Assessment of Median Nerve Mobility by Ultrasound Dynamic Imaging for Diagnosing Carpal Tunnel Syndrome

Abstract

Carpal tunnel syndrome (CTS) is the most common peripheral neuropathy and is characterized by median nerve entrapment at the wrist and the resulting median nerve dysfunction. CTS is diagnosed clinically as the gold standard and confirmed with nerve conduction studies (NCS). Complementing NCS, ultrasound imaging could provide additional anatomical information on pathological and motion changes of the median nerve. The purpose of this study was to estimate the transverse sliding patterns of the median nerve during finger movements by analyzing ultrasound dynamic images to distinguish between normal subjects and CTS patients. Transverse ultrasound images were acquired, and a speckle-tracking algorithm was used to determine the lateral displacements of the median nerve in radial-ulnar plane in B-mode images utilizing the multilevel block-sum pyramid algorithm and averaging. All of the averaged lateral displacements at separate acquisition times within a single flexion-extension cycle were accumulated to obtain the cumulative lateral displacements, which were curve-fitted with a second-order polynomial function. The fitted curve was regarded as the transverse sliding pattern of the median nerve. The R2 value, curvature, and amplitude of the fitted curves were computed to evaluate the goodness, variation and maximum value of the fit, respectively. Box plots, the receiver operating characteristic (ROC) curve, and a fuzzy c-means clustering algorithm were utilized for statistical analysis. The transverse sliding of the median nerve during finger movements was greater and had a steeper fitted curve in the normal subjects than in the patients with mild or severe CTS. The temporal changes in transverse sliding of the median nerve within the carpal tunnel were found to be correlated with the presence of CTS and its severity. The representative transverse sliding patterns of the median nerve during finger movements were demonstrated to be useful for quantitatively estimating median nerve dysfunction in CTS patients.

Fig 1. Ultrasound scanning.

While the fingers performed active flexion and extension movements (a), the transverse sliding motion of the median nerve within the carpal tunnel was clearly identified in dynamic B-mode images. (b), (c) & (d) Representative US images obtained from normal subjects and mild- and severe- CTS patients, respectively. Note that the amount of transverse sliding of the median nerve varied with the severity of CTS. N indicates the median nerve. Normal, Mild, and Severe indicate normal subjects, mild-CTS and severe-CTS patients.

Fig 2. Estimation of the pattern of median nerve motion.

During finger flexion and extension movements in normal subjects and mild-CTS and severe-CTS subjects, the median nerve showed non uniform transverse sliding motion over the ulnar-radial plane. (a) Red arrows indicate the direction of lateral displacements of representative pixels within the median nerve as calculated using a speckle-tracking algorithm. (b) Cumulative average lateral displacements (mean and SD values) of the median nerve at different acquisition times during one finger flexion–extension cycle, indicating marked variations of the motion patterns between normal subjects and mild-CTS and severe-CTS patients. Normal, Mild, and Severe indicate normal subjects, mild-CTS and severe-CTS patients.

Fig 3. Transverse sliding patterns of the median nerve.

Representative fitted curves indicating the various transverse sliding patterns of the median nerve during fingers flexion and extension movements among a normal subject, a mild-CTS patient, and a severe-CTS patient. The dots, triangles, and diamonds represent the cumulative lateral displacements at different acquisition times and the intersecting lines indicate the fitted curves for the different subgroups, respectively. Normal, Mild, and Severe indicate normal subjects, mild-CTS and severe-CTS patients.

Fig 4. Box plots analysis for the R², amplitude, and curvature estimates.

The calculated distributions of R², amplitude, and curvature estimates of the fitted curves for the normal subjects, mild-CTS and severe-CTS patients were presented. The bisecting line, box boundaries, and whiskers indicate the median value, 25^th to 75^th percentiles, and the estimated data range, respectively. Two and three asterisks indicate p < 0.01 and p < 0.001, respectively. Normal, Mild, and Severe indicate normal subjects, mild-CTS and severe-CTS patients.

Fig 5. Receiver operating characteristic (ROC) curves analysis.

The diagnostic performances of the R², curvature, and amplitude estimates alone and of the overall composite estimates in distinguishing normal subjects from CTS patients were demonstrated.

Fig 6. Three-dimensional FCM clustering analysis.

Distinguishing normal subjects from CTS patients by combining three parameters was shown. Each symbol represents an individual subject. Blue and red lines indicate estimated normal and CTS clusters, respectively. Normal, Mild, and Severe indicate normal subjects, mild-CTS and severe-CTS patients.