Functional MRI (fMRI) is a primary tool in the study of brain function. The primary cause of data corruption in fMRI is head motion while scanning. This problem is compounded by the fact that subjects are asked to perform behavioural tasks, which can promote head motion. Random and/or large head motions are often not handled well in post-processing correction algorithms. This thesis investigates the use of an alternate method: an MRI simulator to help reduce head motion in subjects through training. A simulator environment was developed where subjects could be trained to reduce their head motion through closed loop visual feedback. The effect of simulator training was investigated in young, old and stroke subjects. Performance of subjects with respect to head motion was investigated prior, during and after feedback training, including subsequent fMRI scans. This research helps improve fMRI image quality by reducing head motion prior to scanning.

Artifacts caused by head motion are a substantial source of error in fMRI that limits its use in neuroscience research and clinical settings. Real-time scan-plane correction by optical tracking has been shown to correct slice misalignment and non-linear spin-history artifacts, however residual artifacts due to dynamic magnetic field non-uniformity may remain in the data. A recently developed correction technique, PLACE, can correct for absolute geometric distortion using the complex image data from two EPI images, with slightly shifted k-space trajectories. We present a correction approach that integrates PLACE into a real-time scan-plane update system by optical tracking, applied to a tissue-equivalent phantom undergoing complex motion and an fMRI finger tapping experiment with overt head motion to induce dynamic field non-uniformity. Experiments suggest that including volume by volume geometric distortion correction by PLACE can suppress dynamic geometric distortion artifacts in a phantom and in vivo and provide more robust activation maps.

Functional MRI (fMRI) is a primary tool in the study of brain function. The primary cause of data corruption in fMRI is head motion while scanning. This problem is compounded by the fact that subjects are asked to perform behavioural tasks, which can promote head motion. Random and/or large head motions are often not handled well in post-processing correction algorithms. This thesis investigates the use of an alternate method: an MRI simulator to help reduce head motion in subjects through training. A simulator environment was developed where subjects could be trained to reduce their head motion through closed loop visual feedback. The effect of simulator training was investigated in young, old and stroke subjects. Performance of subjects with respect to head motion was investigated prior, during and after feedback training, including subsequent fMRI scans. This research helps improve fMRI image quality by reducing head motion prior to scanning.

Artifacts caused by head motion are a substantial source of error in fMRI that limits its use in neuroscience research and clinical settings. Real-time scan-plane correction by optical tracking has been shown to correct slice misalignment and non-linear spin-history artifacts, however residual artifacts due to dynamic magnetic field non-uniformity may remain in the data. A recently developed correction technique, PLACE, can correct for absolute geometric distortion using the complex image data from two EPI images, with slightly shifted k-space trajectories. We present a correction approach that integrates PLACE into a real-time scan-plane update system by optical tracking, applied to a tissue-equivalent phantom undergoing complex motion and an fMRI finger tapping experiment with overt head motion to induce dynamic field non-uniformity. Experiments suggest that including volume by volume geometric distortion correction by PLACE can suppress dynamic geometric distortion artifacts in a phantom and in vivo and provide more robust activation maps.

<p>The goal of this thesis is to improve Usability and functionality of a tool for artifact management, which applies taxonomic paths for categorizing artifacts. The main issues of using the taxonomic paths are used for categorization and should improve the precision when retrieving documents. The results show the improvements in functionality and usability of the artifact manager. This thesis explains about Usability, re-engineering, and necessary infrastructure to improve the performance of the artifact manager tool.At the end of the thesis necessary modifications has been done to improve usability and functionality of artifact manager</p>

Lung nodule volumetry is used to diagnose the likelihood of malignancy in nodules detected during thoracic CT scans. These measurements are unreliable when the patient is subject to respiratory motion. We seek to understand the relationship between reconstructed images and the actual size of nodules subject to motion induced by quiet breathing. CT images of solid spheres of varying size and composition were acquired while travelling through a known path to approximate the motion of a pulmonary nodule during respiration. The measured size of the sphere’s image was found to increase non-linearly with speed. However, these relationships were dependent on the CT number of the sphere and the reconstruction filter used to generate the image. From these results we expect that for a specific CT number we can estimate the size of an object from a CT image if the speed of the object at the time of the scan is known.

