published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor off Philosophy Johannesburg, 2015 / Background Objective volumetric assessment of white matter in children with HIV involves post M processing, while corpus callosum (CC) thickness measurement on midMsagittal MRI may represent a rapid surrogate marker. Aim To determine whether the thickness of the CC on midMsagittal MRI can be used as a surrogate marker of brain volume in children with HIV Mrelated brain disease and in appropriate controls and to determine whether thickness at particular locations correlates with mental developmental scores and laboratory markers of immunity. Methods A retrospective analysis of 33 children with HIV Mrelated neurology(range 7 M 49 months; median31 months; mean 30 months; 16 boys and 17 girls) and matched controls (range 13 – 48 months; median 34 months; mean 32 months; 6 boys and 5 girls) was performed. A custom software tool imported sagittal MRI images, divided the midline CC contour into 40 segments and measured the thickness of each segment as well as the length of the CC. Brain volume (total brain volume (TBV); white matter volume (WMV);grey matter volume (GMV)) was determined using MATLAB and Statistical Parametric Mapping software. Overall and segmental CC mean and maximum thickness and CC length were checked for correlation with brain volume, Griffiths mental development scores(GMDS) and laboratory parameters. Results Griffiths scores in patients were ‘low average’ (mean Griffiths general quotient (GQ) of 84, range 72 – 101; ‘locomotor’ 84, range 59 – 116; ‘language’ 80; range57 –118). There was no statistical difference in overall and regional CC thickness, CC length, TBV, GMV and WMV between patients and controls. Significant correlation was found in patients for the premotor CC mean with age (p = 0.04). Other significant correlations of CC measurements and laboratory / clinical parameters were the prefrontal CC max with in adir CD4 (p=0.046)(+vecorrelation); motor CC max with GQ (p=0.028) (Mve!correlation) and CC length with CD4(p=0.04) (Mve correlation). Significant correlations between CC thickness and brain volume were found in patients and controls for the CC mean and TBV (p=0.049)(+ve correlation);premotor CC mean and TBV (p=0.039)(+ve correlation); sensory CC mean and TBV (p=0.022)(+ve correlation); prefrontal CC max and WMV (p=0.019)(+ve correlation); premotor CC mean and WMV (p=0.019)(+ve correlation and for the premotor CC max and WMV (p=0.023)(+ve correlation). Conclusion: This research met its objectives in demonstrating a statistically significant, albeit weak, correlation between CC thickness and brain volume in patients and controls, even though patients were not shown to have significantly diminished brain volumes as compared to controls.

Magnetic Resonance Imaging (MRI) has been used to provide high-resolution tomographic information, valuable in the study of static vocal tract. However, speech does not present a static behaviour but relies on the continuous and dynamic interaction of the vocal tract articulators and neighbouring tissues. Thus, this could make real-time MRI (rt-MRI) an essential tool to assess speech, with numerous advantages over the current clinical techniques. However, using rt-MRI to image the upper vocal tract remains challenging; the motion of the articulators, including the velum is fast while MRI data acquisition is slow thus inherently limiting temporal resolution. Additionally, an intrinsic loss in SNR, spatial resolution and/or visual image quality is present. The main focus of this thesis is to increase clinical reliability of rt-MRI in speech by investigating novel methodologies for the imaging of velopharyngeal motion. Firstly, commercial rt-MRI protocols at 1.5 T and 3 T were compared, regarding image quality and temporal resolution compromise. Optimal imaging protocols were suggested for both eld strengths. This provided a starting point for future clinical translation and the use of commercial and currently available protocols to image velopharyngeal motion. Furthermore, imaging of velopharyngeal motion was further improved with non-standard acquisition methods, such as non-Cartesian sampling and more advanced reconstruction schemes. An improved imaging protocol for the assessment of velopharyngeal motion was suggested. This was based on a highly accelerated radial trajectory with a novel parallel imaging reconstruction method (radial tt-GRAPPA). The suggested protocol not only allowed for improved image quality and image sharpness,but it was also viable for future clinical translation regarding o offline computation times compared to other reconstruction methodologies also investigated in this thesis. In summary, this thesis added some novel insights into the eld of speech rt-MRI, presenting improved and time effcient imaging protocols, adequate for the assessment of velopharyngeal motion.

