Advanced MR neuroimaging modalities are becoming more available and useful as their value in central nervous system diagnosis and prognosis is more fully understood and studied. Specifically, diffusion tensor imaging (DTI) has become increasingly studied and utilized in recent years and has become incorporated by many radiologists into routine clinical practice with most research performed on traumatic brain injury. DTI is a variant of diffusion-weighted imaging (DWI) which utilizes a tissue water diffusion rate for image production. The first application of DWI to the human brain was performed in 1986 and since has become the gold standard for detecting acute stroke. DTI does not require contrast and is available on almost all modern MR scanners with relatively quick scan times for this sequence.
Random thermal motion, also known as Brownian motion, is water molecular diffusion in three-dimensional space. Isotropy is defined as uniformity in all directions and when applied to water molecules; isotropy occurs when the diffusion of water is entirely uninhibited (such as water movement in a glass of water). Anisotropy is when there is a directionality in the diffusion of water present, and the movement of water is no longer random (such as water movement along straws placed in a glass). The greater the anisotropy; the more directional and linear the diffusion of water molecules. Water molecules will diffuse differently through space depending on its tissue type, components, structure, architecture, and integrity; these principles allow clinically significant imaging to occur, particularly regarding DTI. DTI measures movement of water along axons, analogous to the straws in a glass of water.
As early as May of 2002, medical literature reported that DTI showed abnormalities in patients who suffered from mild brain trauma as compared to normal control subjects. “This study included five patients with mild traumatic brain injury (three men and two women) and ten volunteers with no known neurological disorders (five men and five women).” This study reported abnormalities in the patients with mild brain injury that were absent in the control subjects or the uninvolved sides of the injured patients' brains: “Patients displayed a significant reduction of diffusion anisotropy in several regions compared with the homologous ones in the contralateral hemisphere. Such differences were not observed in the control subjects. Significant reduction of diffusion anisotropy was also detected when diffusion tensor results from the patients were compared with those of the controls.”
DWI uses volume elements (voxels) as a statistical method for data collection. When a voxel contains scalar values constituting a vector, it is known as a tensor, which is where DTI received its name and explains the additional information provided through DTI. DTI MR settings can measure the diffusion of water along an axon is many directions, 6, 9, 33 and 90 directions are typical parameters used with 33 directions and above increasing confidence in accuracy. Ninety directions typically require upwards of 20 additional minutes in the MR scanner, therefore, may not be suitable for routine clinical practice. In effect, DTI will provide an indirect method of assessing neuroanatomy structure on a microscopic level using water molecules’ degree of anisotropy and structural orientation within a voxel. Therefore, the principal application for DTI is in the imaging of white matter, where the orientation, location, and anisotropy of the tracts can be measured and evaluated. The architecture of the axons in parallel bundles and their myelin sheaths facilitate the diffusion of the water molecules preferentially along their main direction.
There are a number of measures calculated using DTI that can provide quantitative power. One of the most widely used DTI measures is fractional anisotropy (FA). Others include mean diffusivity or apparent diffusion coefficient (ADC), radial (perpendicular) diffusivity, and axial (parallel) diffusivity. DTI uses mean diffusivity for the rate of molecular diffusion, FA for the summative direction of the diffusion which provides a prominent vector, axial diffusivity for the rate of diffusion parallel to the main vector, and radial diffusivity for the rate of diffusion perpendicular to the main vector.
FA quantifies directionality of diffusivity in a summative manner and is highly sensitive to change in microstructure, however, it can unspecific to the cause of change. Mean diffusivity quantifies cellular and membrane density where an increase in mean diffusivity indicates disease processes such as edema or necrosis. Radial diffusivity quantifies myelin neuropathology and increases with demyelination. Axial diffusivity quantifies axonal degeneration and increases with brain maturation.
FA values are a numerical value, based on the anisotropy of water along the axon, which reflects the health of the axon. Abnormal FA values indicate axonal damage. FA values can be calculated utilizing region of interest (ROI) method, whole brain analysis (Voxel-Based analysis) or tract-based spatial statistics. Whole brain analysis is gaining popularity due to its automation and ability to analyze more tracts. ROI method, where the regions to be analyzed are traced by a technologist and then analyzed by a computer, remains reliable and replicable. One of the more common and standardized ROI methods is the segmented corpus callosal values. Being the largest axonal tract in the brain, damage to the corpus callosum is well described following head trauma and other pathologies.
