Biomedical subjects

Siddhartha Sikdar

Publications and source records attributed to Siddhartha Sikdar.

4 recordsLinked to original sources

PubMed2006

Ultrasonic interrogation of tissue vibrations in arterial and organ injuries: preliminary in vivo results.

Soft tissues surrounding vascular injuries are known to vibrate at audible and palpable frequencies, producing bruits and thrills. We report the results of a feasibility study where Doppler ultrasound (US) was used to quantitatively estimate the tissue vibrations after induced trauma in an animal model. A software-programmable US system was used to acquire quadrature-demodulated ensembles of received US echoes bypassing clutter filtering and other conventional Doppler processing stages. The waveforms of tissue velocity surrounding the injury site were then estimated from the clutter data using autocorrelation and analyzed to determine vibration characteristics. Six New Zealand white rabbits and two juvenile pigs were used for the study. The femoral artery of the anesthetized animal was punctured with an 18-gauge needle to model a peripheral arterial trauma, and the liver was surgically exposed and incised to model organ trauma. Two types of oscillatory tissue motion were observed: "vibrations" with high frequency (>50 Hz) and low peak-peak amplitude (<1 microm) and "flutter" with low frequency (<50 Hz) and high peak-peak amplitude (>1 microm). Active bleeding in femoral artery punctures produced tissue vibrations at the frequency of 323 +/- 214 Hz (mean +/- standard deviation, pooled for both rabbits and pigs) and the amplitude of 0.24 +/- 0.15 microm. Active bleeding in liver incisions produced vibrations at the frequency of 120 +/- 47 Hz and the amplitude of 0.33 +/- 0.25 microm. Flutter was observed in punctured arteries at the frequency of 28 +/- 13 Hz the amplitude of 2.92 +/- 1.75 microm, and in incised livers at the frequency of 26 +/- 6 Hz and the amplitude of 1.53 +/- 0.76 microm. In a punctured artery, the vibration frequency and phase of tissue surrounding the artery were highly correlated between neighboring locations in tissue (correlation coefficient = 0.98), and with the flow oscillations in the lumen (correlation coefficient = 0.96). This preliminary study indicates that tissue vibrations could provide additional physiologic information for detecting, localizing and monitoring internal bleeding using US.

Animals↗

PubMed2005

Ultrasonic technique for imaging tissue vibrations: preliminary results.

We propose an ultrasound (US)-based technique for imaging vibrations in the blood vessel walls and surrounding tissue caused by eddies produced during flow through narrowed or punctured arteries. Our approach is to utilize the clutter signal, normally suppressed in conventional color flow imaging, to detect and characterize local tissue vibrations. We demonstrate the feasibility of visualizing the origin and extent of vibrations relative to the underlying anatomy and blood flow in real-time and their quantitative assessment, including measurements of the amplitude, frequency and spatial distribution. We present two signal-processing algorithms, one based on phase decomposition and the other based on spectral estimation using eigen decomposition for isolating vibrations from clutter, blood flow and noise using an ensemble of US echoes. In simulation studies, the computationally efficient phase-decomposition method achieved 96% sensitivity and 98% specificity for vibration detection and was robust to broadband vibrations. Somewhat higher sensitivity (98%) and specificity (99%) could be achieved using the more computationally intensive eigen decomposition-based algorithm. Vibration amplitudes as low as 1 mum were measured accurately in phantom experiments. Real-time tissue vibration imaging at typical color-flow frame rates was implemented on a software-programmable US system. Vibrations were studied in vivo in a stenosed femoral bypass vein graft in a human subject and in a punctured femoral artery and incised spleen in an animal model.

Algorithms↗

PubMed2004

Ultrasound color-flow imaging on a programmable system.

Color-flow imaging is a well-established ultrasound mode and very valuable for visualizing in real time the distribution of blood flow in a specific region of interest. However, it is computationally quite expensive. To meet the large computational need in color-flow imaging, most ultrasound systems have been designed using fixed-function hardware. In this paper, we present a system where all the color-flow processing is supported on a programmable platform. About 95% of the processing modules were programmed in C language. On a single processor, we were able to achieve 7.9 frames/s, when the input data consist of 192 x 512 x 8 (ensemble size) samples for color flow and 384 x 512 for B mode and the output image size is 600 x 420. Additional processors can be added to handle more input data and/or support higher frame rates. Our results demonstrate that a programmable ultrasound system can provide the same functionality for clinical use as conventional ultrasound systems. However, it is more flexible and efficient due to its programmability.

Algorithms↗

PubMed2003

A single mediaprocessor-based programmable ultrasound system.

We have developed a programmable ultrasound imaging system using a single commercially available mediaprocessor. We have efficiently mapped all of the necessary B-mode processing algorithms on the underlying processor architecture, including envelope detection, dynamic range compression, lateral and axial filtering, persistence processing, and scan conversion. Our system can handle varying specifications ranging from 128 vectors and 512 samples per vector to more than 256 vectors and 1024 samples per vector. For an image size of 330 vectors and 512 samples per vector, it can process 30 frames per second using a 300-MHz MAP-CA mediaprocessor from Hitachi/Equator Technologies. This programmable ultrasound machine will not only offer significant advantages in terms of low cost, portability, scalability, and reduced development time, but also provide a flexible platform for developing and deploying new clinical applications to aid the clinicians and improve the quality of healthcare to patients.

Algorithms↗