Optimal Parameters and Location for Diffusion-Tensor Imaging in the Diagnosis of Carpal Tunnel Syndrome: A Prospective Matched Case-Control Study

Transcription

1 Musculoskeletal Imaging Original Research Kwon et al. Diffusion-Tensor Imaging of Carpal Tunnel Syndrome Musculoskeletal Imaging Original Research Bong Cheol Kwon 1 Sung Hye Koh 2 Su Yeon Hwang 3 Kwon BC, Koh SH, Hwang SY Keywords: carpal tunnel syndrome, diagnosis, diffusion-tensor imaging, fractional anisotropy DOI: /AJR B. C. Kwon and S. H. Koh contributed equally to this study. Received July 1, 2014; accepted after revision October 20, Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (grant no ) (B. C. Kwon). 1 Department of Orthopedic Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 896 Pyeongchondong, Dongangu, Anyang, Kyeonggido , South Korea. Address correspondence to B. C. Kwon (bckwon11@gmail.com). 2 Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea. 3 Department of Radiology, Seoul National University Bundang Hospital, Bundang, South Korea. AJR 2015; 204: X/15/ American Roentgen Ray Society Optimal Parameters and Location for Diffusion-Tensor Imaging in the Diagnosis of Carpal Tunnel Syndrome: A Prospective Matched Case-Control Study OBJECTIVE. The purpose of this study was to determine the optimal parameters and location for diffusion-tensor imaging in the diagnosis of carpal tunnel syndrome. SUBJE AND METHODS. A single 3-T MRI (single-shot echo-planar imaging pulse sequence; b value, 1000 s/mm 2 ) and nerve conduction study were performed prospectively for patients with carpal tunnel syndrome and age- and sex-matched control subjects. Fractional anisotropy, apparent diffusion coefficient, radial diffusivity, and parallel diffusivity of the median nerve were measured at the inlet, middle, and outlet of the carpal tunnel and were compared with the nerve conduction study parameters. RESULTS. A total of 50 patients with carpal tunnel syndrome and 50 control subjects were enrolled. Demographic data were comparable between the groups. For all three locations, mean fractional anisotropy increased significantly, and the mean radial diffusivity and apparent diffusion coefficient decreased significantly in carpal tunnel syndrome (p < 0.05). The carpal tunnel inlet had the largest and most consistent changes in diffusion-tensor imaging parameters. Fractional anisotropy measured at the carpal tunnel inlet had the highest diagnostic accuracy, as measured with ROC curves (AUC, 0.82). For a fractional anisotropy threshold of 0.44 or less at the carpal tunnel inlet, sensitivity was 72%; specificity, 82%; positive predictive value, 80%; and negative predictive value, 75%. CONCLUSION. The use of fractional anisotropy measured at the carpal tunnel inlet is optimal for diagnosing carpal tunnel syndrome. C arpal tunnel syndrome () is a compressive neuropathy of the median nerve at the wrist. It is the most common compressive neuropathy of the peripheral nerves, affecting 2 5% of the general population, particularly women in their sixth decade of life [1]. Nerve conduction studies (NCS) are frequently used for confirming the diagnosis, deciding on surgery, and predicting outcomes of. However, despite being considered the reference standard, NCS have multiple pitfalls, such as dependence on the examiner s expertise, a long examination time, and being painful. Diffusion-tensor imaging (DTI) has emerged as a promising alternative to NCS in the diagnosis of. DTI is an MRI-based technique that entails diffusion of water molecules as a probe to detect microstructural changes in anisotropic tissues [2, 3]. Several previous studies have shown the usefulness of DTI for differentiating patients with from healthy subjects [4 7]. However, most of the previ- ous studies were conducted with a small, selected series of cases and unmatched control subjects [4 7], carrying the risks of biased results and limited generalizability. In addition, many aspects of DTI for diagnosing have not been standardized. The most useful DTI parameter for diagnosing has not been determined. Furthermore, the optimal location for measuring DTI parameters has not been studied, even though measurement location can be a source of variation because fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values depend on anatomic location [5, 8, 9]. The purpose of this study was to determine the optimal parameters and location for DTI in the diagnosis of. Subjects and Methods Study Participants The local institutional review board approved this study, and written informed consent was obtained from all participants before study enrollment. Study participants were recruited prospectively 1248 AJR:204, June 2015

