Head trauma patients are susceptible to secondary injuries where afflicted tissues can propagate towards dysfunction after the initial injury is treated. Monitoring blood oxygenation (SpO2) below the skull is crucial for the early detection of secondary head injury such as hematoma. To obtain such information in a point-of-care setting, photoacoustic (PA) imaging can be used to differentiate optical contrast between hemoglobin (Hb) species by measuring resultant ultrasonic waves emitted by optically irradiated tissue. Given the ratio of Red:NIR light absorption, information regarding SpO2 can be determined in vivo. In this project, computer simulations involving PA imaging of tissue models have been performed and techniques tested using an optic fiber/transducer PA imaging system. In simulated and physical PA scans, Red:NIR ratio values are computed for various tissue models to evaluate optical contrast in target absorbers. Images reconstructed from simulations showed the ability to visualize differences in SpO2 across a layer of skull tissue using multi-spectral optical irradiation. Red:NIR ratios were calculated using PA signals produced by 750 nm light and 850 nm light. Physical image reconstructions were conducted using a 5 ns pulsed laser and near-infrared (NIR) optical parametric oscillator (OPO). The pulse energy used during physical PA raster scans reached up to 5.3 mJ/pulse. Tissue phantoms scanned consisted of optical absorbers surrounded by various tissue-mimicking materials. Images from physical acquisitions were acquired using a 2D CNC-controlled moving stage, but free-hand tracking using inertial and optical sensors have been investigated. At 5.3 mJ/pulse, images reconstructed from physical scans could not resolve optical absorbers positioned beneath a layer of skull-mimicking tissue. A limiting factor contributing to low signal-to-noise ratio (SNR) from inferior absorbers was the percentage of power lost during beam focusing. However, simulation results encourage future work to improve pulse energy output before simulation results can be validated. / Thesis / Master of Applied Science (MASc)

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Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion, DWI, and its directionality, DTI, the techniques used for analysis are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption reduces the amount of relevant information that can be interpreted in a clinical setting. By measuring the excess kurtosis, or peakedness, of the Gaussian distribution it is possible to get a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can provide additional information about the micromolecular environment of the pediatric spinal cord by more completely characterizing the probabilistic nature of random water displacement. A novel DKI imaging sequence based on a 2D spatially selective radio frequency pulse providing reduced FOV imaging with view angle tilting (VAT) was implemented, optimized on a 3Tesla MRI scanner, and tested on pediatric subjects (normal:15; patients with spinal cord injury:5). Software was developed and validated in-house for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in kurtosis parameters (mean kurtosis, axial kurtosis) between normal and patients. DKI provides incremental and new information over conventional diffusion acquisitions that can be integrated into clinical protocols when coupled with higher order estimation algorithms. / Electrical and Computer Engineering