FA values can vary depending on which of the above three analyzing methods is used and other factors such as MR technique and type of post-processing performed. Utilizing standardized technique, FA values are highly reproducible and are not technologist dependent and can be subjectively interpreted by a radiologist as well as roughly compared to select values in the literature.
Pediatric normal values are slightly less than those of adults. However, most changes occur by age 5, and 90% of adult FA values are achieved by 11 years of age in the corpus callosum. After adulthood, FA values tend to decrease with age linearly.
Additionally, FA values comparison across different scanners is now possible, even if those scanners are utilizing different techniques. This is achieved using ‘human phantom phenomena’ where a single subject is scanned on two different scanners, enabling a comparison between scanners by a scaling factor, or even to normative databases performed on a different scanner(s).
3D reconstructions of the tensor tracts are accomplished with computer modeling and can beautifully illustrate the fiber tracts, identify pathology and aid neurosurgeons. (Figure 1 illustrates a normal DTI 3D reconstruction on the right as compared to the image on the left showing diffuse frontal lobe injury and global brain injury in a patient with prominent bilateral frontal contusions)
DTI is generally sensitive but has lower specificity. Specificity can increase by combining the interpretation of DTI with the clinical history and the other findings on conventional imaging.
Artifacts and Noise
Artifacts are seen in all modalities of imaging including submodalities of MRI and have many causes. Artifacts can result in misinterpretation of imaging findings if the radiologist is unaware of these artifacts. With a thorough understanding of artifacts associated with DTI, measures should be taken to decrease the artifacts and to become more aware of unavoidable artifacts.
A limitation of DTI is that it currently has a low signal to noise ratio (SNR), which may increase scanning times. SNR compares the level of background noise to the level of the signal obtained. When the noise is too great in comparison to the signal (low SNR), image quality is poor. Two ways to improve on this signal to noise ratio is to either increase scan times or reduce image resolution. Long scan times can result in patient motion artifact which also can cause artifact.
Voxels continue to decrease in size, but a current limitation in DTI is the size of voxels. Low anisotropy shown in a particular voxel may seem to represent unorganized tissue contained within. However, this may just be apparent and what actually lies within the particular voxel is multiple anisotropic structures orientated in multiple directions resulting in isotropy. This means that diffusion anisotropy will only show on DTI when all structures coursing a voxel align on a microscopic (microstructure, myelin sheath, protein filaments) and macroscopic (axons and dendrites) scale. In the cortex, low FA occurs because of macroscopic disorganization. As image resolution improves, the cortex will likely show higher patterns of anisotropy.
Other causes resulting in artifacts can include eddy currents and motion. Changing data acquisition methods can help decrease these artifacts.
DTI has now moved beyond research investigation and is becoming incorporated into routine clinical practice. However, some have urged caution in the interpretation of DTI and other advanced imaging sequences at the individual level, including a 9/13/18 RSNA guideline statement. A minority of studies have not found a relationship between DTI and mild traumatic brain injury, although the majority of the literature is supportive. Given the different techniques in performing DTI, and the wide variety of application, this caution is well taken. However, if the interpreting radiologist has experience in DTI and has developed a standardized, replicable technique concordant with the literature, and particularly if a trauma database or normative database is available, these valid criticisms become moot, and DTI can be interpreted in individual patients. This approach has backing in the literature and studies performed on individual patients demonstrate reproducibility and accuracy in diagnosing individual patients with TBI. Specifically, DTI is most powerful when interpreted in concert with the clinical history and other findings on conventional imaging. The most comprehensive peer review article on TBI and DTI concluded:
"Despite significant variability in sample characteristics, technical aspects of imaging, and analysis approaches, the consensus is that DTI effectively differentiates patients with TBI and controls, regardless of the severity and timeframe following injury. Furthermore, many have established a relationship between DTI measures and TBI outcomes."
DTI has proven to be highly sensitive to brain injury and a variety of pathological conditions and in many instances is becoming a routine part of clinical practice. Further research will continue to expand the application of DTI in evaluating neurological conditions.
Although much of the research on DTI has been in traumatic brain injury, other applications include aid in diagnosis, prognosis, and classifications of stroke, brain tumors, neurodegenerative diseases, developmental disorders, neuropsychiatric disorders, movement disorders, and neurogenetic developmental disorders. The following topics are a few examples of described uses of DTI.