2 Diffusion-Tensor Imaging of Carpal Tunnel Syndrome from a single tertiary referral center between June 2011 and February We included patients older than 18 years with a clinical diagnosis of confirmed with NCS. The exclusion criteria were pregnancy, claustrophobia, implanted materials incompatible with MRI, a medical condition affecting the peripheral nerves (e.g., rheumatoid arthritis, hypothyroidism, or chronic renal failure), a neurologic disorder, or a history of wrist trauma or surgery. When was present in both hands, the more severely involved hand was chosen for the study. Healthy control volunteers were recruited with a leaflet posted on the hospital bulletin board; control subjects with symptoms were patients visiting our outpatient clinic who presented with pain or tingling in the three radial fingers. The final diagnoses in the control subjects with symptoms were trigger finger (10 subjects), de Quervain disease (five subjects), osteoarthritis (five subjects), and cervical herniated intervertebral disks (four subjects). The control subjects were examined for absence of and were matched with patients for age, sex, and laterality of the involved hand. We applied the same exclusion criteria to control subjects and patients. Study participants underwent clinical evaluation, NCS, and DTI. NCS results were classified as minimal, mild, moderate, severe, or extreme according to the grading scheme of Padua et al. [10]. DTI and NCS were conducted in the same week. MRI and Diffusion-Tensor Imaging Protocol MRI was performed with a 3-T MRI system (Achieva, Philips Healthcare) with a dedicated A C eight-channel wrist coil (Achieva Sense, Philips Healthcare). The participants lay in the prone position with their shoulder elevated and elbow extended over their head to align the wrist optimally in the bore of the MRI unit. The wrist was immobilized with cushions and bandages. Axial T2- weighted multishot turbo spin-echo images were obtained before DTI for anatomic reference (TR/ TE, 3719/80; FOV, mm; reconstructed voxel size, mm; number of signals acquired, 1; acquisition time, 2 minutes 51 seconds). Next, DTI in the axial plane was performed with a spin-echo single-shot diffusion-weighted echoplanar imaging sequence with a b value of 1000 s/ mm 2 in 15 different directions along with baseline (b = 0) T2-weighted echo-planar images (TR/TE, 7266/91; FOV, mm; reconstructed voxel size, mm; slice gap, 0; number of slices, 45; number of signals acquired, 5; acquisition time, 10 minutes 27 seconds). The total examination time was 13 minutes 18 seconds. Postprocessing and Quantitative Analysis Two radiologists (10 and 4 years of experience with MRI) blinded to the participants clinical and electrophysiologic information performed the DTI analysis. After 1 month, one of the readers performed a second analysis blinded to enable calculation of intraobserver reliability. The FA and ADC images were generated from raw DTI data by use of vendor-provided software (Achieva release , Philips Healthcare). To reduce motion and eddy current artifacts, we performed B D coregistration by matching the FA and ADC images with the b0 images by use of the same vendor-provided software. This process served to correct the DTI image misalignment induced by eddy currents or motion and entailed several different methods, such as affine coregistration and rigid body transformation [11, 12]. The median nerve was identified on b0, FA, and ADC images at three anatomic locations in the carpal tunnel (i.e., the inlet, middle, and outlet of the tunnel) with the corresponding T2-weighted image as a reference. The carpal tunnel inlet was identified by the pisiform bone and proximal end of the flexor retinaculum. The carpal tunnel outlet was identified by the hook of the hamate and the distal end of the flexor retinaculum. The middle of the carpal tunnel was defined as the location equally distant from both the inlet and outlet. The ROI was manually placed within the margin of the median nerve on b0 images in a voxel-based manner (Fig. 1). Four DTI parameters, namely, FA, ADC, radial diffusivity (RD), and parallel diffusivity (PD), were calculated. PD is the value of the primary eigenvector and indicates diffusivity along the longitudinal direction of an anisotropic tissue. RD is a mean of two minor eigenvectors that are orthogonal to the primary eigenvector and specifies diffusivity along the transverse direction of an anisotropic tissue. ADC is a simple mean of the three eigenvectors and is a measure of overall diffusivity. FA is a measure of the degree