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Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - April 2022)
Dynamic Contrast-Enhanced MRI (DCE-MRI) data may be used to non-invasively investigate the health status of tissue. The technique requires that the concentration of a contrast agent vs. time curve is known in both the tissue of interest and in a blood vessel feeding the tissue - commonly referred to as the arterial input function (AIF). Physiologically relevant parameters are extracted through Pharmacokinetic modeling, though the accuracy is known to be highly sensitive to the quality of the acquired data. It is difficult to get a good measurement of the AIF in pre-clinical studies in mice due to their small body size and limited number of vessels of a sufficient size. As a result, several groups use a population averaged curve from the literature. This curve does not account for inter or intra-individual differences, and impacts the accuracy of the fit parameters.We propose a new projection-based measurement that measures the AIF from a single trajectory in k-space, which provides a temporal resolution equal to the repetition time (TR). This AIF is measured in the mouse tail due to the simpler geometry void of highly enhancing organs nearby. The projection-based AIF is advantageous as it allows for the acquisition of DCE data, in the tissue of interest, between measurements without affecting the temporal resolution of either data set. We set up a dual coil experimental platform that acquires AIF data at the mouse tail and DCE data at the tumour. Our technique allows for data optimization at both locations, without restricting the temporal or spatial resolutions of the AIF or DCE data. It may be applied to any pre-clinical study using mice or rats.
In this thesis, a new magnetic resonance imaging (MRI) quantitative T2 mapping technique, called Luminal water imaging (LWI), has been developed and used for non-invasive detection and grading of prostatic tumours. Using this technique, we measured what we hypothesized to be the fractional amount of water content of luminal spaces in prostate, and called it Luminal Water Fraction, LWF. Based on the differences in tissue composition and fractional amount of luminal space between malignant and normal prostatic tissues and between tumors of different grades, we hypothesized that the measurements of LWF could be used for the detection and grading of prostatic tumours. To verify these hypotheses, we performed two patient studies in which we compared MR measurements of LWI with whole-mount histology. In the first study, we evaluated the correlation between LWF and the percentage area of luminal space in the prostatic tissue. The results of this study demonstrated that LWF is significantly and strongly correlated with the percentage area of luminal space in the prostatic tissue. In the second study, we investigated the feasibility of LWI in the detection and grading of prostate cancer. The results of this study showed that LWI provides high accuracy both in the detection and grading of prostatic tumours. After verifying our hypotheses, we performed a detailed comparison between the diagnostic accuracy of LWI and the more established MRI techniques: Dynamic Contrast-Enhanced (DCE) and Diffusion-Weighted MRI (DW-MRI). The results of this pilot study showed that LWI alone performs better than DCE, DW-MRI, or their combination, in the detection of prostatic tumours and also in correlation with GS. Based on the results of this study, we proposed a guideline for making a more efficient, abbreviated multi-parametric MRI protocol for the diagnosis of prostate cancer.Finally, as a side project, we explored some potential areas of improvement in DCE-MRI by investigating the impact of temporal resolution on the accuracy of DCE-MRI in detection of prostatic tumours. Our results showed that within a certain range of temporal resolutions, the diagnostic accuracy of DCE-MRI would be independent of the temporal resolution.
Master's Student Supervision (2010 - 2021)
No abstract available.
The behavior of MR phase and frequency in demyelination and damage in central nervous tissue white matter arises not only from traditionally associated bulk susceptibility changes, but also from changes to its tissue microstructure. A recently proposed generalized Lorentzian model of microstructure-related magnetic susceptibility effects predicts an increase in MR frequency due to damage in myelin in MS lesions. The same model also predicts reduction in MR frequency due to axonal degeneration. Here, we investigate the effect of both myelin and axonal damage through transection of white matter fibers in the dorsal column of rat cervical spinal cord. This injury generates secondary damage consisting of neurodegeneration along nerve tracts bilateral to the transection site, producing cases of Wallerian and retrograde degeneration free of excessive hemorrhage and inflammation. High-resolution frequency maps of degenerating tracts were correlated with histopathology for axons, myelin, degenerated myelin, and macrophages. Damage to myelin sheaths is prominent in Wallerian degeneration, where we observe strong correlations with increasing frequency up to 8 weeks post-injury. Retrograde degeneration, which consists predominantly of axonal damage, produces decreased frequency shift over time. The MR frequency shifts are sensitive to the effects of macrophage in filtration and debris clearance, which vary with white matter fiber density and affect rates of degeneration. We demonstrate how MR frequency can successfully characterize injury in rat spinal cord white matter in a manner consistent with predictions outlined by the Generalized Lorentzian Approximation Model, and conclude that these results suggest potential applications of MR frequency to supplement or replace current clinical techniques, such as myelin water and diffusion weighted imaging, as a non-invasive and quantitative method of assessing white matter damage in CNS.
Diffusion Tensor Imaging has been successfully applied in prostate cancer diagnosis (Kozlowski et al., 2010). It has been well established that the water Apparent Diffusion Coefficient has a lower value in the prostate carcinomas when compared to normal prostatic tissue (Bashar, 2002; Gürses et al., 2008; Kozlowski et al., 2010; Manenti et al., 2007; Pickles et al., 2005; Sato et al., 2005; Xu et al., 2009). However, fractional anisotropy values in prostatic carcinoma have been reported to be higher (Gürses et al., 2008), lower (Manenti et al., 2007), and unchanged(Xu et al., 2009) when compared to the prostate’s normal peripheral zone. Preliminary data from a study involving diffusion tensor imaging measurements in prostate glands, in-vivo and ex-vivo following radical prostatectomy, is presented. Histology whole mount slides were registered to T2 weighted images and diffusion parametric maps using a mutual information voxel intensity registration algorithm using software developed in-house. Regions of interest which included the normal peripheral zone, the normal peripheral zone with enlarged glands, and tumours were taken into account for this study. The tumours were highlighted and graded with the Gleason score grading system by a specialized pathologist. Values of the apparent diffusion coefficient and the fractional anisotropy parameters were calculated. Monte-Carlo simulations of the behaviour of the fractional anisotropy with respect to the value of the apparent diffusion coefficient, the signal to noise ratio, and the b-value of the Stejskal-Tanner equation were performed. The results show lower values of the apparent diffusion coefficient for regions of tumours for the ex-vivo and in-vivo cases. Values of the fractional anisotropy in the prostate carcinomas are slightly higher ex-vivo than in-vivo, which may be explained by the dependence of the fractional anisotropy on partial volume effects and noise. These preliminary results show that the fractional anisotropy does not show significant differences between normal and cancerous tissue, strongly suggesting that it is not likely to contribute significantly to the diagnostic capabilities of diffusion tensor imaging in prostate cancer.