GPU-Based Electromagnetic Microwave Tomography for Brain Imaging and Stroke Detection

Each year, an estimated 795,000 people in the U.S. suffer a stroke, with approximately 610,000 being first-time cases. Of these, 87% are ischemic strokes, while the remaining 13% are hemorrhagic. Current imaging methods, such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI), provide useful information into brain tissue properties; while each technique has its advantages, they remain expensive, non-portable, and often too slow for emergency bedside or in-ambulance use. Electromagnetic Microwave Tomography (EMT) offers a promising alternative: an affordable, portable, rapid, and safe method for stroke detection. By contrasting dielectric properties between healthy and affected brain tissues, particularly in areas with high blood accumulation, EMT offers the potential for near real-time and continuous diagnosis of cerebrovascular accidents. To achieve this, it is essential to effectively solve the electromagnetic forward and inverse scattering problems. This thesis uses parallel programming on Graphics Processing Units (GPUs) to implement and accelerate EMT image reconstruction. Specifically, the Fast Fourier Transform Twofold Subspace-Based Optimization Method (FFT-TSBOM) is utilized to solve the two-dimensional (2D)electromagnetic scattering problem. The results showed that the GPU-based implementation reduced the reconstruction time from 48 minutes on CPU-only to 7 minutes at high resolution, achieving a speedup of 6.5 times, and under 2 minutes at low resolution, bringing us closer to near real-time monitoring scenarios. This improvement can enhance patient treatment through faster diagnosis and continuous tracking of disease progression.