Lung nodule volumetry is used to diagnose the likelihood of malignancy in nodules detected during thoracic CT scans. These measurements are unreliable when the patient is subject to respiratory motion. We seek to understand the relationship between reconstructed images and the actual size of nodules subject to motion induced by quiet breathing. CT images of solid spheres of varying size and composition were acquired while travelling through a known path to approximate the motion of a pulmonary nodule during respiration. The measured size of the sphere’s image was found to increase non-linearly with speed. However, these relationships were dependent on the CT number of the sphere and the reconstruction filter used to generate the image. From these results we expect that for a specific CT number we can estimate the size of an object from a CT image if the speed of the object at the time of the scan is known.

Montreal’s Lachine Canal, once the cradle of Canadian industry, is now riddled with industrial ruins, testaments to its productive past. Since the canal’s closing in the 1970’s, different attempts were made to reinterpret its role within the city. Contaminated sediments pollute the manufactured waterway, now stagnant and derelict. These toxic remains impact the redevelopments and heritage parks of the canal corridor. In the absence of any holistic future vision, these conditions pose a threat to local inhabitants and industrial artifacts. Meanwhile, Parks Canada’s approved heritage status pertaining to certain parts of the canal, further contributes to the segregation of the corridor into sporadic developments and static voids. Antoine Picon refers to these networks of technological remnants as ‘Anxious Landscapes’ – landscapes of artifacts that exist in the realm between technological obsolescence and ruin in the process of returning to nature. These landscapes are charged with industrial ruins and their residues in decay, perceived as waste, make us feel ill at ease with them. Portions of the canal and its industrial artifacts have been identified as having significant heritage value, but what productive possibilities do these heritage artifacts hold beyond their identified status? What possibilities do these imaginative playgrounds possess to reshape the corridor beyond its static blight? In abandoned industrial icons such as the Canada Malting Plant, resides the potential to address the remediation and reinterpretation of the corridor. The thesis investigates whether interaction with these industrial remnants can permit a tactile connection that allows us to uncover and explore the significance of such landscapes in a larger temporal perspective that considers past, present, and future. It proposes to reveal and express the historical development of the canal, exploring remedial solutions and spaces of community participation, energizing the Lachine Canal and its anxious landscape.

A system for extracellular neural interfacing that had the capability for stimulation and recording at multiple electrodes was presented. As the core of this system was a custom integrated circuit (IC) that contained low-noise amplifiers, stimulation buffers, and artifact-elimination circuitry. The artifact-elimination circuitry was necessary to prevent the activity of the stimulation buffers from interfering with the normal functioning of the low-noise amplifiers. The integrated circuits were fabricated in in a 0.35 micron CMOS process. We measured input-referred noise levels for the amplifiers as low as 3.50 microvolts (rms) in the in the bandwidth 30 Hz-3 kHz, corresponding to the frequency range of neural action potentials. The power consumption was 120 microwatts, corresponding to a noise-efficiency factor of 14.5. It was possible to resume recording signals within 2 ms of a stimulation, using the same electrode for both stimulation and recording. A filtering algorithm to remove the post-discharge artifact was also presented. The filtering was implemented using a field-programmable gate array (FPGA). The filtering algorithm itself consisted of blanking for the duration of the stimulation and artifact-elimination, followed by a wavelet de-noising. The wavelet de-noising split the signal into frequency ranges, discarded those ranges that did not correspond to neural signals, applied a threshold to the retained signals, and recombined the different frequency ranges into a single signal. The combination of the filtering with the artifact-elimination IC resulted in the capability for artifact-free recordings.

The estimation of software project attributes, such as size, is important for software project resource planning and process control. However, research regarding software attribute modeling, such as size, effort, and cost, are high-level and static in nature. This research defines a new operation-level software project attribute that describes the operational characteristic of a software project. The result is a measurement based on the s-curve parameter that can be used as a control variable for software project management. This result is derived from modeling the count of artifact instances created by the software engineering process, which are stored by software tools. Because of the orthogonal origin of this attribute in regard to traditional static estimators, this s-curve based software attribute can function as an additional indicator of software project activities and also as a quantitative metric for assessing development team capability.