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Lipeng Ning, Evren Özarslan, Carl-Fredrik Westin, Yogesh Rathi
Inferring the microstructure of complex media from the diffusive motion of molecules is a challenging problem in diffusion physics. In this paper, we introduce a novel representation of diffusion MRI (dMRI) signal from tissue with spatially-varying diffusivity using a diffusion disturbance function. This disturbance function contains information about the (intra-voxel) spatial fluctuations in diffusivity due to restrictions, hindrances and tissue heterogeneity of the underlying tissue substrate. We derive the short- and long-range disturbance coefficients from this disturbance function to characterize the tissue structure and organization...
October 14, 2016: NeuroImage
Min-Hee Lee, Youngjin Lee, Yoon Ho Hwang, Areum Min, Bong Soo Han, Dong Youn Kim
Sleep restriction (SR) is defined as the condition of not having enough sleep, and it can cause brain injury. In this study, we examined the impact of SR on the structural brain network. We obtained diffusion MRI (dMRI) data for the SR group of fourteen participants who got less than or equal to 5.5 h of sleep for the last 1 month and normal group of the same number of participants who got 7 h of sleep. We constructed the structural brain networks from the dMRI data and analyzed them using graph theoretical approaches...
September 30, 2016: Neuroreport
B F Kjølby, A R Khan, A Chuhutin, L Pedersen, J B Jensen, S Jakobsen, D Zeidler, R Sangill, J R Nyengaard, S N Jespersen, B Hansen
Diffusion kurtosis imaging (DKI) is sensitive to tissue microstructure and may therefore be useful in the diagnosis and monitoring of disease in brain and body organs. Generally, diffusion magnetic resonance imaging (dMRI) in the body is challenging because of the heterogeneous body composition, which can cause image artefacts as a result of chemical shifts and susceptibility differences. In addition, the abdomen possesses physiological factors (e.g. breathing, heartbeat, blood flow) which may severely reduce image quality, especially when echo planar imaging is employed, as is typical in dMRI...
October 12, 2016: NMR in Biomedicine
Zora Kikinis, Kang Ik K Cho, Ioana L Coman, Petya D Radoeva, Sylvain Bouix, Yingying Tang, Ryan Eckbo, Nikos Makris, Jun Soo Kwon, Marek Kubicki, Kevin M Antshel, Wanda Fremont, Martha E Shenton, Wendy R Kates
BACKGROUND: 22q11.2 Deletion Syndrome (22q11DS) is considered to be a promising cohort to explore biomarkers of schizophrenia risk based on a 30 % probability of developing schizophrenia in adulthood. In this study, we investigated abnormalities in the microstructure of white matter in adolescents with 22q11DS and their specificity to prodromal symptoms of schizophrenia. METHODS: Diffusion Magnetic Resonance Imaging (dMRI) data were acquired from 50 subjects with 22q11DS (9 with and 41 without prodromal psychotic symptoms), and 47 matched healthy controls (mean age 18 +/-2 years)...
October 11, 2016: Brain Imaging and Behavior
Nicole Labra, Pamela Guevara, Delphine Duclap, Josselin Houenou, Cyril Poupon, Jean-François Mangin, Miguel Figueroa
This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced...
October 8, 2016: Neuroinformatics
Laurie M Baker, Ryan P Cabeen, Sarah Cooley, David H Laidlaw, Robert H Paul
This paper reviews basic methods and recent applications of length-based fiber bundle analysis of cerebral white matter using diffusion magnetic resonance imaging (dMRI). Diffusion weighted imaging (DWI) is a dMRI technique that uses the random motion of water to probe tissue microstructure in the brain. Diffusion tensor imaging (DTI) is an extension of DWI that measures the magnitude and direction of water diffusion in cerebral white matter, using either voxel-based scalar metrics or tractography-based analyses...
April 2016: Technology and Innovation
Nicolás F Lori, Augustin Ibañez, Rui Lavrador, Lucia Fonseca, Carlos Santos, Rui Travasso, Artur Pereira, Rosaldo Rossetti, Nuno Sousa, Victor Alves
High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings...
November 2016: Journal of Medical Systems
Victor Manuel Vergara, Andrew Mayer, Eswar Damaraju, Kent Kiehl, Vince D Calhoun
Traumatic brain injury (TBI) may adversely affect a person's thinking, memory, personality, and behavior. While mild TBI (mTBI) diagnosis is challenging, there is a risk for long-term psychiatric, neurologic, and psychosocial problems in some patients that motivates the search for new and better biomarkers. Recently, diffusion magnetic resonance imaging (dMRI) has shown promise in detecting mTBI, but its validity is still being investigated. Resting state functional network connectivity (rsFNC) is another approach that is emerging as a promising option for the diagnosis of mTBI...
September 27, 2016: Journal of Neurotrauma
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) provides information about the microstructure of white matter in the human brain. From dMRI, streamlining tractography is often used to reconstruct computational representations of white matter tracts from which differences in structural connectivity can be explored. In the fiber tracking process, anatomical information can help reduce tracking errors caused by crossing fibers and image noise. In this paper, we propose a Bayesian method for estimating fiber orientations (FOs) guided by anatomical tract information using diffusion tensor imaging (DTI), which is a standard clinical and research dMRI protocol...
