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

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena

Diffusion Imaging in the Rat Cervical Spinal Cord

Magnetic resonance imaging (MRI) is the state of the art approach for assessing the status of the spinal cord noninvasively, and can be used as a diagnostic and prognostic tool in cases of disease or injury. Diffusion weighted imaging (DWI), is sensitive to the thermal motion of water molecules and allows for inferences of tissue microstructure. This report describes a protocol to acquire and analyze DWI of the rat cervical spinal cord on a small-bore animal system. It demonstrates an imaging setup for the live anesthetized animal and recommends a DWI acquisition protocol for high-quality imaging, which includes stabilization of the cord and control of respiratory motion. Measurements with diffusion weighting along different directions and magnitudes (b-values) are used. Finally, several mathematical models of the resulting signal are used to derive maps of the diffusion processes within the spinal cord tissue that provide insight into the normal cord and can be used to monitor injury or disease processes noninvasively. The video component of this article can be found at http://www.jove.com/video/52390/ Introduction Magneti

Anisotropic Anomalous Diffusion assessed in the human brain by scalar invariant indices

A new method to investigate anomalous diffusion in human brain is proposed. The method has been inspired by both the stretched-exponential model proposed by Hall and Barrick (HB) and DTI. Quantities extracted using HB method were able to discriminate different cerebral tissues on the basis of their complexity, expressed by the stretching exponent gamma and of the anisotropy of gamma across different directions. Nevertheless, these quantities were not defined as scalar invariants like mean diffusivity and fractional anisotropy, which are eigenvalues of the diffusion tensor. We hypotesize instead that the signal may be espressed as a simple stretched-exponential only along the principal axes of diffusion, while in a generic direction the signal is modeled as a combination of three different stretched-exponentials. In this way, we derived indices to quantify both the tissue anomalous diffusion and its anisotropy, independently of the reference frame of the experiment. We tested and compare our new method with DTI and HB approaches applying them to 10 healty subjects brain at 3T. Our experimental results show that our parameters are highly correlated to intrinsic local geometry when compared to HB indices. Moreover, they offer a different kind of contrast when compared to DTI outputs. Specifically, our indices show a higher capability to discriminate among different areas of the corpus callosum, which are known to be associated to different axonal densities.Comment: 21 pages, 6 figures, 2 table

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator.

Deep brain stimulation (DBS) for Parkinson\u27s disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS

Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer.

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation

Evaluating the accuracy of diffusion MRI models in white matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking

Stability effects on results of diffusion tensor imaging analysis by reduction of the number of gradient directions due to motion artifacts: an application to presymptomatic Huntington's disease.

In diffusion tensor imaging (DTI), an improvement in the signal-to-noise ratio (SNR) of the fractional anisotropy (FA) maps can be obtained when the number of recorded gradient directions (GD) is increased. Vice versa, elimination of motion-corrupted or noisy GD leads to a more accurate characterization of the diffusion tensor. We previously suggest a slice-wise method for artifact detection in FA maps. This current study applies this approach to a cohort of 18 premanifest Huntington's disease (pHD) subjects and 23 controls. By 2-D voxelwise statistical comparison of original FA-maps and FA-maps with a reduced number of GD, the effect of eliminating GD that were affected by motion was demonstrated.We present an evaluation metric that allows to test if the computed FA-maps (with a reduced number of GD) still reflect a "true" FA-map, as defined by simulations in the control sample. Furthermore, we investigated if omitting data volumes affected by motion in the pHD cohort could lead to an increased SNR in the resulting FA-maps.A high agreement between original FA maps (with all GD) and corrected FA maps (i.e. without GD corrupted by motion) were observed even for numbers of eliminated GD up to 13. Even in one data set in which 46 GD had to be eliminated, the results showed a moderate agreement

Diffusion Tensor Imaging in a Large Longitudinal Series of Patients With Cervical Spondylotic Myelopathy Correlated With Long-Term Functional Outcome

BACKGROUND Fractional anisotropy (FA) of the high cervical cord correlates with upper limb function in acute cervical cord injury. We investigated the correlation between preoperative FA at the level of maximal compression and functional recovery in a group of patients after decompressive surgery for cervical spondylotic myelopathy (CSM). OBJECTIVE To determine the usefulness of FA as a biomarker for severity of CSM and as a prognostic biomarker for improvement after surgery. METHODS Patients received diffusion tensor imaging (DTI) scans preoperatively. FA values of the whole cord cross-section at the level of maximal compression and upper cervical cord (C1-2) were calculated. Functional status was measured using the modified Japanese Orthopedic Association (mJOA) scale preoperatively and at follow-up up to 2 yr. Regression analysis between FA and mJOA was performed. DTI at C4-7 was obtained in controls. RESULTS Forty-four CSM patients enrolled prior to decompression were compared with 24 controls. FA at the level of maximal compression correlated positively with preoperative mJOA score. Preoperative FA correlated inversely with recovery throughout the postoperative period. This was statistically significant at 12 mo postoperation and nearly so at 6 and 24 mo. Patients with preoperative FA0.55. CONCLUSION In the largest longitudinal study of this kind, FA promises a valid biomarker for severity of CSM and postoperative improvement. FA is an objective measure of function and could provide a basis for prognosis. FA is particularly useful if preoperative values are less than 0.55