Diffusion tensor imaging of the median nerve at 3.0 T using different MR scanners: Agreement of FA and ADC measurements
Department of Radiology, University Hospital, Raemistrasse 100, Zurich, 8091, Switzerland. Electronic address: . European journal of radiology
(Impact Factor: 2.37).
06/2013; 82(10). DOI: 10.1016/j.ejrad.2013.05.011
To assess the agreement of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the median nerve on 3.0 T MR scanners from different vendors.
Materials and methods:
IRB approved study including 16 healthy volunteers (9 women; mean age 30.6 ± 5.3 years). Diffusion tensor imaging (DTI) of the dominant wrist was performed on three 3.0 T MR scanners (GE, Siemens, Philips) using similar imaging protocols and vendor-proprietary hard- and software. Intra-, inter-reader and inter-vendor agreements were assessed.
ICCs for intra-/inter-reader agreements ranged from 0.843-0.970/0.846-0.956 for FA, and 0.840-0.940/0.726-0.929 for ADC, respectively. ANOVA analysis identified significant differences for FA/ADC measurements among vendors (p < 0.001/p < 0.01, respectively). Overall mean values for FA were 0.63 (SD ± 0.1) and 0.999 × 10(-3)mm(2)/s (SD ± 0.134 × 10(-3)) for ADC. A significant negative measurement bias was found for FA values from the GE scanner (-0.05 and -0.07) and for ADC values from the Siemens scanner (-0.053 and -0.063 × 10(-3)mm(2)/s) as compared to the remainder vendors
FA and ADC values of the median nerve obtained on different 3.0 T MR scanners differ significantly, but are in comparison to the standard deviation of absolute values small enough to not have an impact on larger group studies or when substantial diffusion changes can be expected. However, caution is warranted in an individual patient when interpreting diffusion values from different scanner acquisitions.
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ABSTRACT: The aim of this study was to evaluate whether anterior cruciate ligament (ACL) and ACL graft could be imaged using diffusion tensor imaging (DTI) and to provide the DTI metrics for ACL and grafts.
Magnetic resonance imaging and DTI were performed in 40 healthy volunteers and 15 patients with ACL reconstruction. Fiber tracking and other postprocessing steps were performed on the workstation. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of ACL and grafts were determined.
The courses of ACL and grafts were analyzed quantitatively using FA and ADC, and tractography illustrated nicely the 3-dimensional courses of the fiber bundles and corresponded well to the known anatomy. There was no significant difference in the mean FA and ADC values of the sexes.
Diffusion tensor imaging can be used to image and visualize the structure of ACL and ACL grafts.
Available from: Steve Hui
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ABSTRACT: High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
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ABSTRACT: MR neurography, diffusion tensor imaging (DTI) and tractography at 3 Tesla were evaluated for the assessment of patients with ulnar neuropathy at the elbow (UNE).
Axial T2-weighted and single-shot DTI sequences (16 gradient encoding directions) were acquired, covering the cubital tunnel of 46 patients with clinically and electrodiagnostically confirmed UNE and 20 healthy controls. Cross-sectional area (CSA) was measured at the retrocondylar sulcus and FA and ADC values on each section along the ulnar nerve. Three-dimensional nerve tractography and T2-weighted neurography results were independently assessed by two raters.
Patients showed a significant reduction of ulnar nerve FA values at the retrocondylar sulcus (p = 0.002) and the deep flexor fascia (p = 0.005). At tractography, a complete or partial discontinuity of the ulnar nerve was found in 26/40 (65 %) of patients. Assessment of T2 neurography was most sensitive in detecting UNE (sensitivity, 91 %; specificity, 79 %), followed by tractography (88 %/69 %). CSA and FA measurements were less effective in detecting UNE.
T2-weighted neurography remains the most sensitive MR technique in the imaging evaluation of clinically manifest UNE. DTI-based neurography at 3 Tesla supports the MR imaging assessment of UNE patients by adding quantitative and 3D imaging data.
• DTI and tractography support conventional MR neurography in the detection of UNE • Regionally reduced FA values and discontinuous tractography patterns indicate UNE • T2-weighted MR neurography remains the imaging gold standard in cases of UNE • DTI-based ulnar nerve tractography offers additional topographic information in 3D.
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