of directionality; the values range from 0 to 1, 1 indicating diffusion entirely along a single direction [13, 14]. Fig year-old woman with carpal tunnel syndrome. Placement of ROIs at carpal tunnel inlet. A, T2-weighted MR image shows median nerve (arrow) as oval structure with intermediate signal intensity under transverse carpal ligament. B, Direction-encoded color map shows median nerve (arrow) as blue structure, representing its high directionality, but with considerably reduced resolution and contrast. C, MR image with b value of 0 at same level as A and B shows median nerve (arrow) with high signal intensity and clear margin, matching size and location of same nerve in A. D, MR image with b value of 0 shows ROI selected in voxel-based manner, including only voxels placed entirely within margin of median nerve (arrow). AJR:204, June

3 Kwon et al. Nerve Conduction Studies NCS were performed by a single rehabilitation medicine specialist using an electrodiagnostic study device (Toennies NeuroScreen Plus, Jaeger). The skin temperature over the first dorsal interosseous was maintained above 30.5 C. The physician conducting the NCS was blinded to the subject s symptoms, signs, and DTI results. NCS were conducted according to a standardized protocol based on American Academy of Neurology and American Association of Electrodiagnostic Medicine recommendations. They included measurements of median nerve distal motor latency and amplitude, index and middle finger wrist sensory latencies and amplitudes, sensory conduction velocities in wrist-palm and palm-digit segments, and a comparison with ulnar nerve ring finger wrist sensory latency [15]. TABLE 1: Demographic and Clinical Characteristics Parameter Statistical Analysis One-way ANOVA with the Bonferroni post hoc test was used to compare the three groups with respect to the DTI parameters measured at the same anatomic level and the clinical characteristics. A chi-square test or Fisher exact test was used to compare categoric variables among groups. A value of p < 0.05 was considered to indicate a statistically significant difference. A ROC curve was fitted to the DTI parameters to determine the optimal cutoff point for the DTI parameters and to evaluate their diagnostic performance. The optimal cutoff point, which indicates the maximum differentiating ability, can be derived from a ROC curve by calculation of the Youden index [16]. The optimal cutoff point was then used to calculate the sensitivity, specificity, positive predictive value, and negative predictive value of each DTI parameter. We used a multiple linear regression model to characterize FA variations in terms of the variations of directional diffusivities (i.e., RD and PD) to explore the potential of DTI for detecting a well-known pathologic change in the median nerve, that is, demyelination, in. Control Subjects Without Symptoms (n = 26) (n = 24) Subjects With Carpal Tunnel Syndrome (n = 50) p a Age (y) 50.3 ± ± ± Sex (no. of patients) 1.0 Women Men Involved hand (no. of patients) 0.62 Right Left Height (cm) ± ± ± Weight (kg) 61 ± ± ± Body mass index 22.5 ± ± ± Duration of symptoms (mo) NA NA 23.4 ± 34 Hypoesthesia (no. of patients) NA NA 33 (66) Thenar atrophy (no. of NA NA 24 (48) patients) Positive Phalen test result (no. of patients) NA NA 47 (94) Note Data are mean ± SD unless otherwise indicated. Values in parentheses are percentages. NA = not applicable. a ANOVA. TABLE 2: Comparison of Diffusion-Tensor Imaging Parameters at Different Anatomic Levels Parameter Location Control (n = 50) Without Symptoms (n = 26) (n = 24) (n = 50) Control Without vs In the CNS, specific types of FA variation have been found to correlate with specific microstructural pathologic entities [17 19]. We used Pearson product-moment correlation coefficients to examine correlations between DTI and NCS parameters. Intraobserver and interobserver reliability was assessed with the intraclass correlation coeffi- p a vs Control Without Symptoms vs Control Fractional anisotropy 0.47 ± ± ± ± ± ± ± ± ± < < Radial diffusivity 0.90 ± ± ± ± ± ± < ± ± ± < Apparent diffusion coefficient 1.26 ± ± ± ± ± ± < ± ± ± < Parallel diffusivity 1.98 ± ± ± ± ± ± ± ± ± Note Data are mean ± SD unless otherwise indicated. = carpal tunnel syndrome. a Bonferroni post hoc test AJR:204, June 2015

