Time-varying filtering of ultrasound data
In ultrasound imaging, a well-defined pulse is transmitted into a medium, for instance a human body, and the echo is recorded. Due to frequency-dependent attenuation, the frequency spectrum of the received signal will change as a function of time and the medium imaged.
To improve the signal-to-noise ratio (SNR) of the received signal, unwanted parts of the signal (noise) should be filtered out. Usually, this is done using a bandpass filter. The pass band edges of this filter are, due to attenuation, generally not constant as a function of time (or equally as a function of range).
In this project, we aim to implement time-varying filtering in our ultrasound image reconstruction toolbox USTB (see https://www.ustb.no/ ). To be able to do so, the signal frequencies in the received signal need to be estimated as a function of range. In the project, you will do the following:
- Study the literature on estimation of the frequency spectrum of the received RF data.
- Study the literature on time-varying filtering
- Simulate the generation of various medical ultrasound images using the Field II and k-Wave wave propagation codes and USTB.
- Implement various methods for estimating the frequency spectrum in USTB
- Implement time-varying filters in USTB
- Test the implementation on real ultrasound data recorded using our Verasonic ultrasound scanner.
- Excellent MATLAB programming skills (Object oriented MATLAB)
- Signal processing (IN3190/4190, INF4480, IN5450, and preferably IN3015/4015)
- Git Version Control
Literature (for example):
- Treeby, Bradley E. "Acoustic attenuation compensation in photoacoustic tomography using time-variant filtering." Journal of biomedical optics 18.3 (2013): 036008.
- Ghoshal, Goutam, and Michael L. Oelze. "Time domain attenuation estimation method from ultrasonic backscattered signals." The Journal of the Acoustical Society of America 132.1 (2012): 533-543.
- Brandner, D.M.; Cai, X.; Foiret, J.; Ferrara, K.W.; Zagar, B.G. Estimation of Tissue Attenuation from Ultrasonic B-Mode Images—Spectral-Log-Difference and Method-of-Moments Algorithms Compared. Sensors 2021, 21, 2548.