|Institution:||University of Waterloo|
|Keywords:||image processing; ultrasound imaging; data sparsity; speckle noise; beam profile|
|Full text PDF:||http://hdl.handle.net/10012/9296|
3-D ultrasound imaging offers unique opportunities in the field of non-destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges which cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependant point spread function of the imaging system. There is no characterization model that describes these intrinsic challenges. In this research, we will propose a characterization framework for ultrasound imaging systems that are based on the row column method. The proposed framework will include a joint statistical image formation and noise modeling and characterization as well as a characterization of the system's beam profile using a spatially-variant point spread function. Our proposed framework has many potential applications including building a more adequate image reconstruction model, providing a better metric for comparison of different row column systems, allowing for a better optimization of a row column system's performance, and giving us a better understanding of images acquired from row column systems.