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An intracranial haemorrhage is a life threatening medical emergency, yet only a fraction of the patients receive treatment in time, primarily due to the transport delay in accessing diagnostic equipment in hospitals such as Magnetic Resonance Imaging or Computed Tomography. A mono-static microwave head imaging system that can be carried in an ambulance for the detection and localization of intracranial haemorrhage is presented. The system employs a single ultra-wideband antenna as sensing element to transmit signals in low microwave frequencies towards the head and capture backscattered signals. The compact and low-profile antenna provides stable directional radiation patterns over the operating bandwidth in both near and far-fields. Numerical analysis of the head imaging system with a realistic head model in various situations is performed to realize the scattering mechanism of haemorrhage. A modified delay-and-summation back-projection algorithm, which includes effects of surface waves and a distance-dependent effective permittivity model, is proposed for signal and image post-processing. The efficacy of the automated head imaging system is evaluated using a 3D-printed human head phantom with frequency dispersive dielectric properties including emulated haemorrhages with different sizes located at different depths. Scattered signals are acquired with a compact transceiver in a mono-static circular scanning profile. The reconstructed images demonstrate that the system is capable of detecting haemorrhages as small as 1 cm3. While quantitative analyses reveal that the quality of images gradually degrades with the increase of the haemorrhage’s depth due to the reduction of signal penetration inside the head; rigorous statistical analysis suggests that substantial improvement in image quality can be obtained by increasing the data samples collected around the head. The proposed head imaging prototype along with the processing algorithm demonstrates its feasibility for potential use in ambulances as an effective and low cost diagnostic tool to assure timely triaging of intracranial hemorrhage patients.

Microwave imaging reconstruction is applied to an anatomically realistic, numerical head phantom using the Born iterative method for detection of a haemorrhagic stroke within the brain tissues. A forward model is used to estimate back‐scattered signals which are subsequently used in the image reconstruction process. Image reconstruction using excitation signals at 850 MHz is shown to produce the emulated stroke with a high contrast particular to the estimated conductivity profile.

Abstract-In recent years, the growth of the computational power available in the Central Processing Units (CPUs) of consumer computers has tapered significantly. At the same time, growth in the computational power available in the Graphics Processing Units (GPUs) has remained strong. Algorithms that can be implemented on GPUs today are not only limited to graphics processing, but include scientific computation and beyond. This paper is concerned with massively parallel implementations of incremental sampling-based robot motion planning algorithms, namely the widely-used Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT*. We demonstrate an example implementation of RRT and RRT* motion-planning algorithm for a high-dimensional robotic manipulator that takes advantage of an NVidia CUDA-enabled GPU. We focus on parallelizing the collision-checking procedure, which is generally recognized as the computationally expensive component of sampling-based motion planning algorithms. Our experimental results indicate significant speedup when compared to CPU implementations, leading to practical algorithms for optimal motion planning in high-dimensional configuration spaces.

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