Purpose

Current coronoid fracture classification systems are based on fragment size and configuration using plain radiographs and/or CT. During surgery, coronoid fracture fragments appear much larger than anticipated because cartilage is radiolucent and therefore not taken into account with preoperative imaging. The purpose of this study was to quantify the articular cartilage thickness of the coronoid process, with reference to coronoid fracture classifications.

Purpose: Techniques to quantify soft-tissue forces in the upper extremity are not well described. Consequently, ligament forces of the elbow joint have not been reported. Knowledge of the magnitudes of tension of the primary valgus stabilizer, the anterior bundle of the medial collateral ligament (AMCL), would allow for an improved understanding of the load bourne by the ligament. The purpose of this in vitro study was to quantify the magnitude of tension in the native AMCL throughout flexion with the arm in the valgus orientation. We hypothesized that tension in the AMCL would increase with flexion.

Method: Five fresh-frozen cadaveric upper extremities (mean age 72 ± 10 years) were tested. To produce active muscle loading in a motion simulator, cables were affixed to the distal tendons of the brachialis, biceps brachii, triceps brachii, and brachioradialis and attached to actuators. The wrist was fixed in neutral flexion/extension and the forearm in neutral rotation. The arm was orientated in the valgus gravity-loaded position. A custom designed ligament load transducer was inserted into the AMCL. Active simulated flexion was achieved via computer-controlled actuation while passive elbow flexion was achieved by an investigator manually guiding the arm through flexion. Motion of the ulna relative to the humerus was measured using a tracking device.

Results: Both the active and passive motion pathways showed an increase in AMCL tension with increasing angles of elbow flexion (p < 0.05). There was no difference in AMCL tension levels between active and passive elbow flexion (p = 0.20). The mean maximum tension achieved was 97±33N and 94±40 N for active and passive testing respectively.

Conclusion: AMCL tension levels were observed to increase with elbow flexion, indicating that other structures (such as the joint capsule and the shape of the articulation) are likely more responsible for joint stability near full extension, and that the AMCL is recruited at increased angles of elbow flexion. With respect to load magnitudes, Regan et al. found the maximum load to failure of the AMCL was 261 N, while Armstrong et al. reported a failure load of 143 N in cyclic testing. The maximum AMCL tension level observed in this study was 160 N. Failure of the AMCL was not observed, which may be due to differences in specimen size, age, or the method of load application. In summary, this in vitro cadaveric study has provided a new understanding of the magnitudes of AMCL tension through the arc of elbow flexion, and this has important implications with respect to the desired target strength of repair and reconstruction techniques. These findings will also assist in the development and validation of computational models of the elbow.

Purpose: The identification of anatomical landmarks is an important aspect of joint surgery, to ensure proper placement and alignment for implants and other reconstructive procedures. At the elbow, the center of the capitellum (derived via a digitization of the surface and subsequent sphere fitting) has been well established as a key landmark to identify the axis of rotation of the joint. For some cases, and in particular minimally invasive surgery, only small regions of the capitellum may be exposed which may lead to errors in determining the centre. The purpose of this study was to identify the optimal location of digitizations of the capitellum.

Method: Twenty-five fresh frozen cadaveric distal humeri (19 left, 6 right) were studied. Using an x-ray computed tomography scanner, volumetric images of each specimen were acquired and used to reconstruct a 3-dimensional digital model of the specimen using the Visualization Toolkit (VTK). A sphere-fit algorithm was used to determine the centre of the spherical capitellum based on manually chosen (digitized) points across the 3D capitellar surface. The true geometric centre was located by digitizing points across the entire capitellar surface. Three sub-regions of the capitellum, commensurate with typical surgical approaches with minimal dissection, were then digitized. These were superior anterior lateral (SAL), inferior anterior lateral (IAL) and a combination of these two regions. These regions were compared to the true center using a 1-way Repeated Measures ANOVA with significance set to p = 0.05.

Results: Digitizations of only SAL and IAL sub-regions resulted in the largest differences relative to the true centre: SAL = 3.9±3.4 mm, IAL = 4.2±3.4 mm, (p < 0.0005). There was no difference between SAL and IAL (p = 1.0). Digitization of the combined SAL + IAL regions, while significantly different from the entire capitellum, resulted in the smallest mean difference of 0.87±0.84 mm.

Conclusion: These data show that the region of digitization affects the accuracy of predicting the capitellum centre. In a previous study by our group, we showed that an accurate determination of the centre of a sphere can be achieved with a small surface area of digitization. In the current study, the large errors that occurred when a small surface was digitized (i.e. SAL and IAL alone), are in all likelihood, due the non-spherical nature of the capitellum. In summary, while the most precise method in locating the true centre is to digitize the entire capitellar surface where possible, an alternative approach is to digitize both the superior and inferior anterior lateral regions.

Purpose: Current techniques for the investigation of elbow stability following injury or surgical interventions rely on kinematic descriptors. Typically, the motion pathways of the bones are employed to describe the effect of various clinical variables on alignment joint stability. This study describes a new approach to better visualize joint motion pathways that relates the anatomical geometry of the joint, obtained using medical imaging, with the recorded motion of the joint. The clinical aim of our study was to use this approach to investigate the effect of radial head resection and subsequent radial head arthroplasty on joint kinematics and elbow stability.

Method: Five fresh-frozen cadaveric specimens were employed. Computed tomography (CT) scans of each upper extremity were obtained to create a three-dimensional model of the joint. Simulated active elbow flexion with the arm in the valgus gravity loaded position was achieved using an upper arm simulator previously developed in our laboratory. Receivers from an electromagnetic tracking device were attached to the humerus and ulna in order to record their relative motion. Sutures were secured to the tendons of relevant muscles, which were connected to servomotors and pneumatic actuators, used to simulate motion. Kinematic data was collected with the radial head intact, radial head resected and following placement of metallic radial head implant. A repeated-measures analysis of variance was used to detect statistical differences. After testing, each specimen was denuded of all soft tissue and disarticulated. Fiducial markers were attached to the humerus and the ulna. The joint was then re-imaged in the CT scanner to obtain a volumetric image of each fiducial. Using the kinematic data recorded during simulated motion, and the knowledge of the position of each fiducial, a direct visualization of the recorded motion, using the 3D models was obtained. The bony position was then compared to the traditional graphical kinematic analysis examining changes in valgus angulations throughout the arc of motion.

Results: We observed a close agreement between the kinematic output and the registered bony 3D models showing the joint position. Following resection of the radial head, in the valgus dependent position, there was an increase in the valgus angulation of the ulna with respect to the humerus (p< 0.05).

Conclusion: Using this visualization approach, these changes in bony alignment were readily observed and understood visually in the 3D model of the ulna. Unlike the traditional graphical approach used to investigate elbow stability, this technique allows for the representation of coupled motion (rotation) of the bones. This technique also permits direct visualization the relative position of the bones within the joint, hence improving the overall understanding of joint motion.