Traumatic Brain Injury/Diffuse Axonal Injury
Diffuse axonal injury (DAI) can be seen in traumatic brain injury. Diffuse axonal injury is diffuse brain injury damage to axonal microstructures and can potentially appear normal on traditional CT and MRI when there is an absence of macroscopic tissue disruption. Additionally, if there are macroscopic findings of DAI on CT or MRI, it indicates poorer outcomes. A predominance of published literature on DTI is about diffuse axonal injury its close correlation with decreased FA values throughout specified regions of the brain. The clinical effectiveness of DTI has been compared to control subjects for validation in various scenarios involving the use of MRI to help diagnose brain injury. There are numerous peer-reviewed and case-control studies in the medical literature allowing for individual evaluations of brain-injured patients using DTI. The comparison of cases (patients with a history of traumatic brain injury) and controls (no history of traumatic brain injury) utilizing DTI is an accepted methodology and standard technique utilized in order to demonstrate the clinical utility of DTI in adding incremental diagnostic information to structural MRI, multimodal MR studies, other imaging modalities and the clinical condition. Additionally, peer reviewed articles on DTI and TBI have demonstrated regional associations between findings on conventional imaging and FA values and 3D DTI (as demonstrated at our centers in Figure 1). 
Carbon Monoxide Poisoning
DTI has utility for studying the brains of patients after CO poisoning. The peer-reviewed literature supports the use of DTI for identifying and longitudinally monitoring the changes in the brain that can occur secondary to CO poisoning. DTI has been shown to demonstrate the evolution of white matter injury in CO encephalopathy progression over months, and to correlate with neuropsychological deficits.
DTI has also demonstrated evidence of demyelination with abnormal FA values in the central nervous system of patients who develop chronic neurological symptoms in the subacute phase after CO intoxication. A decrease in FA values correlates with clinically observed neurological symptoms and, conversely, improvement in FA values may parallel improvement in the neurological condition.
Hypoxic Brain Injury
A recent 2018 multicenter review article demonstrated DTI might be more predictive of neurologic outcome than conventional MRI in patients with cardiac arrest and hypoxic brain injury.
As the brain develops, diffusivity is found to decline particularly in the direction that myelination would be oriented. Damage to myelin sheath affects diffusivity. DTI has been used in multiple sclerosis to monitor disease progression and correlate with clinical status. Suggestions are that radial diffusivity is a promising biomarker.
The application of DTI in instances of neoplasia is used mostly for surgical planning. With DTI and tractography, a more in depth layout of neuroanatomy and specific elequant white matter tracts can be seen allowing a neurosurgeon to plan precise surgical approaches to minimize damage to the critical tracts and preserving vital functions such as motor capabilities, language, and vision. DTI has even been used intraoperatively to compensate for shifting tissue to minimize complications.
Another application of DTI in neoplasia is using FA values. Correlations have been found between FA values and cell density. In neoplasia with high cell proliferation and density such as glioblastomas, higher FA values have been reported.
Some neoplastic disease requires radiation and DTI has found that FA decreases in areas targeted by radiation. By observing the changes of FA in the anatomy, it can potentially be used to monitor the dose distribution.
A relatively new limb of DTI research has been showing correlations between DTI findings and developmental neuropathology in a fetus or neonate. Discovering utility of DTI in fetuses and neonates has been hopeful for a field that has limited diagnostic imaging options.
Extracranial DTI applications
White Matter Tractography and future research
Using the assumption that diffusion anisotropy represents white matter bundles, these bundles of aligned voxels can be parcellated, providing a 3D map of white matter bundle tracts. Fiber tracking or reconstruction by starting with a voxel and propagating forward following the anisotropic path until encountering isotropy is also known as tractography and can give insight into brain connectivity. Further study and mapping of these tracts, in conjunction with fMRI, resting state and other MRI submodalities, remains exciting cutting edge research. DTI remains in the research stage regarding neuropsychological conditions such as autism and ADHD.
