Two graduate students will present their research at the fourth Beckman Graduate Student Seminar of the spring 2023 semester: Zhengchang Kou, electrical and computer engineering; and Zepeng Wang, bioengineering. The event takes place at noon Wednesday, April 5 in 1005 Beckman Institute and on Zoom. Lunch is provided to in-person attendees.

Register in advance to attend.

Improving ultrafast ultrasound imaging in both speed and quality

Zhengchang Kou

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy and functional ultrasound. Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, we proposed a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging on a field programmable gate array by using high-level synthesis. Besides, to improve the spatial resolution and image quality of ultrafast ultrasound imaging we explored null subtraction imaging as an alternative beamforming technique to delay-and-sum. NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. Higher spatial resolution and image quality were observed from the NSI-based microvessel images compared to microvessel images generated by traditional DAS-based beamforming.

Zhengchang Kou is a fifth-year Ph.D. student in the Department of Electrical and Computer Engineering under the supervision of Professor Michael L. Oelze. His work is mainly focused on enabling faster processing of ultrafast ultrasound imaging on hardware accelerators such as FPGA and high-speed ultrasound communications for in-body devices.

Multi-parametric molecular imaging of the brain using multi-dimensional MR spectroscopic imaging

Zepeng WangMagnetic resonance spectroscopic imaging is a label-free, noninvasive molecular imaging modality, and has demonstrated unique potential in detecting and quantifying biochemical changes in brain injury for improved diagnosis and recovery assessment. However, traditional MRSI methods were limited by (1) insufficient image resolution and SNR; (2) insufficient molecular specificity (e.g., reliably separating signals from metabolites and neurotransmitters); and (3) the need of more quantitative biomarkers beyond molecule concentrations to probe the complex neural tissue microstructure. To address these challenges, we are developing novel multi-dimensional MRSI techniques that introduce additional encoding dimensions (i.e., multi-TE and diffusion encoding) integrated with machine learning-powered data processing methods. These developments have produced new multi-parametric molecular imaging capabilities allowing for simultaneous mapping of metabolites, neurotransmitters, and their in vivo biophysical properties (such as relaxation and diffusion parameters), in both healthy and injured brains. I will discuss these exciting progresses in my talk.

Zepeng Wang received his bachelor’s degree in biomedical engineering from Tsinghua University in 2019 and is currently a Ph.D. candidate in the Department of Bioengineering. His research focuses on developing multi-dimensional, quantitative MRSI to probe the brain at the molecular level, and their clinical applications to brain injury. Zepeng received the Beckman Graduate Fellowship in 2022, and is also a Mavis Future Faculty Fellow of 2022. His works have received multiple abstract awards at the annual meetings of the International Society of Magnetic Resonance in Medicine (ISMRM), the flagship conference in the field of MRI.

Learn more about Beckman's Graduate Student Seminar Series.

Read Q&As with student researchers on Beckman's Student Researcher Spotlight page.