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Faculty of Science

research & innovation

Paper of the Month

By Connie Jeske Crane


Ultrasound Imaging: Ryerson Researchers Explore Novel Methods to Overcome Current Limitations


For parents, it’s a seemingly miraculous medical innovation, able to offer up a first glimpse of an unborn child. First pioneered in the 1950s, today ultrasound imaging has become a ubiquitous and invaluable medical tool – applied not only to monitor human fetal development, but also in countless other diagnostic situations. Over many decades of use, the ultrasound has proven to be a safe, relatively simple and inexpensive real-time imaging modality for soft tissues.

Ping Gong, who recently completed her PhD in Biomedical Physics, spent her time at Ryerson exploring and seeking to overcome the limitations of ultrasound imaging. Addressing its current capabilities, Gong says “Ultrasound imaging has a good spatial resolution – on the order of hundreds of microns – and imaging depth on the order of 10 cm.” But she also adds that ultrasound imaging has some limitations. 

Ping Gong

For example, Gong explains that with B-mode imaging (one of the most commonly used ultrasound imaging modes), ultrasound signals are acquired after sending focused beams along various lines sequentially in time. “A disadvantage of B-mode imaging is that the images are only optimally focused at one depth because of the single transmission focus.” Subsequently, synthetic transmit aperture (STA) imaging was developed to address the limitations with B-mode imaging. Based on a software beamforming process, STA allows focusing to be obtained at every image point, says Gong, but the limitation with STA is “low signal-to-noise ratio (SNR) due to single element excitation.” It was here that Gong, supervised by Dr. Yuan Xu and Dr. Michael C. Kolios, sought a solution. “We proposed a novel imaging method to encode STA imaging with delays – which we referred to as delay-encoded synthetic transmit aperture (DE-STA) imaging.”


Gong describes this unique process in more detail: “With DE-STA imaging, each transducer element is assigned a delay coding factor to modify the transmitted pulse. Then all transducer elements are excited simultaneously, leading to improved SNR compared to single element excitation as in STA imaging. At last, the received data undergo decoding and reconstruction to form final ultrasound image. A pseudoinverse process is implemented through decoding to further improve DE-STA performance.”

In the wake of their investigations, Ryerson researchers concluded that DE-STA is “a relatively stable encoding and decoding technique.” Not only that, beyond medical applications, Gong says the novel DE-STA method has great potential in other fields from radar and sonar systems, and nondestructive testing (NDT) to seismic monitoring.”

Gong’s team’s findings were recently published in a paper called “Pseudo-Inverse Decoding Process in Delay-Encoded Synthetic Transmit Aperture (DE-STA) Imaging,” in the journal, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

Today, having completed her Ryerson PhD in the spring, Gong lives in Rochester, MN and serves as a postdoctoral research fellow at the Mayo Clinic. Thinking back on her time in Toronto, Gong says, “Scientific research is full of challenges and difficulties, especially for PhD studies. But all the encouragement from my supervisors and colleagues at Ryerson helped me keep a positive attitude and persist through the project.” 

Gong’s research was supported by funding from NSERC and the Canada Foundation for Innovation (CFI).

Last December, Bassan and Dr. Santos presented the Ryerson research at the 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016) in Athens, Greece. Their paper, titled, “Classifying Streaming Data using Grammar-based Immune Programming” is currently “In Press.”

Ultimately, Bassan is keen on the functioning of the immune programming model, saying: “It inspires me to observe nature more carefully.” She also stresses that potential applications stretch far beyond examples noted. “We could potentially use this research to analyze electricity demand or financial trends.”

Asked about her experience at Ryerson, Bassan lists interacting with a diverse group of students as a teaching assistant among her personal highlights.  She also credits a supportive faculty: “All this would have been impossible without my supervisor’s constant guidance and I would like to take this opportunity to thank him big time.”