Projects
Active
Biomedical
Project Title:

Compressed Sensing for Ultrasound Elastography

Project ID:

4122

Abstract:

Elastography is a medical imaging modality that maps the elastic properties of soft tissue. The main idea is that whether the tissue is hard or soft will give diagnostic information about the presence or status of disease. For example, elastography is used for detection and diagnosis of breast, thyroid and prostate cancers. Certain types of elastography are also used to investigate disease in the liver.

Compressed sensing (CS) is a signal processing technique for efficiently acquiring and reconstructing a signal. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem



Description:

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Project Title:

Correlation Beamformer for Ultrasound Diverging-Waves

Project ID:

4125

Abstract:

Medical ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. Novel imaging method based on insonification with diverging improves image quality and acquisition time, however, it is limited by data transfer rates and severe computational load.

Recently, a new array geometry have been introduced, which provides a novel way to perform array processing with much fewer physical sensors when the second-order statistics of the received data is used.

In this project, we will derive a model for diverging waves imaging, based on second-order statistics. Incorporating the new array geometry into our model will allow to create a way of imaging which requires much less transducer elements, paving the way to 3D imaging. This work will include the basics of ultrasound imaging as well as advanced tools for array processing and MATLAB simulations.



Description:

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Student(s):
Project Title:

Velocity Estimation in Spatial-Temporal Frequency Domain

Project ID:

4123

Abstract:

Doppler ultrasound is a non-invasive and safe modality that is used for the estimation of blood velocities by transmitting high-frequency sound waves (ultrasound) and analyzing the signals reflected from circulating red blood cells. Doppler scans help diagnose many conditions, including: heart valve defects and congenital heart disease, artery occlusions and aneurysms.


Description:

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Project Title:

Ultrasound Tomography for Breast Cancer

Project ID:

4124

Abstract:

Ultrasound tomography (UST) is an imaging technique that combines sonography with computed tomography (CT) methods to solve an inverse problem. It is well suited for inferring biomechanical properties of a volume of tissue from measurements made along a surface surrounding the tissue. One clinically relevant application is the detection of breast cancer.


UST has been under development for more than 30 years, motivated by many potential advantages over x-ray CT in the area of medical imaging. At diagnostic levels, sound waves do not appreciably heat tissue and, unlike x-rays, do not damage tissue through the process of ionization. With mounting concerns over radiation exposure, UST offers a nonsignificant risk alternative for medical imaging. In the area of breast imaging, UST offers the possibility of a comfortable alternative to mammography, which requires substantial compression that many women find uncomfortable and some even painful. Furthermore, UST is poised to address limitations associated with current clinical breast imaging.


In this research project, we will develop a new recovery method for ultrasound tomography based on sparse regularizer. The work will involve studying the basics of ultrasound imaging and tomography, deriving new theory and implementing it in MATLAB simulations.


Description:

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Project Title:

Coded Excitation for diverging wave Imaging

Project ID:

3791

Abstract:

Medical ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. Novel imaging method based on insonification with diverging improves image quality and acquisition time, however, it is limited by data transfer rates and severe computational load. In addition, 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. Therefore, coded signals are extremely valuable in medical ultrasound imaging although their implementation is challenging due to imaging and not detection nature of ultrasound, its high dynamic range and frequency dependent attenuation. Recently, diverging waves imaging was implemented using Fourier domain beamforming developed in SAMPL, leading to significant reduction in both sampling and processing rates. In this project we aim to extend this work by developing Fourier-based coded ultrasound for diverging waves imaging. The combination of these novel techniques will pave a way to enhanced SNR, real-time ultrafast ultrasound system. Performance of the developed method will be tested on a Verasoincs (research-oriented) ultrasound system Matlab environment. Required background: Introduction to Digital Signal Processing (044198)



Description:

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Supervisor(s):

Regev Cohen, Tanya Chernyakova

Student(s):
Elad Hirsch | Gil Cherniak
Project Title:

Limited Angle Reconstruction for Computed Tomography

Project ID:

3863

Abstract:

Medical CT imaging is probably the most widely used imaging tool in-use today. In this project, we consider the problem of scanning from only a limited angle of projections (less than 180o).

Successfully reconstructing from a limited projection angle would allow us to install small CT scanners in small clinics, and maybe even on-board ambulances. However, limited angle reconstruction is a tough mathematical problem, and we will attempt to solve it using recently developed, signal processing tools.

In this project, we will learn and use sophisticated tools from signal processing, such as sub-Nyquist sampling, algebraic reconstruction techniques and new optimization based solvers.

The students will first learn on tomographic imaging, and will get familiar with the required background in advanced signal processing. We will then design and build a simulator that can reconstruct images from limited scanning angles, using the structure of the tomographic scans.


