Thoreson, Andrew R.

Abstract Objective. Four-dimensional CT (4DCT) enables visualization of dynamic wrist joint pathology but may be compromised by motion artifacts. Deep learning (DL)-assisted motion correction has the potential to enhance wrist 4DCT quality, but model development is hindered by scarce training data. To address this, we developed a model-based motion simulation framework that generated realistic 4DCT data with paired static ground truth images to demonstrate wrist motion correction using a physics-informed DL model trained with simulated data. Approach. Wrist motion was simulated by synchronizing rotational and translational movements of carpal bones and connective tissues. Elastic deformation guided by analytically-derived motion vector fields was applied to static wrist scans at neutral positions to create artifact-free images at multiple angular positions. Virtual motion-corrupted 4DCT images were produced via forward projection and reconstruction with synchronized gantry and wrist motion. Experimental data from 4DCT cadaver scans were collected to validate simulation quality. Low-intensity region score (LIRS), positivity, and entropy were used to assess simulated motion artifacts. A physics-informed motion correction network was adapted exclusively using simulated training data and subsequently deployed on simulated, real cadaveric, and in vivo 4DCT scans. LIRS and structural similarity index (SSIM) were used to evaluate motion correction quality. Wilcoxon signed-rank tests were conducted ( α = 0.05). Results. The simulation method created realistic motion artifacts compared to real scan reference ( p > 0.05). Motion artifacts were effectively mitigated across testing cases demonstrating generalizability. In simulated scans, LIRS improved from 0.55 ± 0.12–0.75 ± 0.04 and SSIM from 0.87 ± 0.08–0.95 ± 0.03 at bone regions (each p < 0.01). In experimental cadaveric scans, LIRS improved from 0.05 ± 0.01–0.31 ± 0.17 in marker regions and from 0.58 ± 0.19–0.77 ± 0.14 in bone regions (each p < 0.01). Significance. Our simulation method facilitates training DL-assisted motion correction algorithms by providing high-quality training data, effectively supporting 4DCT image quality enhancement.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Objective To evaluate the feasibility and potential efficacy of percutaneous spinal stimulation (epidural stimulation, ES) combined with task-specific training to reduce spasticity and improve gait and balance in individuals with progressive multiple sclerosis (MS). Methods Two men with progressive MS (EDSS 6.5) underwent ES lead implantation targeting the lower spinal cord, followed by one month of rehabilitation involving 12 ES-assisted training sessions. Assessments included instrumented gait analysis, Modified Ashworth Scale (MAS), pendulum test, and static balance testing performed at baseline, and at end of the study with ES-Off and ES-On conditions. Results Participant 1, with spastic hemiparetic gait, demonstrated improved lower extremity joint kinematics and muscle activation during gait, reduced knee extensor spasticity, and enhanced functional movement patterns with ES-On. Participant 2, with significant paraparesis and minimal spasticity, showed limited gait changes but experienced marked improvements in static balance, particularly under eyes-closed conditions. No adverse events were reported. Conclusion This pilot study demonstrates the feasibility and tolerability of ES paired with task-specific training in progressive MS. Participant-specific responses were observed, including improvements in gait kinematics, neuromuscular activation, spasticity, or balance; however, spatiotemporal gait parameters did not improve. These preliminary findings highlight response heterogeneity and the need for individualized ES parameter tuning. Trial registry name and URL: https://clinicaltrials.gov/study/NCT06019611

Epidural and dorsal root stimulation have emerged as promising therapies to restore function after spinal cord injury. However, how each modality uniquely or jointly engages spinal circuits remains unclear. To address this, we analyzed leg electromyography (EMG) responses to epidural stimulation (ES) and dorsal root ganglia stimulation (DRGS) delivered to the lumbosacral cord in the same individuals.

Abstract Background Computational modeling is a tool being deployed for orthopaedic solutions but its use in the hand and wrist remains limited. This work used a model to simulate a clinically relevant provocative scaphoid shift maneuver (SSM) with different levels of scapholunate interosseous ligament (SLIL) injuries to observe the effect on different metrics. Methods A personalized model simulated the full SSM motion cycle from ulnar deviation with extension to radial deviation with flexion informed by the participant’s motion obtained from dynamic computed tomography. Models repeated the SSM under different levels of SLIL injury and reported changes in joint kinematics, contact mechanics, and ligament forces. Results The fully injured model increased scaphoid dorsal translation, flexion, and radial deviation compared to the intact condition and caused a subluxation of the scaphoid. Radioscaphoid contact areas were approximately 200% greater in the fully injured model compared with all others and the fully injured model was the only condition where contact force decreased across the motion cycle. Ligament forces in the intact condition were on average 33.0 N and 54.2 N for the volar and dorsal SLIL, respectively. Lastly, the long radiolunate, an extrinsic stabilizer, had forces that increased following SLIL injury. Conclusions Computational models can successfully recreate clinically observed behaviors of an SSM, including scaphoid subluxation, while providing new insights via quantification of contact mechanics and ligament forces. Contact mechanics metrics may be important for understanding the long-term progression of untreated SLIL injuries to osteoarthritis. Additionally, ligament force metrics may explain the progression of SLIL injuries from volar SLIL to dorsal SLIL and highlight the importance of repairing extrinsic stabilizers of the joint, due to increased force sharing following SLIL injury. This work provides a pathway to future studies investigating the effects of SLIL injury and repair, both acutely and chronically.