Introduction: The Elastography Software Library (ESL) was developed to achieve clinically feasible, super-resolution (SR) Magnetic Resonance Elastography (MRE). ESL was created by accomplishing four objectives: 1. perform a critical analysis of MRE inversion, using discrete-time Fourier transform (DTFT) methods, to enable selection of the wave inversion approach most suitable to high- and SR MRE (Chapter 2) 2. develop a new method for real-time 4D phase unwrapping, to enable large acquisitions to be processed in clinical work ow (Chapter 3) 3. develop a new inversion pipeline that recovers fine features in elastograms (Chapter 4) 4. extend this pipeline with a novel interpolation technique to achieve super-resolution (Chapter 5) The results of these experiments were combined to make the ESL. Over the course of the work, two objectives also resulted in software applications in their own right (PhaseTools for phase unwrapping, and Elastography Software Pipeline (ESP) for fine feature elasticity map recovery). Methods: Critical Analysis: Two-filter cascades were designed to model the signal processing pipelines found in the present MRE literature. These models were subjected to DTFT-based analysis to determine the relative advantage of various mathematical approaches to the MRE inverse problem. Phase Unwrapping: A test data set was developed to measure algorithm performance in 4D on data sets with varying levels of wrap, gradient and noise. The algorithms that performed most accurately and efficiently on test data were then applied to in vivo brain, liver, and muscle data, of both moderate and severe wrap, and inspected for wrap failure. Fine Feature Recovery: A new MRE image processing pipeline was developed that incorporates wavelet-domain denoising, image-driven noise estimation, and feature detection. ESP was first validated using simulated data, including viscoelastic Finite Element Method (FEM) simulations, at multiple noise levels. ESP images were then compared with Multifrequency Dual Elasto-Visco Inversion (MDEV) pipeline images in three ten-subject cohorts of brain, thigh, and liver acquisitions. Finally the proportion of spectral energy at fine frequencies was quantified using the Reduced Energy Ratio (RER) for both ESP and MDEV. Super-Resolution: An extension of the ESP pipeline was developed that incorporated a new image fusion technique to combine non-redundant information. The algorithm was validated on an analytic simulation program developed for the study. An in vivo cross-validation was performed between 1X, 2X and 4X magnification levels measuring both spectral gains and shear modulus values. Results: Critical Analysis: The more complex, heterogeneous FEM models were found to only outperform Algebraic Helmholtz Inversion (AHI) in very low noise, with Gaussian smoothing of σ > 0:8px or Butterworth low-pass cutoffs of < 0:8π negating any advantages from assumption of local heterogeneity. Phase Unwrapping: Three algorithms were determined to perform with sufficient robustness in real-time on 4D data sets with challenging phase wrap. These algorithms were then applied to in vivo brain, skeletal muscle, liver and phantom data and shown to successfully resolve heavy phase wrap within a \real-time" criterion of under 3 minutes. Fine Feature Recovery: For FEM inversions, mean values of background and soft target simulated results remained within 8% of prescribed up to σ = 10% for both jG*j and ϕ, though inspection of the ϕ image showed scatter- and boundary-related artefacts around the soft target. Hard target results showed jG*j means within 7% of prescribed up to σ = 5% but unreliable ϕ means, and inspection showed showed scatter- and boundary-related artefacts. For the in vivo cohorts, ESP results showed mean correlation of R = 0:83 with MDEV and liver stiffness estimates within 7% of Local Frequency Estimation (LFE) results. Finally, ESP showed statistically significant increase in fine feature spectral energy as measured with RER for both jG*j (p < 1X10-9) and ϕ (p < 1X10-3). Super-Resolution: At 4X SR, both brain and liver cohorts showed a highly significant (p ≤ 10-6) increase in both number of recovered frequencies and normalised spectral energy in those recovered frequencies. Both the 2X and 4X SR techniques showed a decrease in stiffness estimate from the original resolution (mean decrease of 11.6% and 14.0%) respectively; however cohort correlations between SR and original values were upwards of R = 0:988. Discussion: Established as a technique highly sensitive to important tissue changes, MR Elastography is now also a finely-featured super resolution technique in two parameters, enabling new clinical and research applications. Future work includes statistical mapping of both localised and diffuse soft tissue changes, rapid computation on heterogeneous processing architectures, and two-parameter super-resolution MRI-based lesion mapping.