4 Diffusion-Tensor Imaging of Carpal Tunnel Syndrome FA PD ( 10 3 mm 2 ) A D cient (ICC) [20]. ICCs were interpreted as follows: , slight agreement; , fair agreement; , moderate agreement; , substantial agreement; and 0.81 and greater, almost perfect agreement [21]. Results Demographic and Clinical Characteristics Of the 65 patients and 53 control subjects eligible for the study, 15 patients and three control subjects were excluded. The group included six men and 44 women (mean age, 53.6 years; range, years) (Table 1) who had a wide range of clinical and electrophysiologic severity. Of the 50 patients, 42 underwent open release, and eight underwent conservative treatment. Forty of the 42 surgically treated patients had complete resolution of or much improvement in symptoms after surgery as assessed with a visual analog scale for tingling RD ( 10 3 mm 2 ) B sensation and the Boston Carpal Tunnel Syndrome Questionnaire. The control group included five men and 45 women (mean age, 51 years; range, years); 26 were healthy volunteers, and 24 had -mimicking symptoms. The control subjects had negative results of provocative tests and NCS. Age, sex, and the laterality of the evaluated hand were not significantly different among the, control with symptoms, and healthy control groups (Table 1). ADC ( 10 3 mm 2 ) C Fig. 2 Box plots show diffusion-tensor imaging parameters in subjects with carpal tunnel syndrome () and control subjects without and with symptoms at carpal tunnel inlet, middle, and outlet. Single asterisk indicates p < 0.05; double asterisk, p < 0.001; circles, outliers. Upper and lower ends of box indicate first (Q1) and third (Q3) quartiles of data, and, thus, box represents interquartile range (IQR); line within box indicates median; and whiskers extend from Q1 or Q3 to data points nearest to Q1 (1.5 IQR) or Q3 + (1.5 IQR), respectively. A C, Fractional anisotropy (FA) is decreased (A), whereas radial diffusivity (RD) (B) and apparent diffusion coefficient (ADC) (C) are significantly increased in group compared with control groups without and with symptoms. Differences were largest and most consistent at carpal tunnel inlet. D, Parallel diffusivity (PD) was not significantly different between groups except for that between and group control group without symptoms at middle of carpal tunnel. Differences in Diffusion-Tensor Imaging Parameters Between Groups at Different Locations The mean FA value for the group was significantly lower than that for the control groups with and without symptoms at the middle and outlet of the carpal tunnel (p < 0.05) (Fig. 2A). In contrast, the mean RD increased significantly in the group compared with the control groups at all measured locations (p < 0.05) (Fig. 2B). The mean ADC values also increased significantly in the group compared with the control groups at the middle and the outlet of the carpal tunnel (p < 0.05) (Fig. 2C). Of the three measurement locations, the inlet consistently had the largest differences across the DTI parameters. The differences between groups in mean PD were largely insignificant at the three measured locations (Fig. 2D). No significant differences were found between the two control groups in FA, RD, ADC, or PD (p > 0.05) (Table 2). Diagnostic Performance of Diffusion-Tensor Imaging Parameters for Carpal Tunnel Syndrome FA measured at the carpal tunnel inlet had the highest accuracy in the diagnosis of (Fig. 3). ROC curve analysis revealed that the AUC for FA was largest at the carpal tunnel in- AJR:204, June

5 Kwon et al. let (0.82; 95% CI, ) (Fig. 3) and that the AUC for RD was largest at the same location (0.79; 95% CI, ). In contrast, the AUC for ADC was largest at the middle of the carpal tunnel (0.74; 95% CI, ). The optimal cutoffs at the inlet were 0.44 for FA, mm 2 /s for RD, and mm 2 /s for ADC. At an FA threshold of 0.44 or less, sensitivity was 72% (95% CI, 60 84%); specificity, 82% (95% CI, 71 93%); positive predictive value, 80% (95% CI, 69 91%); and negative predictive value, 75% (95% CI, 62 87%). RD and ADC had higher sensitivities but lower specificities than FA (Table 3). TABLE 3: Diagnostic Performance of Fractional Anisotropy, Radial Diffusivity, and Apparent Diffusion Coefficient Parameter Sensitivity (%) Specificity (%) Positive Predictive Value (%) Negative Predictive Value (%) Fractional anisotropy 72 (60 84) 82 (71 93) 80 (69 91) 75 (62 87) Radial diffusivity 92 (84 100) 56 (36 64) 88 (79 96) 68 (54 81) Apparent diffusion coefficient 84 (74 94) 60 (46 64) 79 (68 90) 68 (54 81) Note Values in parentheses are 95% CI. Test Reliability The ICCs for interobserver agreement were 0.88 (95% CI, ) for FA, 0.89 (95% CI, ) for RD, and 0.87 (95% CI, ) for ADC. The ICCs for intraobserver agreement were 0.95 (95% CI, ) for FA, 0.96 (95% CI, ) for RD, and 0.96 (95% CI, ) for ADC. Cause of Anisotropic Loss A multiple linear regression model using RD and PD as explanatory variables accounted for 96% of the variance