390 research outputs found
Diffusion imaging and tractography of congenital brain malformations.
Diffusion imaging is an MRI modality that measures the microscopic molecular motion of water in order to investigate white matter microstructure. The modality has been used extensively in recent years to investigate the neuroanatomical basis of congenital brain malformations. We review the basic principles of diffusion imaging and of specific techniques, including diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI). We show how DTI and HARDI, and their application to fiber tractography, has elucidated the aberrant connectivity underlying a number of congenital brain malformations. Finally, we discuss potential uses for diffusion imaging of developmental disorders in the clinical and research realms
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Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique challenge is to identify, with perfect or near-perfect sensitivity and very high specificity, often small subtle abnormalities on a multislice cross-sectional (three-dimensional [3D]) imaging modality that is characterized by poor soft tissue contrast, low signal-to-noise using current low radiation-dose protocols, and a high incidence of artifacts. We trained a fully convolutional neural network with 4,396 head CT scans performed at the University of California at San Francisco and affiliated hospitals and compared the algorithm's performance to that of 4 American Board of Radiology (ABR) certified radiologists on an independent test set of 200 randomly selected head CT scans. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 ± 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists. We demonstrate an end-to-end network that performs joint classification and segmentation with examination-level classification comparable to experts, in addition to robust localization of abnormalities, including some that are missed by radiologists, both of which are critically important elements for this application
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Disrupted White Matter Microstructure of the Cerebellar Peduncles in Scholastic Athletes After Concussion.
Concussion, or mild traumatic brain injury (mTBI), is a major public health concern, linked with persistent post-concussive syndrome, and chronic traumatic encephalopathy. At present, standard clinical imaging fails to reliably detect traumatic axonal injury associated with concussion and post-concussive symptoms. Diffusion tensor imaging (DTI) is an MR imaging technique that is sensitive to changes in white matter microstructure. Prior studies using DTI did not jointly investigate white matter microstructure in athletes, a population at high risk for concussive and subconcussive head traumas, with those in typical emergency room (ER) patients. In this study, we determine DTI scalar metrics in both ER patients and scholastic athletes who suffered concussions and compared them to those in age-matched healthy controls. In the early subacute post-concussion period, athletes demonstrated an elevated rate of regional decreases in axial diffusivity (AD) compared to controls. These regional decreases of AD were especially pronounced in the cerebellar peduncles, and were more frequent in athletes compared to the ER patient sample. The group differences may indicate differences in the mechanisms of the concussive impacts as well as possible compound effects of cumulative subconcussive impacts in athletes. The prevalence of white matter abnormality in cerebellar tracts lends credence to the hypothesis that post-concussive symptoms are caused by shearing of axons within an attention network mediated by the cerebellum, and warrant further study of the correlation between cerebellar DTI findings and clinical, neurocognitive, oculomotor, and vestibular outcomes in mTBI patients
Evaluating metabolites in patients with major depressive disorder who received mindfulness-based cognitive therapy and healthy controls using short echo MRSI at 7 Tesla.
ObjectivesOur aim was to evaluate differences in metabolite levels between unmedicated patients with major depressive disorder (MDD) and healthy controls, to assess changes in metabolites in patients after they completed an 8-week course of mindfulness-based cognitive therapy (MBCT), and to exam the correlation between metabolites and depression severity.Materials and methodsSixteen patients with MDD and ten age- and gender-matched healthy controls were studied using 3D short echo-time (20 ms) magnetic resonance spectroscopic imaging (MRSI) at 7 Tesla. Relative metabolite ratios were estimated in five regions of interest corresponding to insula, anterior cingulate cortex (ACC), caudate, putamen, and thalamus.ResultsIn all cases, MBCT reduced severity of depression. The ratio of total choline-containing compounds/total creatine (tCr) in the right caudate was significantly increased compared to that in healthy controls, while ratios of N-acetyl aspartate (NAA)/tCr in the left ACC, myo-inositol/tCr in the right insula, and glutathione/tCr in the left putamen were significantly decreased. At baseline, the severity of depression was negatively correlated with my-inositol/tCr in the left insula and putamen. The improvement in depression severity was significantly associated with changes in NAA/tCr in the left ACC.ConclusionsThis study has successfully evaluated regional differences in metabolites for patients with MDD who received MBCT treatment and in controls using 7 Tesla MRSI
Local Identification of Subsets of Quantum states: A Stronger Quantum Nonlocality
Nonolocality makes quantum theory nontrivially sacred and useful in the
paradigm of information theoretic tasks. Apart from Bell nonlocality, which
deals with measurement outcome statistics of spatially separated agents, there
is also another kind of quantum nonlocality, that is associated with perfect
distinguishability of quantum states by local operations and classical
communication (LOCC). We propose a distributed task: perfect identification of
subsets of a known set of multipartite orthogonal states by LOCC, namely, local
subset identification. Failure in accomplishing this task guarantees a new
notion of quantum nonlocality, viz., local subset unidentifiability. Here, we
show that both local distinguishability and local markability of quantum states
implies local subset identifiability, but the converse is not necessarily true.
This makes local subset unidentifiability a stronger quantum nonlocal
phenomenon than its predecessors -- local indistinguishability and local
unmarkability. Moreover, we also present an even stronger version of local
subset unidentifiablity involving more than two spatially separated parties
namely, genuine local subset unidentifiability, where a given subset becomes
identifiable if and only if all the parties come together in a common lab.Comment: Initial draft, New results added, Comments are welcom
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An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography.
There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning
System Identification and Control of Front-Steered Ackermann Vehicles through Differentiable Physics
In this paper, we address the problem of system identification and control of
a front-steered vehicle which abides by the Ackermann geometry constraints.
This problem arises naturally for on-road and off-road vehicles that require
reliable system identification and basic feedback controllers for various
applications such as lane keeping and way-point navigation. Traditional system
identification requires expensive equipment and is time consuming. In this work
we explore the use of differentiable physics for system identification and
controller design and make the following contributions: i)We develop a
differentiable physics simulator (DPS) to provide a method for the system
identification of front-steered class of vehicles whose system parameters are
learned using a gradient-based method; ii) We provide results for our
gradient-based method that exhibit better sample efficiency in comparison to
other gradient-free methods; iii) We validate the learned system parameters by
implementing a feedback controller to demonstrate stable lane keeping
performance on a real front-steered vehicle, the F1TENTH; iv) Further, we
provide results exhibiting comparable lane keeping behavior for system
parameters learned using our gradient-based method with lane keeping behavior
of the actual system parameters of the F1TENTH.Comment: Accepted for IROS 202
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