Description:

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Supervisor(s):
Student(s):
Rotem Hon | Dmitry Nabedrik
Project Title:

Reducing Metal Artifacts in CT

Project ID:

3819

Abstract:

Metal artifacts in CT scans are a major problem for radiologists today. Whenever a patient has a metallic object in his body (dental fills, platinum bolts or knife wounds for instance), the CT image gets completely ruined, as seen in the image below.

We suggest to use advanced signal processing tools, such as sparse representations, dictionary learning and others, in order to exploit the structure of the CT scans to our advantage. By smart modelling of the metal artifacts, we hope to reduce or even remove them altogether from the output scans.

Doctors from both Rambam and Ichilov hospitals have shown tremendous interest in an algorithm that can reduce these artifacts, and we will be working in collaboration with them. Our goal is to ultimately include our solution on a real CT machine, within the hospital. Real experiments can be conducted in General Electric, CT department.

In this project the students will learn state of the art methods in signal processing and sampling, and will get familiar with principles of tomography.



Description:

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Supervisor(s):
Student(s):
נגה איזנר | Dor Meron
Deep Learning
Project Title:

Machine Learning methods For Pain Analysis from EEG Recordings

Project ID:

3903

Abstract:

Electroencephalogram (EEG) is a typically non-invasive test used to measure the electrical activity of the brain (i.e. neuronal activations), by placing electrodes along the scalp. Traditional analysis of EEG signals generally focus on measuring the: (i) Magnitudes of neuronal activations within a specific frequency bands. (ii)Time of the response to a stimulus (from the averaged signal called ERP).
The goal of this project is to perform automatic analysis of such signals recorded from patients suffering from neuropathic pain and a control group. In order to do so, we will: (i) Use signal processing tools in order to extract from the raw EEG recordings valuable information (called features). (ii) Apply a machine learning methodologies on the extracted features to separate between the conditions and extract valuable information regarding the origin of the problem.

Description:

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Supervisor(s):
Student(s):
David Ben-Kalifa | Yuval Spiegel
Project Title:

Super-Resolution Fluorescence Microscopy using Deep Learning

Project ID:

3902

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.
However, these techniques are currently limited by low temporal resolution and long acquisition times. In this project, we will investigate an exciting new direction which will combine Deep Learning of both low and high resolution images, to improve both its
temporal and spatial resolution.
The students will get a hands on experience with a research project, combining disciplines in fluorescence microscopy, machine learning and
optimization techniques.

Description:

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Supervisor(s):
Student(s):
Orr Avrech | Alexander Finkelshtein
Machine Learning
Project Title:

Machine learning for blood glucose prediction

Project ID:

4166

Abstract:

Diabetes was recently declared as a worldwide epidemic by the National Health Organization, with hundreds of millions of patients worldwide, and many more in a pre-diabetic state.

Diabetes patients need to monitor their blood glucose levels constantly, and are prone to high (hypercalcemia) and low (hypoglycemia) spikes for blood glucose fluctuations.

One of the promising ways to measure glucose levels is with the Libre system, which attaches to the back of the patients hand.

In this project we will develop a recurrent network to predict blood glucose levels, based on the collected data from the Freestyle Libre.


Description:

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Supervisor(s):
Student(s):
Communication
Project Title:

Reduced RF Chains for Massive MIMO - Analog Combiner Demo

Project ID:

4091

Abstract:

Reducing the number of RF chains in a multi-antenna system is a central effort in next generation communication systems, as the RF components grow more expensive and area consuming. This issue had been the subject of many of the latest researches in the communication area and is also of great interest in the industry side. One common method to allow for RF chains reduction is to shift some of the data processing to the analog side, using an analog combiner. The purpose of the analog combiner is to project the high number of analog samples to the low number of RF chains while preserving as much relevant information as possible.

Description:

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Supervisor(s):
Student(s):
Optics
Project Title:

Super-Resolution Photoacoustic imaging

Project ID:

4058

Abstract:

Photoacoustic imaging is a new method for visualizing blood vessels in-vivo. The key idea is to excite the red blood cells with short laser pulses and record the acoustic vibrations which are emitted afterwards. Recently, we have been able to achieve sub-diffraction resolution in contrast enhanced ultrasonic imaging and in fluorescent microscopy imaging using compressed sensing techniques.

In this project we will apply similar super-resolution concepts to photoacoustic data to improve the spatial resolution of this imaging modality.


Description:

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Project Title:

Super-Resolution without contrast agents in US

Project ID:

4057

Abstract:

Ultrasound super-localization microscopy techniques presented in the last few years enable non-invasive imaging of vascular structures at the capillary level by tracking the flow of ultrasound contrast agents (gas microbubbles). However, these techniques are currently limited by low temporal resolution and long acquisition times. Super-resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal.


In this project we will attempt to achieve super-resolution without the use of contrast agents.

We will explore different methods for separating the blood vessels from the tissue and apply super-resolution methods to the separated blood vessels.

Description:

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Supervisor(s):
Student(s):
INTERIA Web Design & Development