of FA (adjusted R 2 = 0.963; p < 0.001). FA could be predicted primarily from RD (β = 1.134; 95% CI, to 0.518; p < 0.001) and to a lesser extent from PD (β = 0.419; 95% CI, ; p < 0.001). Thus, a decrease in FA in is largely attributable to an increase in RD. Correlations Between Diffusion-Tensor Imaging and Nerve Conduction Study Parameters The FA, RD, and ADC values measured at the carpal tunnel inlet correlated significantly with most of the NCS parameters (Table 4). Of the three DTI parameters, FA had the highest correlation with NCS parameters, and FA was the only parameter that cor- Fig year-old woman with carpal tunnel syndrome. Graphs show ROC curves for fractional anisotropy (FA), radial diffusivity (RD), apparent diffusion coefficient (ADC), and parallel diffusivity (PD) at inlet, middle, and outlet of carpal tunnel. Axial T2-weighted MR images show carpal tunnel inlet delineated by pisiform bone and proximal end of flexor retinaculum and carpal tunnel outlet by hook of hamate and distal end of flexor retinaculum. of carpal tunnel was defined as location of equal distance from both inlet and outlet of carpal tunnel. Arrows indicate median nerve. Highest diagnostic accuracy was achieved with FA measured at carpal tunnel inlet AJR:204, June 2015

6 Diffusion-Tensor Imaging of Carpal Tunnel Syndrome TABLE 4: Correlation Between Diffusion-Tensor Imaging Parameters Measured at the Carpal Tunnel and Age and Nerve Conduction Study Parameters Fractional Anisotropy Radial Diffusivity Apparent Diffusion Coefficient Parameter related significantly with electrophysiologic grades. FA had the highest correlation with sensory (r = 0.54, p < 0.001) and motor (r = 0.54, p < 0.001) amplitude. r a p r a p r a p Age Median nerve sensory latency 0.39 < Median nerve sensory amplitude 0.54 < < < Median nerve conduction velocity at 7-cm palm-wrist segment Median nerve motor latency Median nerve motor amplitude 0.54 < Nerve conduction study Padua grade a Pearson correlation coefficient. Discussion DTI is a promising technique for noninvasive assessment of peripheral nerve lesions [22]. DTI parameters have been found useful for differentiating patients with from healthy subjects [4 6, 23, 24]. However, the optimal parameters and location for diagnosing with DTI have not been studied, to our knowledge. In this study, we found that the use of FA at the carpal tunnel inlet is optimal for diagnosing. We found that FA measured at the carpal tunnel inlet had the highest diagnostic accuracy. There have been controversies regarding the utility of FA and ADC for differentiating patients from healthy subjects. FA was consistently found useful in differentiating patients from healthy subjects [4, 23, 24], but the usefulness of ADC has been denied in some studies [23, 24]. In our study, of the four parameters tested, FA, ADC, and RD were found useful for diagnosing. Analysis based on area under the ROC curve showed that FA has the highest diagnostic accuracy compared with RD and ADC. Thus, our findings indicate that FA is the optimal parameter for diagnosing. An interesting finding was that the accuracy of RD was higher than that of ADC for measurements at the carpal tunnel inlet. RD may be more useful than ADC for diagnosing because the diffusion changes occur largely in a perpendicular direction rather than in the longitudinal direction of the median nerve in [6]. We found that the carpal tunnel inlet was the most suitable location for measuring DTI parameters in. Standardization of measurement location is important for diagnosing with DTI because of the location dependence of DTI parameters [5, 8]. To determine the optimal location, we analyzed three locations in the carpal tunnel and found that the carpal tunnel inlet had the greatest differences in DTI parameters between the control and groups. The highest diagnostic accuracy was achieved when parameters measured at the carpal tunnel inlet were used. Our findings are corroborated by the fact that the carpal tunnel inlet is the most frequent site of severe compression of the median nerve in and thus has the most remarkable pathologic and morphologic changes [25 28]. At the carpal tunnel outlet, in contrast, the nerve becomes flat and branched so that imaging assessment of the nerve becomes more difficult [25, 27, 29]. However, discrepancies exist across studies regarding the optimal measurement location. In contrast to our study, another study [4] showed that FA obtained from the carpal tunnel outlet had higher diagnostic accuracy than FA at the inlet or distal radioulnar joint. Another study [7], on the other hand, refuted location-dependent