Mengxing Tang Improvement of 4D ultrasound imaging with a Matrix transducer Array Lab based Biomedical sensing diagnostics and imaging A range of ultrasound applications suffer from out of plane motion and other artefacts attributed to the single slice of conventional 2D ultrasound. Matrix array transducer overcome this limitation and open new opportunities by offering 4D imaging with full volumetric information. However, current data transfer rates and the size of data acquired with a 2D matrix array transducer currently exceeds practical limits of high-performance computers. The typical large number of transducer elements (32 x 32 grid = 1024 elements) comes with physical limitations and requires creative solutions like using only a sub-aperture in reception and multiplexing in transmission. Coherent compounding with a 2D array transducer requires steering in two dimensions. When transmitting at an angle the multiplexing of the array probe has implications for the map of adjustable delays and possible steering angles in one of those two dimensions. The goal of this project is to address these issues and improve the current beamforming code to include steering in all directions. The algorithm is written in CUDA. CUDA is an accessible parallel computing platform developed by NVIDIA and has conquered scientific applications in recent years. Your task:• Understand the current beamforming algorithm. • Translate the current code to beamform all steering angles. • Assess the improvement of contrast and resolution on existing in vitro and in vivo datasets. As a student you should have knowledge in any program language and basic knowledge of ultrasound delay and sum beamforming. Ideally you have some knowledge of C/C++ or CUDA or are willing to learn. Last, good organisational skills and competence in documentation are very important. What you will learn:• Introduction to the matrix array ultrasound system.• Understand ultrasound delay and sum beamforming.• Translation, implementation, and development of algorithms used in research.• CUDA
Mengxing Tang Tissue motion correction for Super-Resolution Ultrasound microvascular imaging Desk based Biomedical sensing diagnostics and imaging,Computational and theoretical modelling Microvasculature morphology is linked to the regulation of blood perfusion and tissue remodelling e.g., wound healing, carcinogenesis, plague formation or blood glucose removal. Measurement of these structures with high spatiotemporal resolution is consequently useful in understanding the underlying biomechanical processes. Super-resolution ultrasound localization microscopy can non-invasively visualize microvasculature beyond the diffraction limit to create super-resolved images of microvascular structures at microscopic level. For this to work, motion must be corrected. In this project you will learn existing motion correction methods and codes, and compare their performance on both simulation data and experimental data. Your task:• Learn the principle and codes for existing algorithms of image motion correction.• Generate/identify data sets suitable for evaluation of tissue motion correction• Evaluate and compare the performance of existing motion correction algorithmsAs a student you should have knowledge in any program language. Good organisational skills and competence in documentation are very important. What you will learn:• Ultrasound super-resolution imaging.• Understand the concept of motion correction and existing methods/algorithms.• Quantitative evaluation of algorithm performance