Diffusion tensor imaging is a specialized diagnostic imaging tool utilizing diffusion-weighted imaging of MRI. DTI has required and will continue to require many fields of expertise in advancing its utility in everyday patient care. Continued research on its growing capabilities will make more healthcare practitioners aware of its value and more radiologists comfortable with interpreting DTI. As radiologists see the value in DTI, more recommendations will be made to acquire DTI to aid with diagnoses and prognosis. As with any modality or imaging tool, discussion with referring physicians, physician assistants, and nurse practitioners to increase awareness of this new option is helpful. For example, utilizing DTI for surgical planning would require surgeons knowing they can trust the findings made by radiologists with this diagnostic tool.
|||Le Bihan D,Breton E,Lallemand D,Grenier P,Cabanis E,Laval-Jeantet M, MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986 Nov [PubMed PMID: 3763909]|
|||A hitchhiker's guide to diffusion tensor imaging., Soares JM,Marques P,Alves V,Sousa N,, Frontiers in neuroscience, 2013 [PubMed PMID: 23486659]|
|||Arfanakis K,Cordes D,Haughton VM,Carew JD,Meyerand ME, Independent component analysis applied to diffusion tensor MRI. Magnetic resonance in medicine. 2002 Feb [PubMed PMID: 11810680]|
|||Thaler HT,Ferber PW,Rottenberg DA, A statistical method for determining the proportions of gray matter, white matter, and CSF using computed tomography. Neuroradiology. 1978 [PubMed PMID: 740152]|
|||A decade of DTI in traumatic brain injury: 10 years and 100 articles later., Hulkower MB,Poliak DB,Rosenbaum SB,Zimmerman ME,Lipton ML,, AJNR. American journal of neuroradiology, 2013 Nov-Dec [PubMed PMID: 23306011]|
|||Diffusion tensor imaging: concepts and applications., Le Bihan D,Mangin JF,Poupon C,Clark CA,Pappata S,Molko N,Chabriat H,, Journal of magnetic resonance imaging : JMRI, 2001 Apr [PubMed PMID: 11276097]|
|||Diffusion tensor imaging of the brain., Alexander AL,Lee JE,Lazar M,Field AS,, Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 2007 Jul [PubMed PMID: 17599699]|
|||Oh JS,Suk Park K,Chan Song I,Ju Kim S,Hwang J,Chung A,Kyoon Lyoo I, Fractional anisotropy-based divisions of midsagittal corpus callosum. Neuroreport. 2005 Mar 15 [PubMed PMID: 15729129]|
|||Hofer S,Frahm J, Topography of the human corpus callosum revisited--comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. NeuroImage. 2006 Sep [PubMed PMID: 16854598]|
|||Kim EY,Park HJ,Kim DH,Lee SK,Kim J, Measuring fractional anisotropy of the corpus callosum using diffusion tensor imaging: mid-sagittal versus axial imaging planes. Korean journal of radiology. 2008 Sep-Oct [PubMed PMID: 18838846]|
|||Rutgers DR,Fillard P,Paradot G,Tadié M,Lasjaunias P,Ducreux D, Diffusion tensor imaging characteristics of the corpus callosum in mild, moderate, and severe traumatic brain injury. AJNR. American journal of neuroradiology. 2008 Oct [PubMed PMID: 18617586]|
|||Li Z,Li C,Fan L,Jiang G,Wu J,Jiang T,Yin X,Wang J, Altered microstructure rather than morphology in the corpus callosum after lower limb amputation. Scientific reports. 2017 Mar 17 [PubMed PMID: 28303959]|
|||Rimkus Cde M,Junqueira Tde F,Callegaro D,Otaduy MC,Leite Cda C, Segmented corpus callosum diffusivity correlates with the Expanded Disability Status Scale score in the early stages of relapsing-remitting multiple sclerosis. Clinics (Sao Paulo, Brazil). 2013 [PubMed PMID: 24037007]|
|||Lövdén M,Bodammer NC,Kühn S,Kaufmann J,Schütze H,Tempelmann C,Heinze HJ,Düzel E,Schmiedek F,Lindenberger U, Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia. 2010 Nov [PubMed PMID: 20816877]|
