Diffusion Tensor MR Imaging

This unit reviews the physical principles and methodologies involved in diffusion‐weighted imaging (DWI) and diffusion tensor imaging (DTI) for clinical applications. Diffusion‐sensitive MRI noninvasively provides insight into processes and microscopic cellular structures that alter molecular water mobility. Formalism to extend the Bloch equation to include effects of random translational motion through field gradients is reviewed. Definition of key acquisition parameters is also reviewed along with common methods to calculate and display tissue diffusion properties in a variety of image formats. Characterization of potential directional‐dependence of diffusion (i.e., anisotropy), such as that which exists in white matter, requires DTI. Diffusion tensor formalism and measurement techniques then reduce the diffusion tensor into standard anisotropy quantities that are summarized along with commonly used methods to depict directional information in an image format.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145343/1/cpmib0801.pd

Applications of Chemical Shift Imaging to Marine Sciences

The successful applications of magnetic resonance imaging (MRI) in medicine are mostly due to the non-invasive and non-destructive nature of MRI techniques. Longitudinal studies of humans and animals are easily accomplished, taking advantage of the fact that MRI does not use harmful radiation that would be needed for plain film radiographic, computerized tomography (CT) or positron emission (PET) scans. Routine anatomic and functional studies using the strong signal from the most abundant magnetic nucleus, the proton, can also provide metabolic information when combined with in vivo magnetic resonance spectroscopy (MRS). MRS can be performed using either protons or hetero-nuclei (meaning any magnetic nuclei other than protons or 1H) including carbon (13C) or phosphorus (31P). In vivo MR spectra can be obtained from single region of interest (ROI or voxel) or multiple ROIs simultaneously using the technique typically called chemical shift imaging (CSI). Here we report applications of CSI to marine samples and describe a technique to study in vivo glycine metabolism in oysters using 13C MRS 12 h after immersion in a sea water chamber dosed with [2-13C]-glycine. This is the first report of 13C CSI in a marine organism

AI-generated Content for Various Data Modalities: A Survey

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the demonstrated potential of recent works, AIGC developments have been attracting lots of attention recently, and AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape (as voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human avatar (body and head), 3D motion, and audio -- each presenting different characteristics and challenges. Furthermore, there have also been many significant developments in cross-modality AIGC methods, where generative methods can receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar), and audio modalities. In this paper, we provide a comprehensive review of AIGC methods across different data modalities, including both single-modality and cross-modality methods, highlighting the various challenges, representative works, and recent technical directions in each setting. We also survey the representative datasets throughout the modalities, and present comparative results for various modalities. Moreover, we also discuss the challenges and potential future research directions

Characterization of soft-tissue response to mechanical loading using nuclear magnetic resonance (NMR) and functional magnetic resonance imaging (fMRI) of neuronal activity during sustained cognitive-stimulus paradigms

Research applications of nuclear magnetic resonance (NMR) span a broad range of fields and disciplines. The work presented in this dissertation attests to this fact. Specifically, the research topics discussed in the body of this work employ NMR spectroscopy and imaging to characterize the water diffusion and NMR relaxation times ex vivo in rabbit Achilles tendon and, in a clinical setting, employ functional magnetic resonance imaging (fMRI) to investigate the behavior of different neural networks over a period of sustained activity. In the ex vivo rabbit Achilles tendon work, a series of studies were performed. First, the diffusion-time dependence of the water apparent diffusion coefficient (ADC) was characterized in a spectroscopic mode with the samples subjected to different states of tensile loading. The results of this study demonstrated: (1) the anisotropy of the diffusion of water through tendon; (2) the ADC is diffusion-time dependent; (3) the values of the ADC(tdif) curve increased with tensile loading; (4) a change at the short diffusion-time points that is consistent with the interpretation of a load-induced increase in the collagen fibril packing density; and (5) an increase in the water ADC at long diffusion times is hypothesized to be due to T1 editing. To further investigate these issues, another series of ex vivo rabbit Achilles tendon experiments was performed that employed NMR imaging to spatially characterize the water ADC, T1 and T2 relaxation time constants. As with the spectroscopic work, these studies were also conducted with the tendon samples subjected to different states of tensile loading. The results from these imaging experiments demonstrate: (1) two regions with distinct differences in signal intensity across the tendon: a thin region of high signal intensity at the peripheral rim of the tendon that encircles a region of low signal intensity in the central core of the tendon; (2) a higher diffusion anisotropy ratio in the tendon central core relative to the peripheral rim; (3) upon tensile loading, significant increases in the ADC of water in the peripheral rim region and a corresponding increase in a measure of the change in proton density in the rim region, consistent with the hypothesis that tensile loading causes extrusion of water from the core to the rim region of the tendon; (4) this water extrusion is not uniformly distributed throughout the tendon rim region; and (5) the long-diffusion-time ADC behavior is consistent with the T1 spin editing hypothesis of the spectroscopic work. From the clinical fMRI studies, an analysis method was presented for observing dynamic changes in brain regions involved in different neural network processes during a period of sustained activity. The results from these studies are consistent with the idea that over time, brain regions adapt to the given task demands through either recruitment or discharge of adjacent areas of tissue. These results also indicate that traditional analysis of block design fMRI studies may underestimate dynamic changes in brain regions during a sustained task. The analysis method may be useful as an exploratory tool to observe region specific variations in activation that may allow inferences to be made regarding how different brain regions adapt to and interact with one another during periods of extended activity

Application of bootstrap resampling in fMRI

This thesis demonstrates the use of the bootstrap resampling technique considering temporal dependency in the fMRI data to determine the reliability and confidence interval of fMRI parameters. Traditionally, the test-retest method has been used to reliably detect active voxels in the fMRI image of the brain, which is based on repetitive experimentation. The main concern with the test-retest method is the reproducibility of data over these multiple repetitions. Fatigue, habituation, motion artifacts, and repositioning errors are few of the factors, which can affect the reproducibility of data. The conventional bootstrap resampling technique is based on the assumption that the dataset is independent and identically distributed over time. However, studies have shown temporal dependency in the fMRI images of the brain acquired from subjects in the resting phase. This study demonstrates the use of the bootstrap resampling technique, incorporating the criterion of temporal dependency in the fMRI data set, to detect reliable active voxels in the fMRI images acquired during a task activated motor paradigm, where the subject is instructed to perform bilateral finger tapping. The results of the study showed that the active regions detected using the bootstrap resampling technique considering temporal dependency in the fMRI data were more reliable than the active regions detected using the bootstrap resampling technique without considering any temporal dependency in the fMRI data