September 19, 2016: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography...
September 16, 2016: Medical Image Analysis
Maxime Chamberland, Benoit Scherrer, Sanjay P Prabhu, Joseph Madsen, David Fortin, Kevin Whittingstall, Maxime Descoteaux, Simon K Warfield
Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighboring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer's loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer's loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery...
September 20, 2016: Human Brain Mapping
Rafael L O'Halloran, Alexander G Chartrain, Jonathan J Rasouli, Ritesh A Ramdhani, Brian Harris Kopell
BACKGROUND: The caudal zona incerta (cZI) is an increasingly popular deep brain stimulation (DBS) target for the treatment of tremor-predominant disease. The dentatorubrothalamic tract (DRTT) is a white matter fiber bundle that traverses the cZI and can be identified using diffusion-weighted magnetic resonance imaging fiber tractography to ascertain its precise course. In this report, we compare 2 patient cases of cZI DBS, a responder and a nonresponder. CASE DESCRIPTION: Patient 1 (responder) is a 65-year-old man with medically refractory Parkinson disease who underwent bilateral DBS lead placement in the cZI...
September 1, 2016: World Neurosurgery
Kurt Schilling, Yurui Gao, Iwona Stepniewska, Ann S Choe, Bennett A Landman, Adam W Anderson
PURPOSE: Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions...
August 29, 2016: Magnetic Resonance Imaging
Brian Hansen, Sune Nørhøj Jespersen
Here we describe and provide diffusion magnetic resonance imaging (dMRI) data that was acquired in neural tissue and a physical phantom. Data acquired in biological tissue includes: fixed rat brain (acquired at 9.4 T) and spinal cord (acquired at 16.4 T) and in normal human brain (acquired at 3 T). This data was recently used for evaluation of diffusion kurtosis imaging (DKI) contrasts and for comparison to diffusion tensor imaging (DTI) parameter contrast. The data has also been used to optimize b-values for ex vivo and in vivo fast kurtosis imaging...
2016: Scientific Data
Matteo Figini, Alessandro Scotti, Stefania Marcuzzo, Silvia Bonanno, Francesco Padelli, Victoria Moreno-Manzano, José Manuel García-Verdugo, Pia Bernasconi, Renato Mantegazza, Maria Grazia Bruzzone, Ileana Zucca
Diffusion-weighted Magnetic Resonance Imaging (dMRI) has relevant applications in the microstructural characterization of the spinal cord, especially in neurodegenerative diseases. Animal models have a pivotal role in the study of such diseases; however, in vivo spinal dMRI of small animals entails additional challenges that require a systematical investigation of acquisition parameters. The purpose of this study is to compare three acquisition protocols and identify the scanning parameters allowing a robust estimation of the main diffusion quantities and a good sensitivity to neurodegeneration in the mouse spinal cord...
2016: PloS One
Jung Sun Lee, Chang-Yoon Kim, Yeon Ho Joo, Dominick Newell, Sylvain Bouix, Martha E Shenton, Marek Kubicki
INTRODUCTION: Diffusion weighted MRI (dMRI) is a method sensitive to pathological changes affecting tissue microstructure. Most dMRI studies in schizophrenia, however, have focused solely on white matter. There is a possibility, however, that subtle changes in diffusivity exist in gray matter (GM). Accordingly, we investigated diffusivity in GM in patients with recent onset schizophrenia. METHODS: We enrolled 45 patients and 21 age and sex-matched healthy controls...
October 2016: Schizophrenia Research
Oscar Esteban, Dominique Zosso, Alessandro Daducci, Meritxell Bach-Cuadra, María J Ledesma-Carbayo, Jean-Philippe Thiran, Andres Santos
The verification and validation of segmentation and registration methods is a necessary assessment in the development of new processing methods. However, verification and validation of diffusion MRI (dMRI) processing methods is challenging for the lack of gold-standard data. The data described here are related to the research article entitled "Surface-driven registration method for the structure-informed segmentation of diffusion MR images" [1], in which publicly available data are used to derive golden-standard reference-data to validate and evaluate segmentation and registration methods in dMRI...
September 2016: Data in Brief
Lee B Reid, Ross Cunnington, Roslyn N Boyd, Stephen E Rose
Diffusion MRI (dMRI) tractography analyses are difficult to perform in the presence of brain pathology. Automated methods that rely on cortical parcellation for structural connectivity studies often fail, while manually defining regions is extremely time consuming and can introduce human error. Both methods also make assumptions about structure-function relationships that may not hold after cortical reorganisation. Seeding tractography with functional-MRI (fMRI) activation is an emerging method that reduces these confounds, but inherent smoothing of fMRI signal may result in the inclusion of irrelevant pathways...
2016: PloS One
Filip Szczepankiewicz, Danielle van Westen, Elisabet Englund, Carl-Fredrik Westin, Freddy Ståhlberg, Jimmy Lätt, Pia C Sundgren, Markus Nilsson
The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery...
July 19, 2016: NeuroImage
Jesper L R Andersson, Mark S Graham, Enikő Zsoldos, Stamatios N Sotiropoulos
Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence...
November 1, 2016: NeuroImage
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