|||Parizel PM,Ozsarlak,Van Goethem JW,van den Hauwe L,Dillen C,Verlooy J,Cosyns P,De Schepper AM, Imaging findings in diffuse axonal injury after closed head trauma. European radiology. 1998 [PubMed PMID: 9683701]|
|||Feldman HM,Yeatman JD,Lee ES,Barde LH,Gaman-Bean S, Diffusion tensor imaging: a review for pediatric researchers and clinicians. Journal of developmental and behavioral pediatrics : JDBP. 2010 May [PubMed PMID: 20453582]|
|||Palacios EM,Martin AJ,Boss MA,Ezekiel F,Chang YS,Yuh EL,Vassar MJ,Schnyer DM,MacDonald CL,Crawford KL,Irimia A,Toga AW,Mukherjee P, Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study. AJNR. American journal of neuroradiology. 2017 Mar [PubMed PMID: 28007768]|
|||Venkatraman VK,Gonzalez CE,Landman B,Goh J,Reiter DA,An Y,Resnick SM, Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths. NeuroImage. 2015 Oct 1 [PubMed PMID: 26146196]|
|||Farrell JA,Landman BA,Jones CK,Smith SA,Prince JL,van Zijl PC,Mori S, Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. Journal of magnetic resonance imaging : JMRI. 2007 Sep [PubMed PMID: 17729339]|
|||Rohde GK,Barnett AS,Basser PJ,Marenco S,Pierpaoli C, Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magnetic resonance in medicine. 2004 Jan [PubMed PMID: 14705050]|
|||Meltzer CC,Sze G,Rommelfanger KS,Kinlaw K,Banja JD,Wolpe PR, Guidelines for the ethical use of neuroimages in medical testimony: report of a multidisciplinary consensus conference. AJNR. American journal of neuroradiology. 2014 Apr [PubMed PMID: 23988754]|
|||Wintermark M,Sanelli PC,Anzai Y,Tsiouris AJ,Whitlow CT, Imaging evidence and recommendations for traumatic brain injury: advanced neuro- and neurovascular imaging techniques. AJNR. American journal of neuroradiology. 2015 Feb [PubMed PMID: 25424870]|
|||Mac Donald CL,Johnson AM,Cooper D,Nelson EC,Werner NJ,Shimony JS,Snyder AZ,Raichle ME,Witherow JR,Fang R,Flaherty SF,Brody DL, Detection of blast-related traumatic brain injury in U.S. military personnel. The New England journal of medicine. 2011 Jun 2 [PubMed PMID: 21631321]|
|||Bouix S,Pasternak O,Rathi Y,Pelavin PE,Zafonte R,Shenton ME, Increased gray matter diffusion anisotropy in patients with persistent post-concussive symptoms following mild traumatic brain injury. PloS one. 2013 [PubMed PMID: 23776631]|
|||Kim N,Branch CA,Kim M,Lipton ML, Whole brain approaches for identification of microstructural abnormalities in individual patients: comparison of techniques applied to mild traumatic brain injury. PloS one. 2013 [PubMed PMID: 23555665]|
|||Lipton ML,Kim N,Park YK,Hulkower MB,Gardin TM,Shifteh K,Kim M,Zimmerman ME,Lipton RB,Branch CA, Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: intersubject variation, change over time and bidirectional changes in anisotropy. Brain imaging and behavior. 2012 Jun [PubMed PMID: 22684769]|
|||Tae WS,Ham BJ,Pyun SB,Kang SH,Kim BJ, Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders. Journal of clinical neurology (Seoul, Korea). 2018 Apr [PubMed PMID: 29504292]|
|||Yuh EL,Cooper SR,Mukherjee P,Yue JK,Lingsma HF,Gordon WA,Valadka AB,Okonkwo DO,Schnyer DM,Vassar MJ,Maas AI,Manley GT, Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a TRACK-TBI study. Journal of neurotrauma. 2014 Sep 1 [PubMed PMID: 24742275]|
|||Aoki Y,Inokuchi R, A voxel-based meta-analysis of diffusion tensor imaging in mild traumatic brain injury. Neuroscience and biobehavioral reviews. 2016 Jul [PubMed PMID: 27133211]|
|||Ewing-Cobbs L,Johnson CP,Juranek J,DeMaster D,Prasad M,Duque G,Kramer L,Cox CS,Swank PR, Longitudinal diffusion tensor imaging after pediatric traumatic brain injury: Impact of age at injury and time since injury on pathway integrity. Human brain mapping. 2016 Nov [PubMed PMID: 27329317]|
