Automatic Vessel Detecting Algorithm in Pulsed Wave Doppler Ultrasound
Date Issued
2012
Date
2012
Author(s)
Kuang Hsieh, Kevin
Abstract
Cerebral vascular diseases (strokes) account for the third highest cause of death in Taiwan. There are mainly two types of stroke, ischemic (clogging of brain vessel) and hemorrhagic (burst of brain vessel) strokes. The permanent disability caused by stroke also creates a huge cost for the society. Currently, Doppler ultrasound systems are used to detect vessel flows in the heart or intracranial region during the open-heart operation and stroke patients monitoring. However, the operation of transcranial Doppler (TCD) ultrasound systems requires experienced medical personnel and the Doppler gate seeking is done manually. Furthermore, the wires connecting the systems often restrict the angle and range of operation for the examiners. In order to perform faster diagnosis for stroke symptoms, automatic vessel detecting algorithm (AVDA) finding the Doppler gate (depth interval) of the phantom vessel and common carotid arteries is developed. The algorithm stresses on the depth interval where best flow signal can be obtained. In the early stage, a 5 MHz linear ultrasound transducer and CompuFlow 1000 are used to construct an ultrasound system. Under the phantom setup, the velocity scale AVDA locates the correct Doppler gate at starting depth of 34.18mm. Likewise, the vessel range for the Doppler gate is accurately determined to be at 28.64~32.34mm for the common carotid data from using the waveform AVDA. In the post image processing, adaptive pulse coupled neural network denoising (AD-PCNN) technique is used to improve the image SNR from 8.70 to 21.72 dB, and able to effectively inhibit additional noise at 10 dB. A wireless TCD system is designed under collaboration between various labs funded by National System on Chip’s (NSOC) grant. The wireless data transfer is inspected by experimenting over a phantom vessel with peristaltic pump. The wireless TCD system is able to correctly input and output flow signal from the transducer to the end display on the computer. The implemented 60 GHz wireless module and computed unified device architecture (CUDA) can provide fast data transferring (~1 Gb/s) and calculation of the Doppler spectrogram. Not only the spectrogram can be executed real-time for the patient diagnosis, the data can be transferred wirelessly into the hospital server. For future work, we wish to integrate the AVDA and the NSOC wireless TCD system to achieve automatic vessel detection in clinical settings.
Subjects
Automatic Scanning
Wireless Pulsed Wave Doppler Ultrasound
Computed Unified Device Architecture
Adaptive Pulse-Coupled Neural Network
Type
thesis
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