differences in FA and ADC values. Determining the optimal measurement location is an important issue to solve before DTI is used in clinical practice. Further studies performed in a standardized manner are needed to form a conclusion on this issue. In our study, FA, RD, and ADC correlated significantly with most NCS parameters. The highest correlation coefficient was between FA and sensory and motor amplitudes, and only FA had a significant correlation with both the NCS parameters and electrophysiologic grades. These findings are consistent with those of a study by Wang et al. [7] and support the choice of FA as the optimal parameter for diagnosing. However, only moderate to weak correlations were found between the two test modalities, suggesting that the capability of DTI for providing useful electrophysiologic information may be limited. DTI may capture different dimensions of nerve changes apart from those detected with NCS, or the techniques of diffusion tensor measurement may be too limited for measurement of electrophysiologic changes with as much accuracy as NCS. We anticipate that advances in DTI techniques will provide further evidence about whether and to what extent DTI can depict electrophysiologic changes in peripheral nerves. One interesting finding of this study was that anisotropic loss in was largely attributable to increased RD rather than to decreased PD. The importance of this finding seems difficult to interpret at present, but some hints can be derived from the studies on the CNS. In the CNS, anisotropic loss resulting from increased RD corresponds to demyelination, but anisotropic loss resulting from decreased PD is consistent with axonal damage [17 19]. Thus, the type of parameter changes observed in this study may suggest demyelination, which exactly matches the typical pathologic findings of the median nerve in [30]. An alternative explanation for the increased RD in may be subperineural edema. Nerve compression could induce edema by reducing microvascular blood flow and subsequent breakdown of the blood-nerve barrier [31]. Increased isotropic diffusion in the fluid accumulated in the perineural space may be captured by DTI as increased RD and anisotropic loss [6]. A future histologic correlation study may elucidate the importance of anisotropic loss in and verify the usefulness of DTI for detecting microstructural changes in the peripheral nerves. The strength of this study was in its design. Most of the previous studies examining the di- AJR:204, June

7 Kwon et al. agnostic performance of DTI for included a limited number of patients, usually fewer than 20 [4, 5, 23], which seems insufficient to cover the full range of disease severity. Furthermore, control subjects were not matched for age in those studies, although age is a well-known confounder for DTI values [5, 9, 32]. Age has the same effect as on FA and ADC values: with advancing age, FA decreases and ADC increases [5, 9, 28]. Thus, the use of younger control subjects likely yields overestimated diagnostic accuracy for DTI [6]. In addition, failure to include symptomatic non- subjects in the control group may introduce a spectrum bias, which also generates overestimated accuracy [33]. To avoid these biases, we included a total of 100 participants (50 consecutively registered patients, 50 control subjects) in a prospective manner and matched the controls to patients for age and sex, including both healthy control subjects and those with symptoms. Despite the use of these rigorous design precautions against potential biases, we found that DTI parameters, particularly FA measured at the carpal tunnel inlet, were still accurate for diagnosis. The study had several limitations. First, spectrum bias may be present because we used a case-control design. Second, DTI has technical challenges that should be overcome. For example, DTI is sensitive to artifacts, such as motion, eddy currents, and susceptibility. We took precautions against such artifacts by immobilizing the wrist, limiting the acquisition time to 15 minutes, and postprocessing the DTI data. In summary, we found that the use of FA measured at the carpal tunnel inlet had the highest diagnostic accuracy in the diagnosis of. We recommend use of FA measured at the carpal tunnel inlet for diagnosing with DTI. Future studies to standardize the other details of performing DTI would make this technique a valuable tool beyond the conventional imaging methods and NCS in the diagnosis of. References 1. Mondelli M, Giannini F, Giacchi M. Carpal tunnel syndrome incidence in a general population. Neurology 2002; 58: Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis