Artifacts caused by patient motion during scanning remain a serious problem in most MRI applications. The prospective motion correction technique attempts to address this problem at its source by keeping the measurement coordinate system fixed with respect to the patient throughout the entire scan process. In this study, a new image-based approach for prospective motion correction is described, which utilizes three orthogonal two-dimensional spiral navigator acquisitions, along with a flexible image-based tracking method based on the extended Kalman filter algorithm for online motion measurement. The spiral navigator/extended Kalman filter framework offers the advantages of image-domain tracking within patient-specific regions-of-interest and reduced sensitivity to off-resonance-induced corruption of rigid-body motion estimates. The performance of the method was tested using offline computer simulations and online in vivo head motion experiments. In vivo validation results covering a broad range of staged head motions indicate a steady-state error of less than 10% of the motion magnitude, even for large compound motions that included rotations over 15 deg. A preliminary in vivo application in three-dimensional inversion recovery spoiled gradient echo (IR-SPGR) and three-dimensional fast spin echo (FSE) sequences demonstrates the effectiveness of the spiral navigator/extended Kalman filter framework for correcting three-dimensional rigid-body head motion artifacts prospectively in high-resolution three-dimensional MRI scans. Artifacts caused by patient motion during scanning remain a serious problem in most clinical and research MRI applications. In fast single-shot sequences, such as dynamic two-dimensional (2D) echo-planar imaging (EPI), between-scan motion can introduce significant motionrelated variance to the voxel-time courses and disrupt the spin excitation history of the acquisition (1,2). In multishot 2D and three-dimensional (3D) sequences, withinscan patient motion results in k-space data inconsistencies, causing artifacts such as ghosting, blurring, and ringing in the images themselves. Offline image registration can mitigate most between-scan motion artifacts in time-series data (3-5) but cannot correct for changes in the spin excitation history caused by through-plane motion. In addition, while some within-scan motion artifacts can be corrected retrospectively using knowledge of the motion history derived from either navigator scans (6,7) or overlapping k-space data (8,9), most of these methods are limited by the inability to (1) fully correct for through-plane motion in 2D sequences and (2) avoid k-space data inconsistencies caused by interpolation errors.An alternative approach to motion correction, which shares none of these drawbacks, is modify the pulsesequence online, in real-time, during the acquisition itself. Some of the first real-time prospective motion correction methods used straight-line navigators to correct for linear translations of organs in the chest (10-12). Since then, navigator...

Diffusion magnetic resonance imaging (dMRI) is a powerful tool for studying biological tissue microarchitectures in vivo. Recently, there has been increased effort to develop quantitative dMRI methods to probe both length scale and orientation information in diffusion media. Diffusion spectrum imaging (DSI) is one such approach that aims to resolve such information on the basis of the three-dimensional diffusion propagator at each voxel. However, in practice only the orientation component of the propagator function is preserved when deriving the orientation distribution function. Here, we demonstrate how a straightforward extension of the linear spherical deconvolution (SD) model can be used to probe tissue orientation structures over a range (or “spectrum”) of length scales with minimal assumptions on the underlying microarchitecture. Using high b-value Cartesian q-space data on a fixed rat brain sample, we demonstrate how this “restriction spectrum imaging” (RSI) model allows for separating the volume fraction and orientation distribution of hindered and restricted diffusion, which we argue stems primarily from diffusion in the extra- and intra-neurite water compartment, respectively. Moreover, we demonstrate how empirical RSI estimates of the neurite orientation distribution and volume fraction capture important additional structure not afforded by traditional DSI or fixed-scale SD-like reconstructions, particularly in grey matter. We conclude that incorporating length scale information in geometric models of diffusion offers promise for advancing state-of-the-art dMRI methods beyond white matter into grey matter structures while allowing more detailed quantitative characterization of water compartmentalization and histoarchitecture of healthy and diseased tissue.

Diffusion weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000’s. Prior to its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neuro-oncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions as to the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hindered and restricted diffusion, and elucidating how diffusion parameters in tissue are derived from the measurements via the diffusion model. We describe one advanced DWI modeling technique, called Restriction Spectrum Imaging (RSI). This technique offers a more direct in vivo measure of tumor cells, due to its ability to distinguish separable pools of water within tissue based on their intrinsic diffusion characteristics. Using RSI as an example, we then highlight the ability of advanced DWI techniques to address key clinical challenges in neuro-oncology, including improved tumor conspicuity, distinguishing actual response to therapy from pseudoresponse, and delineation of white matter tracts in regions of peritumoral edema. We also discuss how RSI, combined with new methods for correction of spatial distortions inherent diffusion MRI scans, may enable more precise spatial targeting of lesions, with implications for radiation oncology, and surgical planning.

Neuroimaging studies of painful stimuli in humans have identified a network of brain regions that is more extensive than identified previously in electrophysiological and anatomical studies of nociceptive pathways. This extensive network has been described as a pain matrix of brain regions that mediate the many interrelated aspects of conscious processing of nociceptive input such as perception, evaluation, affective response, and emotional memory. We used functional magnetic resonance imaging in healthy human subjects to distinguish brain regions required for pain sensory encoding from those required for cognitive evaluation of pain intensity. The results suggest that conscious cognitive evaluation of pain intensity in the absence of any sensory stimulation activates a network that includes bilateral anterior insular cortex/frontal operculum, dorsal lateral prefrontal cortex, bilateral medial prefrontal cortex/anterior cingulate cortex, right superior parietal cortex, inferior parietal lobule, orbital prefrontal cortex, and left occipital cortex. Increased activity common to both encoding and evaluation was observed in bilateral anterior insula/frontal operculum and medial prefrontal cortex/anterior cingulate cortex. We hypothesize that these two regions play a crucial role in bridging the encoding of pain sensation and the cognitive processing of sensory input.