|||Ware JB,Biester RC,Whipple E,Robinson KM,Ross RJ,Nucifora PG, Combat-related Mild Traumatic Brain Injury: Association between Baseline Diffusion-Tensor Imaging Findings and Long-term Outcomes. Radiology. 2016 Jul [PubMed PMID: 27022770]|
|||Kou Z,Wu Z,Tong KA,Holshouser B,Benson RR,Hu J,Haacke EM, The role of advanced MR imaging findings as biomarkers of traumatic brain injury. The Journal of head trauma rehabilitation. 2010 Jul-Aug [PubMed PMID: 20611045]|
|||Xu J,Rasmussen IA,Lagopoulos J,Håberg A, Diffuse axonal injury in severe traumatic brain injury visualized using high-resolution diffusion tensor imaging. Journal of neurotrauma. 2007 May [PubMed PMID: 17518531]|
|||Terajima K,Igarashi H,Hirose M,Matsuzawa H,Nishizawa M,Nakada T, Serial assessments of delayed encephalopathy after carbon monoxide poisoning using magnetic resonance spectroscopy and diffusion tensor imaging on 3.0T system. European neurology. 2008 [PubMed PMID: 17917459]|
|||Chang CC,Chang WN,Lui CC,Wang JJ,Chen CF,Lee YC,Chen SS,Lin YT,Huang CW,Chen C, Longitudinal study of carbon monoxide intoxication by diffusion tensor imaging with neuropsychiatric correlation. Journal of psychiatry [PubMed PMID: 20184809]|
|||Beppu T,Nishimoto H,Ishigaki D,Fujiwara S,Yoshida T,Oikawa H,Kamada K,Sasaki M,Ogasawara K, Assessment of damage to cerebral white matter fiber in the subacute phase after carbon monoxide poisoning using fractional anisotropy in diffusion tensor imaging. Neuroradiology. 2010 Aug [PubMed PMID: 20066405]|
|||Beppu T, The role of MR imaging in assessment of brain damage from carbon monoxide poisoning: a review of the literature. AJNR. American journal of neuroradiology. 2014 Apr [PubMed PMID: 23598831]|
|||Velly L,Perlbarg V,Boulier T,Adam N,Delphine S,Luyt CE,Battisti V,Torkomian G,Arbelot C,Chabanne R,Jean B,Di Perri C,Laureys S,Citerio G,Vargiolu A,Rohaut B,Bruder N,Girard N,Silva S,Cottenceau V,Tourdias T,Coulon O,Riou B,Naccache L,Gupta R,Benali H,Galanaud D,Puybasset L, Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. The Lancet. Neurology. 2018 Apr [PubMed PMID: 29500154]|
|||Aung WY,Mar S,Benzinger TL, Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging in medicine. 2013 Oct 1 [PubMed PMID: 24795779]|
|||Sbardella E,Tona F,Petsas N,Pantano P, DTI Measurements in Multiple Sclerosis: Evaluation of Brain Damage and Clinical Implications. Multiple sclerosis international. 2013 [PubMed PMID: 23606965]|
|||Klistorner A,Wang C,Fofanova V,Barnett MH,Yiannikas C,Parratt J,You Y,Graham SL, Diffusivity in multiple sclerosis lesions: At the cutting edge? NeuroImage. Clinical. 2016 [PubMed PMID: 27489769]|
|||Zanin E,Ranjeva JP,Confort-Gouny S,Guye M,Denis D,Cozzone PJ,Girard N, White matter maturation of normal human fetal brain. An in vivo diffusion tensor tractography study. Brain and behavior. 2011 Nov [PubMed PMID: 22399089]|
|||Cauley KA,Filippi CG, Diffusion-tensor imaging of small nerve bundles: cranial nerves, peripheral nerves, distal spinal cord, and lumbar nerve roots--clinical applications. AJR. American journal of roentgenology. 2013 Aug [PubMed PMID: 23883249]|
|||Rutman AM,Peterson DJ,Cohen WA,Mossa-Basha M, Diffusion Tensor Imaging of the Spinal Cord: Clinical Value, Investigational Applications, and Technical Limitations. Current problems in diagnostic radiology. 2018 Jul - Aug [PubMed PMID: 28869104]|
|||Chung MK,Hanson JL,Adluru N,Alexander AL,Davidson RJ,Pollak SD, Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging. Brain connectivity. 2017 Aug [PubMed PMID: 28657774]|
|||van Ewijk H,Heslenfeld DJ,Zwiers MP,Buitelaar JK,Oosterlaan J, Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis. Neuroscience and biobehavioral reviews. 2012 Apr [PubMed PMID: 22305957]|
|||Chen R,Jiao Y,Herskovits EH, Structural MRI in autism spectrum disorder. Pediatric research. 2011 May [PubMed PMID: 21289538]|