a technical review. NMR Biomed 2002; 15: Beaulieu C. The basis of anisotropic water diffu- tractography of the median nerve. Eur Radiol 2013; 23: Guggenberger R, Markovic D, Eppenberger P, et al. Assessment of median nerve with MR neurography by using diffusion-tensor imaging: normative and pathologic diffusion values. Radiology 2012; 265: Stein D, Neufeld A, Pasternak O, et al. Diffusion tensor imaging of the median nerve in healthy and carpal tunnel syndrome subjects. J Magn Reson Imaging 2009; 29: Wang CK, Jou IM, Huang HW, et al. Carpal tunnel syndrome assessed with diffusion tensor imaging: comparison with electrophysiological studies of patients and healthy volunteers. Eur J Radiol 2012; 81: Kabakci N, Gurses B, Firat Z, et al. Diffusion tensor imaging and tractography of median nerve: normative diffusion values. AJR 2007; 189: Tanitame K, Iwakado Y, Akiyama Y, et al. Effect of age on the fractional anisotropy (FA) value of peripheral nerves and clinical significance of the age-corrected FA value for evaluating polyneuropathies. Neuroradiology 2012; 54: Padua L, LoMonaco M, Gregori B, Valente EM, Padua R, Tonali P. Neurophysiological classification and sensitivity in 500 carpal tunnel syndrome hands. Acta Neurol Scand 1997; 96: Mangin JF, Poupon C, Clark C, Le Bihan D, Bloch I. Distortion correction and robust tensor estimation for MR diffusion imaging. Med Image Anal 2002; 6: Nielsen JF, Ghugre NR, Panigrahy A. Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns. Magn Reson Imaging 2004; 22: 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 2013; 201:[web] W326 W Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR 2008; 29: American Association of Electrodiagnostic Medicine, American Academy of Neurology, and American Academy of Physical Medicine and Rehabilitation. Practice parameter for electrodiagnostic studies in carpal tunnel syndrome: summary statement. Muscle Nerve 2002; 25: Youden WJ. Index for rating diagnostic tests. Cancer 1950; 3: Budde MD, Xie M, Cross AH, Song SK. Axial 18. Herrera JJ, Chacko T, Narayana PA. Histological correlation of diffusion tensor imaging metrics in experimental spinal cord injury. J Neurosci Res 2008; 86: Song SK, Yoshino J, Le TQ, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 2005; 26: Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 1979; 86: Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: Chhabra A, Thakkar RS, Andreisek G, et al. Anatomic MR imaging and functional diffusion tensor imaging of peripheral nerve tumors and tumorlike conditions. AJNR 2013; 34: Khalil C, Hancart C, Le Thuc V, Chantelot C, Chechin D, Cotten A. Diffusion tensor imaging and tractography of the median nerve in carpal tunnel syndrome: preliminary results. Eur Radiol 2008; 18: Tasdelen N, Gurses B, Kilickesmez O, et al. Diffusion tensor imaging in carpal tunnel syndrome. Diagn Interv Radiol 2012; 18: Kwon BC, Jung KI, Baek GH. Comparison of sonography and electrodiagnostic testing in the diagnosis of carpal tunnel syndrome. J Hand Surg Am 2008; 33: Mackinnon SE, Dellon AL, Hudson AR, Hunter DA. Chronic human nerve compression: a histological assessment. Neuropathol Appl Neurobiol 1986; 12: Moran L, Perez M, Esteban A, Bellon J, Arranz B, del Cerro M. Sonographic measurement of crosssectional area of the median nerve in the diagnosis of carpal tunnel syndrome: correlation with nerve conduction studies. J Clin Ultrasound 2009; 37: Phalen GS. The carpal-tunnel syndrome: seventeen years experience in diagnosis and treatment of six hundred fifty-four hands. J Bone Joint Surg Am 1966; 48: Yao L, Gai N. Median nerve cross-sectional area and MRI diffusion characteristics: normative values at the carpal tunnel. Skeletal Radiol 2009; 38: Mackinnon SE. Pathophysiology of nerve compression. Hand Clin 2002; 18: Mackinnon SE, Dellon AL, Hudson AR, Hunter DA. Chronic nerve compression: an experimental model in the rat. Ann Plast Surg 1984; 13: Hiltunen J, Kirveskari E, Numminen J, Lindfors N, Goransson H, Hari R. Pre- and post-operative diffusion tensor imaging of the median nerve in carpal sion in the nervous system: a technical review. diffusivity is the primary correlate of axonal in- tunnel syndrome. Eur Radiol 2012; 22: NMR Biomed 2002; 15: jury in the experimental autoimmune encephalo- 33. Boyer K, Wies J, Turkelson CM. Effects of bias on 4. Barcelo C, Faruch M, Lapegue F, Bayol MA, Sans myelitis spinal cord: a quantitative pixelwise the results of diagnostic studies of carpal tunnel N. 3-T MRI with diffusion tensor imaging and analysis. J Neurosci 2009; 29: syndrome. J Hand Surg Am 2009; 34: AJR:204, June 2015