Projects
Completed
Last projects
Biomedical
Project Title:

Super-Resolution in Computer Tomography (CT)

Project ID:

2627

Abstract:

Today, 3rd generation CTs can scan over 100 patients a day, but their radiation dose is relatively high. In this project the students will first study the basics of computer aided tomography, advanced mathematical imaging tools and cutting edge techniques in digital signal processing. The students will then research, design and implement a novel reconstruction algorithm for CT scans, which could allow faster scan times and reduced radiation dosage. The basis for this algorithm will implement principles from novel fields such as "Compressed Sensing" and "Super Resolution by Dictionary Learning".

Description:

PDR

Supervisor(s):

Student(s):

Gal Sadeh & Dmitry Lavrov

Semester:

Fall 2015

Project Title:

Minimal ECG data acquisition in cardiac patients Part: 2

Project ID:

2894

Abstract:

Improvement in the data acquisition stage can make a significant improvement/contribution in the setting of pace makers and long term sampling. One of the problems in pace makers is battery life time. Minimal acquisition with maximal accuracy has a lot of potential in this field. Integration of compressed sensing models into ECG acquisition can lead to accurate heart rhythm analysis using fewer samples allowing less memory and less battery usage. Most of the time, the pacemakers generate the simple R-R (inter bit) signal and record the entire ecg morphology only when tachycardia is detected. Sparse acquisition will allow R-R recognition for longer periods.

Description:

PDR

Supervisor(s):

Student(s):

Tamar Loeub & Jhonatan Rubin

Semester:

Fall 2015

Project Title:

Sub-Nyquist Tissue Doppler Ultrasound System

Project ID:

2903

Abstract:

Xampling and Compressed Sensing methods developed at SAMPL, allows the reconstruction of signals sampled at a sub-Nyquist rate with reduced number of pulses per velocity estimation, using priors on the sparsity of the signal. This reduced sapling is performed without compromising the same time temporal and spatial resolutions.
Xampling and Compressed Sensing methods developed at SAMPL, allows the reconstruction of signals sampled at a sub-Nyquist rate with reduced number of pulses per velocity estimation, using priors on the sparsity of the signal. This reduced sapling is performed without compromising the same time temporal and spatial resolutions.
Also, in this project you will become familiar with Xampling and Compressed Sensing theory and Matlab simulations. In addition, you will gain experience in implementing signal processing algorithms on real ultrasound systems.

Description:

PDR

Supervisor(s):

Alon Eilam

Student(s):

Ido Cohen & Shai Yagil

Semester:

Fall 2015

Project Title:

Push Button MRI

Project ID:

2880

Abstract:

This project is based on new methods for quantitative estimation of physical parameters of different tissues. From these values we will produce with a "push button" to obtain all different contrast images which are being used by doctors nowadays, while putting emphasis on maximal tissue intensity separation in the image.The project will be carried in collaboration with the RAMBAM hospital and it is a solution for a need that exists among doctors nowadays. A fun project which focuses on introducing students to the world of MRI and also to the fingerprinting approach (MRF) that provides quantitativephysical measurements of tissues and uses compressed sensing (CS) to save scan time.

Description:

PDR

Supervisor(s):

Student(s):

Reut Farkash & Marva Rom

Semester:

Fall 2015

Communication
Project Title:

Image deconvolution In fluorescence microscopy

Project ID:

2910

Abstract:

Super resolution fluorescence microscopy techniques are an ensemble of light-microscopy techniques which achieve spatial resolution beyond the limitations imposed by the diffraction of light. On the other hand, since its introduction in 1983, deconvolution microscopy has become a key processing tool for the visualization of cellular structures of fixed and living specimens in three dimensions and at sub-resolution scale. Deconvolution is also referred to as an inverse problem, since given the output of the system we aim at recovering the input to the system. In the proposed project we will survey and implement several deconvolution techniques over simulated and experimental data of fluorescence microscopy. These techniques will then be used as a preprocessing step towards a super-resolution imaging technique.

Description:

PDR

Supervisor(s):

Student(s):

Irina Akhvlediany

Semester:

Fall 2015

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