Novel Multi-Scale Architecture for Medical Image Registration

Medical image registration is an integral component of many medical image analysis pipelines. While registration has conventionally been carried out using optimization techniques, there is growing interest in the application of deep learning to medical image registration. Deep learning based image registration (DLIR) methods have shown mixed results; they are competitive with optimization-based methods for some small-displacement datasets, but struggle to match the performance of optimization-based methods in large displacement settings. This work explores what architectural features can improve network generalization by adopting tried and tested approaches from optical flow literature

Characterization of a spinal cord diffusion tensor imaging pipeline with pathological spine data

Fiber tractography from magnetic resonance (MR) diffusion tensor imaging (DTI) enables the visualization of white matter bundles. In the presence of pathology, these bundles can be distorted and disconnected, which can reveal clinically significant information about the nature of the underlying pathology. This work studies DTI in the spine in the presence of pathology. A spine DTI pipeline that was developed in an earlier study is evaluated against the pathological data. We study the challenges of adapting the pipeline to pathological spine data, where MRI artifacts and significant distortion in cord shape and contrast from pathology make automated cord segmentation and registration extremely challenging. Moreover, we identify challenges with processing highly anisotropic MRI volumes and the implications this has on DTI processing. Heuristics are developed to handle these issues and are incorporated into the pipeline. Finally, visualizations of the tractography streamlines are generated and the impact of pathology on the streamline trajectories is briefly discussed, awaiting clinical validation

Characterization of a spinal cord diffusion tensor imaging pipeline with pathological spine data

Fiber tractography from magnetic resonance (MR) diffusion tensor imaging (DTI) enables the visualization of white matter bundles. In the presence of pathology, these bundles can be distorted and disconnected, which can reveal clinically significant information about the nature of the underlying pathology. This work studies DTI in the spine in the presence of pathology. A spine DTI pipeline that was developed in an earlier study is evaluated against the pathological data. We study the challenges of adapting the pipeline to pathological spine data, where MRI artifacts and significant distortion in cord shape and contrast from pathology make automated cord segmentation and registration extremely challenging. Moreover, we identify challenges with processing highly anisotropic MRI volumes and the implications this has on DTI processing. Heuristics are developed to handle these issues and are incorporated into the pipeline. Finally, visualizations of the tractography streamlines are generated and the impact of pathology on the streamline trajectories is briefly discussed, awaiting clinical validation

Experimental Study of an Organic Rankine Cycle Using n-Hexane as the Working Fluid and a Radial Turbine Expander

Conversion of low-grade waste heat to electrical energy paves the way to reducing environmental pollution. This work focuses on the experimental study of an organic Rankine cycle (ORC) with an n-hexane working fluid and radial turbine expander. The heat source is varied from 120 to 190 °C with a mass flow rate of 0.10 to 0.50 kg/s and pressure between 12 and 15 bar. The heat-source temperature has a direct impact on turbine performance. Increase in the mass flow rate of the working fluid led to an increase in pressure and temperature at the turbine inlet. The rise in turbine speed enhanced electrical efficiency while cutting down isentropic efficiency. The optimum speed of the turbine increased with increasing in turbine inlet temperature. Superheating leads to an increase in power along with a decrease in isentropic efficiency. The thermal efficiency followed an increasing trend when there was an increase in turbine inlet temperature and mass flow rate and decreased with an increase in turbine speed. The electrical efficiency increased for all three cases. The system was found to have a highest thermal efficiency of 5.57% with a power of 1.75 kW. Based on the experimental results, it can be concluded that an ORC with n-hexane as the working fluid and a radial turbine as the expander can be used in low-temperature waste heat recovery systems to produce power