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

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