Subcortical hyperintensities (SH) are believed to be observable signs of cerebrovascular disease, indicating some form of subcortical vasculopathy. Also commonly referred to as leukoariosis, these hyperintense signals on proton density, T2-weighted and fluid attenuated inversion recovery magnetic resonance images, are commonly observed phenomena in Alzheimer’s disease patients and elderly persons. Several SH sub-types with differential brain-behavior associations have been proposed in the scientific literature: periventricular, deep white, cystic fluid filled lacunar-like infarcts and perivascular Virchow-Robin spaces. This study will present Lesion Explorer (LE): a comprehensive tri-feature MRI-based processing pipeline that effectively and reliably quantifies SH sub-types in the context of additional brain tissues volumetrics in a regionalized manner. The LE pipeline was validated using a scan-rescan procedure. Finally, the LE pipeline was applied in a cross-sectional study of Alzheimer’s disease patients and normal elderly controls. Brain-behavior relationships were demonstrated with regional SH volumes and executive functioning, speed of mental processing, and verbal memory.

No description available.

A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.

Current models of face perception suggest independent processing of identity and expression, though this distinction is still unclear. Using converging methods of psychophysics and functional magnetic resonance imaging (fMRI) in healthy and patient populations we assessed the relationship between these two perceptual processes. First, using perceptual aftereffects, we explored the neural representations underlying identity and expression. The expression aftereffect only partially transferred across different identities, suggesting adaptation within identity-invariant and identity-dependent expression representations. Contrarily, the identity aftereffect fully transferred across different expressions. This asymmetry cannot be explained through low-level adaptation. The identity-dependent component of the expression aftereffect relies on adaptation to a coherent expression, not low-level features, in the adapting face. Thus adaptation generating the expression aftereffect must occur within high-level representations of facial expression. Second, using fMRI adaptation, we examined identity and expression sensitivity in healthy controls. The fusiform face area and posterior superior temporal sulcus showed sensitivity for both identity and expression changes. Independent sensitivity for identity and expression changes was observed in the precuneus and middle superior temporal sulcus respectively. Finally, we explored identity and expression perception in a neuropsychological population. Selective identity impairments were associated with inferior occipitotemporal damage, not necessarily affecting the occipital or fusiform face areas. Impaired expression perception was associated with superior temporal sulcus damage, and also with deficits in the integration of identity and expression. In summary, psychophysics, neuroimaging and neuropsychological methods all provide converging evidence for the independent processing of identity and expression within the face network. However, these same methods also supply converging evidence for a partial dependence of these two perceptual processes: in the expression aftereffect, the functional sensitivities of the FFA and pSTS, and identity deficits observed in a patient with primarily impaired expression perception and a spared inferotemporal cortex. Thus, future models of face perception must incorporate representations or regions which independently process identity or expression as well as those which are involved in the perception of both identity and expression.

Examination of functional brain anatomy is a crucial step in the process of surgical removal of many brain tumors. Functional magnetic resonance imaging (fMRI) is a promising technology capable of mapping brain function non-invasively. To be successfully applied to presurgical mapping, there are questions of diagnostic accuracy that remain to be addressed. One of the greatest difficulties in implementing fMRI is the need to define an activation threshold for producing functional maps. There is as of yet no consensus on the best approach to this problem, and a priori statistical approaches are generally considered insufficient because they are not specific to individual patient data. Additionally, low signal to noise and sensitivity to magnetic susceptibility effects combine to make the production of activation maps technically demanding. This contributes to a wide range of estimates of reproducibility and validity for fMRI, as the results are sensitive to changes in acquisition and processing strategies. Test-retest fMRI imaging at the individual level, and receiver operator characteristic (ROC) analysis of the results can address both of these concerns simultaneously. In this work, it is shown that the area under the ROC curve (AUC) can be used as an indicator of reproducibility, and that this is dependent on the image thresholds used. Production of AUC profiles can thus be used to optimize the selection of individual thresholds on the basis of detecting stable activation patterns, rather than a priori significance levels. The ROC analysis framework developed provides a powerful tool for simultaneous control of protocol reproducibility and data driven threshold selection, at the individual level. This tool can be used to guide optimal acquisition and processing strategies, and as part of a quality assurance program for implementing presurgical fMRI.

