Projects
Completed
Fall 2014
Biomedical
Project Title:

Coded Excitation Approach to Medical Ultrasound - Part 2

Project ID:

2359

Abstract:

Sonography techniques use multiple transducer elements for tissue visualization by radiating it with acoustic energy. The image is typically comprised of multiple scanlines, obtained by sequential insonification of the medium using focused beams. Modern imaging systems use single-carrier short pulses for transducer excitation, while the usage of more sophisticated signals can be beneficial in terms of SNR, penetration depth and frame rate. These parapeters are extremely valuable in medical ultrasound imaging. Coded signals are successfully used in such areas as radar and communication systems. The application of this approach to ultrasound is challenging due to imaging and not detection nature of ultrasound, its high dynamic range and frequency dependent attenuation.
In this project methods for coded excitation adopted from the radar processing will be incorporated to frequency domain beamforming (FDBF) framework developed recently at SAMPL. Performance of modified FDBF will be simulated using k-Wave simulation tool running in Matlab environment.

Description:

PDR

Supervisor(s):

Student(s):

Almog Lahav, Uval Ben-Shalom

Semester:

Fall 2014

Project Title:

Speedup of MRI by Exploiting Similatiry between Different Sequences Using Compressed Sensing

Project ID:

2365

Abstract:

Magnetic Resonance Imaging (MRI) is the method of choice for diagnosis, evaluation and follow-up of brain clinical pathologies. However, the acquisition of a routine brain MRI requires an average of 30mins per examination. As such, it causes many difficulties, such as patient discomfort during scanning and blurry images due to patient movements during acquisition. Magnetic Resonance Imaging has numerous sequences; each one images a different physical quantity. However, there is great resemblance between different MRI sequences and much of the scan time could be saved by decreasing the amount of measurement in each sequence and using the similarity between the sequences. For instance, FLAIR imaging is T2 imaging where only the liquids are attenuated (see figure below). The project will include working on real Magnetic Resonance images of artificial objects and also of healthy and sick patients. The reconstruction will be done using Compressed Sensing methods and is expected to shorten the scan time and to decrease the distortions which will decrease the patient discomfort during the scan.

Description:

PDR

Supervisor(s):

Student(s):

Ev Zisselman, Eran Lustig

Semester:

Fall 2014

Project Title:

Exploiting Similarity in Adjacent Slices for Compressed Sensing MRI

Project ID:

1879

Abstract:

Due to fundamental characteristics of MRI that limit scan speedup, sub-sampling techniques such as compressed sensing (CS) have been developed for rapid MRI. Current CS MRI approaches utilize sparsity of the image in the wavelet or other transform domains to speed-up acquisition. Another drawback of MRI is its poor signal-to-noise ratio (SNR), which is proportional to the image slice thickness. In this paper, we use the difference between adjacent slices as the sparse domain for CS MRI. We propose to acquire thick MRI slices and to reconstruct the thin slices from the thick slices’ data, utilizing the similarity between adjacent thin slices. The acquisition of thick slices, instead of thin ones, improves the total SNR of the reconstructed image. Experimental results show that the image reconstruction quality of the proposed method outperforms existing CS MRI methods using the same number of measurements.

Description:

PDR

Supervisor(s):

Student(s):

Ohad Rahamim, Roey Dekel

Semester:

Fall 2014

Communication
Project Title:

Joint Spectrum Blind Reconstruction and Direction-of-Arrival Estimation from Sub-Nyquist Samples - Demo Part I

Project ID:

2377

Abstract:

Spectrum blind reconstruction and direction-of-arrival (DOA) estimation of several narrowband signals spread over a wide spectrum from sub-Nyquist samples has been thoroughly investigated separately. In many communication applications, both the carrier frequencies and DOAs of the narrowband transmissions are unknown and their joint reconstruction is of great interest. Although many methods have been proposed for their joint estimation from Nyquist rate samples, very little has been done in the sub-Nyquist rate regime. The goal of this project is to implement a sub-Nyquist sampling system that allows for joint spectrum reconstruction and DOA estimation.

Description:

PDR

Supervisor(s):

Student(s):

Shani Mahlab, Matan-David Schlanger

Semester:

Fall 2014

Project Title:

Joint Spectrum Blind Reconstruction and Direction-Of-Arrival Estimation from Sub-Nyquist Samples

Project ID:

2368

Abstract:

Project goal is to adapt a sub-Nyquist sampling system, the modulated Wideband Converter (MWC) that allows for spectrum reconstruction, to joint spectrum and DOA estimation.

Description:

PDR

Supervisor(s):

Student(s):

Shahar Stein, Or Yair

Semester:

Fall 2014

Project Title:

Cyclostationary detection from sub-Nyquist samples for Cognitive Radio - demo - Part 2

Project ID:

2474

Abstract:

The goal of this project is to demonstrate cyclostationary detection from sub-Nyquist samples in a cognitive radio scenario. The demo will consists of an analog part, the MWC, that mixes and samples the analog input signal at a low rate. Then, the cyclic spectrum is reconstructed from the sub-Nyquist samples. Last, the carrier frequencies and symbol rates are estimated from the cyclic spectrum (using the existing algorithm developed in a previous project).

Description:

PDR

Supervisor(s):

Student(s):

Noga Shaul, Liad Pollack

Semester:

Fall 2014

Radar
Project Title:

Cognitive Radar Demo

Project ID:

2358

Abstract:

The aim of this project is to assess feasibility of radar signal transmission in narrow bands, and successful detection of the received signal. A demo system that compares performance vs. existing technology will be built. The solution is based on implementing advanced Compresses Sensing algorithms.

Description:

PDR

Supervisor(s):

Student(s):

Michal Aharonovm, Boris Frayman

Semester:

Fall 2014

INTERIA Web Design & Development