Background: Spasticity is common in spinal cord injury (SCI) and multiple sclerosis (MS), and it can manifest as repeated, rhythmic muscle contractions called clonus. The spontaneous nature of clonus can disrupt independent performance of activities of daily living and negatively impact overall health and quality of life. Objectives: To quantify biomarkers of clonus and explore management of clonus in individuals with SCI or progressive MS using epidural spinal cord stimulation (ES) or dorsal root stimulation (DRS). Methods: Four male participants were included in this case series study: 3 with SCI and 1 with MS. All participants underwent temporary percutaneous ES lead placement over the dorsolateral thoracolumbar region of the spinal cord. Participants with SCI were also implanted with DRS leads at the L4 dorsal root. Clonus was elicited mechanically at the ankle while recording electromyography of the soleus muscle synchronized to direct spinal cord field potential recordings from ES and DRS percutaneous leads. Results: Clonus was evident as a prominent band (5-8 Hz) in recordings from ES leads, DRS leads, soleus muscle, and accelerometry. In 4 participants, percutaneous spinal stimulation reduced median clonus duration and cycle count. Clonus was immediately suppressed upon activation of spinal cord stimulation, and the suppression persisted even when clonus was reinitiated after turning off the stimulation. Conclusion: The use of objective biomarkers, including spinal cord potentials, to quantify clonus in real time combined with the immediate and reversible effects of stimulation highlight the potential of neuromodulation as a therapeutic tool for managing clonus. These data demonstrate preliminary efficacy of ES and DRS for clonus monitoring and treatment in 4 participants.

Background: The radiolunate joint is conventionally thought to be spared from radiocarpal osteoarthritis, but proximal lunate degenerative changes have been noted. An investigation of radiolunate arthrokinematics in ostensibly healthy wrists may improve understanding of posttraumatic and postoperative outcomes. We hypothesize that four-dimensional computed tomography (4DCT)-derived radiolunate arthrokinematics during motion will differ between wrists with type 1 and type 2 lunates. The clinical-translational relevance of this analysis is highlighted using a 4DCT case study of scaphoid excision with 4-corner arthrodesis (SEFCA). Methods: Four-dimensional computed tomography data were collected during flexion-extension and radioulnar deviation in 19 healthy wrists (mean [SD] age: 43.6 [11.7] years, 21.1% female) and 1 wrist 3 years after SEFCA (male, 52 years). Carpal osteokinematics and joint arthrokinematics were calculated to derive median radiolunate interosseous proximities at each 4DCT-captured timepoint. Results: Median radiolunate interosseous proximities during motion are well approximated by second-order regressions in uninjured wrists. In the arthrodesed wrist, closest proximities occurred at the extremes of motion except maximum radial deviation. Conclusions: This study quantifies a normative range of median radiolunate interosseous proximities during wrist motion. The normative 4DCT datasets provide a reference range for evaluating postoperative arthrokinematics, suggesting the clinical-translational potential of dynamic imaging. The case study highlights applications for patient-level postoperative evaluation, which can be expanded to other pathologies, surgeries, or conditions in the future.

Clonus is a disabling symptom of upper motor neuron dysfunction in patients with multiple sclerosis (MS). We report immediate termination of mechanically evoked clonus by epidural lumbosacral spinal cord stimulation (ES) in a 58-year-old man with secondary progressive MS. This observation was captured during training using video and skin surface electromyography of soleus muscle activity. ES immediately reduced mechanically evoked clonus duration and cycle count compared to trials without stimulation, as determined by a Kruskal–Wallis test ( p < 0.05, n = 2–3 trials), and reverted to baseline status when stimulation was off.

Abstract The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three‐dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi‐domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM‐generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three‐dimensional triangular surface meshes. A multi‐domain, particle‐based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface‐normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities—a proxy for joint space—were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging‐derived STT joint space.