Porphyrins are naturally occurring molecules. Porphysomes are simple multimodal nanoparticles that derive their multifunctionality from porphyrin-based building-blocks. While previous studies have probed their interactions with light, their capacity to stably chelate metal ions has gone largely uninvestigated. Herein are presented and discussed two investigations into metalloporphysomes. First is a method for non-invasively labeling porphysomes with radioactive copper-64. Utilizing exceptionally simple chemistry, this method produces a highly stable radiotracer capable of both PET and fluorescence imaging. Second is a profile of a MRI-detectable, photothermal agent whose photonic properties are serendipitously improved by the incorporation of MRI-active metal ions. By taking advantage of simple chemical substitutions, these studies illustrate methods of accessing new functionalities while maintaining a deeply simple construct, an often overlooked aspect in the development of multimodal nanoparticles.

No description available.

Porphyrins are naturally occurring molecules. Porphysomes are simple multimodal nanoparticles that derive their multifunctionality from porphyrin-based building-blocks. While previous studies have probed their interactions with light, their capacity to stably chelate metal ions has gone largely uninvestigated. Herein are presented and discussed two investigations into metalloporphysomes. First is a method for non-invasively labeling porphysomes with radioactive copper-64. Utilizing exceptionally simple chemistry, this method produces a highly stable radiotracer capable of both PET and fluorescence imaging. Second is a profile of a MRI-detectable, photothermal agent whose photonic properties are serendipitously improved by the incorporation of MRI-active metal ions. By taking advantage of simple chemical substitutions, these studies illustrate methods of accessing new functionalities while maintaining a deeply simple construct, an often overlooked aspect in the development of multimodal nanoparticles.

Magnetic resonance imaging (MRI) is a non-invasive technique used to produce high-quality images of the interior of the human body. Compared to other imaging modalities, however, MRI requires a relatively long data acquisition time to form an image. Patients often have difficulty staying still during this period. This is problematic as motion produces artifacts in the image. This thesis explores the methods of imaging a moving object using MRI. Testing is performed using simulations, a moving phantom, and human subjects. Several strategies developed to avoid motion artifact problems are presented. Emphasis is placed on techniques that provide motion correction without penalty in terms of acquisition time. The most significant contribution presented is the development and assessment of the 'TRELLIS' pulse sequence and reconstruction algorithm. TRELLIS is a unique approach to motion correction in MRI. Orthogonal overlapping strips fill k-space and phase-encode and frequency-encode directions are alternated such that the frequency-encode direction always runs lengthwise along each strip. The overlap between pairs of orthogonal strips is used for signal averaging and to produce a system of equations that, when solved, quantifies the rotational and translational motion of the object. Acquired data is then corrected using this motion estimation. The advantage of TRELLIS over existing techniques is that k-space is sampled uniformly and all collected data is used for both motion detection and image reconstruction. This thesis presents a number of other contributions: a proposed means of motion correction using parallel imaging; an extension to the phase-correlation method for determining displacement between two objects; a metric to quantify the level of motion artifacts; a moving phantom; a physical version of the ubiquitous Shepp-Logan head phantom; a motion resistant data acquisition technique; and a means of correcting for T2 blurring artifacts.

Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such as the computed tomography (CT), MRI features a relatively long scan time that gives rise to many potential issues. The advent of parallel MRI, which employs multiple receiver coils, has started a new era in speeding up the scan of MRI by reducing the number of data acquisitions. However, the finally recovered images from under-sampled data sets often suffer degraded image quality. This thesis explores methods that incorporate prior knowledge of the image to be reconstructed to achieve improved image recovery in parallel MRI, following the philosophy that ‘if some prior knowledge of the image to be recovered is known, the image could be recovered better than without’. Specifically, the prior knowledge of image sparsity is utilized. Image sparsity exists in different domains. Image sparsity in the image domain refers to the fact that the imaged object only occupies a portion of the imaging field of view; image sparsity may also exist in a transform domain for which there is a high level of energy concentration in the image transform. The use of both types of sparsity is considered in this thesis. There are three major contributions in this thesis. The first contribution is the development of ‘GUISE’. GUISE employs an adaptive sampling design method that achieves better exploitation of image domain sparsity in parallel MRI. Secondly, the development of ‘PBCS’ and ‘SENSECS’. PBCS achieves better exploitation of transform domain sparsity by incorporating a prior estimate of the image to be recovered. SENSECS is an application of PBCS that achieves better exploitation of transform domain sparsity in parallel MRI. The third contribution is the implementation of GUISE and PBCS in contrast enhanced MR angiography (CE MRA). In their applications in CE MRA, GUISE and PBCS share the common ground of exploiting the high sparsity of the contrast enhanced angiogram. The above developments are assessed in various ways using both simulated and experimental data. The potential extensions of these methods are also suggested.