Development, Optimization and Implementation of New Algorithms for Improving the Resolution of EM, Acoustic and fMRI Reconstructions

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Many people are familiar with the internal body images available through magnetic resonance (MR) imaging. Unlike photographic images, MR images are not captured directly as an image but must be calculated using a discrete Fourier transform (DFT) algorithm from signals excited from the hydrogen nuclei in the body. It has been said that “There are more DFT algorithms used to analyze signals on any given day than any other algorithm”. This implies “There are many research opportunities available to understand and solve the limitations of DFTs." A basic problem is that a compromise must be made between spatial resolution, the quality of the MR image captured, and time resolution, the ability to track changes such as imaging the moving heart. False information appears in the image which confuses diagnosis. My team has recently combined our own constrained reconstruction for improving spatial resolution with proposed sparse sampling approaches that deliberately time save by gathering less data. Some unexpected preliminary results were found, with some parts of the image improving quality while others became more distorted. We are developing a new post-processing reconstruction process capable of mathematically manipulating the MR signals to produce an image with all parts having the quality to meet diagnosis needs. This requires combining the best parts of many poor quality images -- similar to recent ideas of replacing the bulky (thick), expensive, high-resolution camera lens in mobile phones with software that combines images from many flat, inexpensive, low-resolution lenses. We have identified that our new approach has the capability of improving the capability of a wide range of signal and image reconstruction algorithms: (1) using a sparse array of acoustic sensors to gather intensity and phase information leading to industrial and community noise localization to help with our Community Acoustic Noise which has recently received considerable media attention, (2) improving the low-resolution fMRI images to provide new markers to diagnose Optical Neuritis (ON). ON is often a preliminary stage leading to Multiple Sclerosis; unfortunately common in northern countries such as Canada and (3) improving an industrial real-time lab bench embedded system’s resolution in characterizing magnetic near-field effects measured using a sparse 2D hall-effect sensor array, Developing these algorithms in the laboratory is one thing, but we must be able to apply these algorithms in practical industrial, medical and community situations. We have been following up on the recent movement of successful business (enterprise) “Agile oriented software processes” into reliable embedded system code. The lack of literature has led us to suggest we use our proposed new reconstruction algorithms to undertake systematic investigations into using Agile ideas in a research field; an area with a potential wide audience.