אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Although chirp-coded signals can theoretically improve the performance of ultrasound medical imaging, they are presently of limited use. Their main advantage is better power delivery without loss of axial resolution. Their drawback, however, is that the biological tissue distorts the ultrasound echo of the chirp, and thus the shape of the received ultrasound pulse is unknown. This causes degradation in the quality of the reconstructed ultrasound image when a matched filter is used.

To overcome this difficulty, we introduce a method for ultrasound pulse estimation by analyzing the received echo. A modification of blind homomorphic deconvolution is proposed using multi-resolution sparse representations and machine learning. This method trains multi-resolution dictionaries using training sets. The proposed system is trained on chirp signals, resulting in high precision pulse estimates. Our simulation results show that the proposed method, which can be used also for additional types of pulses, facilitates the use of chirp signals in medical ultrasound imaging and contributes to enhanced imaging results.

M.Sc. research under the supervision of Prof. Moshe Porat (EE, Technion) and Dr. Zvi Friedman (GE Healthcare).