Electrogoniometer measurement and directional analysis of wrist angles and movements during the Sollerman hand function test.

STUDY DESIGN: Clinical measurement. INTRODUCTION: To investigate the characteristics of wrist motion (area, axis, and location) during activities of daily living (ADL) using electrogoniometry. METHODS: A sample of 83 normal volunteers performed the Sollerman hand function test (SHFT) with a flexible biaxial electrogoniometer applied to their wrists. This technique is accurate and reliable and has been used before for assessment of wrist circumduction in normal volunteers. A software package was used to overlay an ellipse of best fit around the 2-dimensional trace of the electrogoniometer mathematically computing the area, location, and axis angle of the ellipse. RESULTS: Most ADL could be completed within 20% of the total area of circumduction (3686°° ± 1575°°) of a normal wrist. An oblique plane in radial extension and ulnar flexion (dart-throwing motion plane) was used for rotation (-14° ± 32°) and power grip tasks (-29° ± 25°) during ADL; however, precision tasks (4° ± 28°), like writing, were performed more often in the flexion extension plane. In the dominant hand, only 2 power tasks were located in flexion region (cutting play dough [ulnar] and pouring carton [radial]), precision tasks were located centrally, and rotation and other power tasks were located in extension region. DISCUSSION: This study has identified that wrist motion during the ADL requires varying degrees of movement in oblique planes. Using electrogoniometry, we could visualize the area, location, and plane of motion during ADL. This could assist future researchers to compare procedures leading to loss of motion in specific quadrants of wrist motion and its impact on patient's ability in performing particular ADL. It could guide hand therapists to specifically focus on retraining the ADL that may be affected when wrist range of motion is lost after injury. LEVEL OF EVIDENCE: Diagnostic level III.

Empirical Curvelet-ridgelet Wavelet Transform for Multimodal Fusion of Brain Images.

BACKGROUND: Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms. OBJECTIVE: The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images. METHODS: An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images. RESULTS: The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology. CONCLUSION: The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.