 ◽  
Vol 38 (1) ◽  
pp. 145-151 ◽  
Author(s):  
Mads Møller Pedersen ◽  
Michael Johannes Pihl ◽  
Per Haugaard ◽  
Jens Munk Hansen ◽  
Kristoffer Lindskov Hansen ◽  
...  
Keyword(s):  
Real Time ◽  
Velocity Estimation ◽  
Vector Velocity

scholarly journals FPGA Implementation of a Single Step MFCV Estimator Based on EMG in Diabetic Neuropathy

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Diabetic Neuropathy ◽  
Real Time ◽  
Muscle Fibre ◽  
Diagnostic System ◽  
Correlation Method ◽  
Single Step ◽  
Velocity Estimation ◽  
In Vivo Test ◽  
Starting Point

This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and <2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature.

In vivo comparison of three ultrasound vector velocity techniques to MR phase contrast angiography

Ultrasonics ◽  
2009 ◽  
Vol 49 (8) ◽  
pp. 659-667 ◽  
Author(s):  
K.L. Hansen ◽  
J. Udesen ◽  
N. Oddershede ◽  
L. Henze ◽  
C. Thomsen ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Vector Velocity ◽  
Contrast Angiography

Feasibility of pulse wave velocity estimation from low frame rate US sequences in vivo

2017 ◽  
Author(s):  
Maria Zontak ◽  
Matthew Bruce ◽  
Michelle Hippke ◽  
Alan Schwartz ◽  
Matthew O'Donnell
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Velocity Estimation ◽  
Frame Rate ◽  
Low Frame Rate
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