Purpose: Accurate measurement of dynamic joint motion remains a clinical challenge. To address this problem, we have developed a low-dose clinical procedure using the Roentgen Single-plane Photogrammetric Analysis (RSPA) technique. A validation study was performed in a clinical setting, using a conventional digital flat-panel radiography system.

Method: To validate the technique, three experiments were performed: assessment of static accuracy, dynamic repeatability and measurement of effective dose. A knee joint phantom, imbedded with tantalum markers, was utilized for the experiments. Relative spatial positions of the markers were reconstructed using Radiostereometric Analysis (RSA). A digital flat-panel radiography system was used for image acquisition, and the three-dimensional pose of each segment was determined from single-plane projections by applying the RSPA technique. All images were processed using software developed in-house. To assess static accuracy, the phantom was mounted onto a three-axis translational stage and moved through a series of displacements ranging from 0 to 500 μm. Images of the phantom were acquired at each position. Accuracy was calculated by analyzing differences between reconstructed and applied displacements. To assess dynamic repeatability, the phantom was mounted on a six-axis robot, programmed to apply a flexion-extension movement to the joint. Multiple cine acquisitions of the moving phantom were acquired (30 fps, 4 ms exposure). Repeatability was calculated by analyzing the variation between motions reconstructed from repeated acquisitions. The effective dose of the procedure was measured using an ion-chamber dosimeter. The ion chamber was positioned between the phantom and x-ray source, facing the source. Entrance exposure was measured for multiple acquisitions, from which the effective dose was calculated.

Results: The accuracy determined from the static assessment was 25 μm and 450μm at the 95% confidence intervals for translations parallel and orthogonal to the image plane, respectively. Repeatability of the motion reconstructed from dynamic acquisitions was better than ± 200 μm for translations and ± 0.1 for rotations. The average effective dose for a 6 second dynamic acquisition was approximately 2μSv.

Conclusion: The proposed clinical procedure demonstrates both a high degree of accuracy and repeatability, and delivers a low effective dose.

Purpose: This study evaluated the accuracy of humeral component alignment in total elbow arthroplasty. An image-based navigated approach was compared against a conventional non-navigated technique. We hypothesized that an image-based navigation system would improve humeral component positioning, with navigational errors less than or approaching 2.0mm and 2.0°.

Method: Eleven cadaveric distal humeri were imaged using a CT scanner, from which 3D surface models were reconstructed. Non-navigated humeral component implantation was based on a visual estimation of the flexion-extension (FE) axis on the medial and lateral aspects of the distal humerus, followed by standard instrumentation and positioning of a commercial prosthesis by an experienced surgeon. Positioning was based on the estimated FE axis and surgeon judgment. The stem length was reduced by 75% to evaluate the navigation system independent of implant design constraints. For navigated alignment, the implant was aligned with the FE axis of the CT surface model, which was registered to landmarks of the physical humerus using the iterative closest point algorithm. Navigated implant positioning was based on aligning a 3D computer model calibrated to the implant with a 3D model registered to the distal humerus. Each alignment technique was repeated for a bone loss scenario where distal landmarks were not available for FE axis identification.

Results: Implant alignment error was significantly lower using navigation (P< 0.001). Navigated implant alignment error was 1.2±0.3 mm in translation and 1.3±0.3° in rotation for the intact scenario, and 1.1±0.5 mm and 2.0±1.3° for the bone loss scenario. Non-navigated alignment error was 3.1±1.3 mm and 5.0±3.8° for the intact scenario, and 3.0±1.6 mm and 12.2±3.3° for the bone loss scenario. Without navigation, 5 implants were aligned outside 5° for intact bone while 9 were aligned outside 10° for the bone loss scenario.

Conclusion: Image-based navigation improved the accuracy of humeral component placement to less than 2.0 mm and 2.0°. Further, outliers in implant positioning were reduced using image-based navigation, particularly in the presence of bone loss. Implant malalignment may well increase the likelihood of early implant wear, instability and loosening. It is likely that improved implant positioning will lead to fewer implant related complications and greater prosthesis longevity.

Purpose: While computer-assisted techniques can improve the alignment of the implant articulation with the native structure, stem abutment in the intramedullary canal may impede achievement of this alignment. In the current study, the effect of a fixed valgus (6 degree) stemmed humeral component on the alignment of navigated total elbow arthroplasty was investigated. Our hypothesis was that implantation of a humeral component with a reduced stem length would be more accurate than implantation of the humeral component with a standard length stem.

Method: Thirteen cadaveric distal humeri were imaged using a CT scanner, and a 3D surface model was reconstructed from each scan. Implantation was performed using two implant configurations. The first set was unmodified (Regular) while the second set was modified by reducing the length of the humeral stem to 25% of the original stem (Reduced). A surface model of the humeral component was aligned with the flexion-extension (FE) axis of the CT-based surface model, which was registered to the landmarks of the physical humerus using the iterative closest point algorithm. Navigated implant positioning was based on aligning a 3D computer model calibrated to the implant with a 3D model registered to the distal humerus.

Results: Implant alignment error was significantly lower for the Reduced implant, averaging 1.3±0.5 mm in translation and 1.2±0.4° in rotation, compared with 1.9±1.1 mm and 3.6±2.1° for the Regular implant. Abutment of the implant stem with the medullary canal of the humerus prevented optimal alignment of the Regular humeral component as only four of the 13 implantations were aligned to within 2.0° using navigation.

Conclusion: These results demonstrate that a humeral component with a fixed valgus angulation cannot be accurately positioned in a consistent fashion within the medullary canal of the distal humerus without sacrificing alignment of the FE axis due to stem abutment. Improved accuracy of implant placement can be achieved by introducing a family of humeral components, with three valgus angulations of 0°, 4° and 8°. Based on humeral morphology for these specimens, 12 of the 13 implants may be positioned to within 2° of the native FE axis using one of these 3 valgus angulations.

Purpose: The successful placement of elbow prostheses, external fixators and ligament reconstructions is dependent on the accurate identification of the elbow’s flexion-extension (FE) axis. In the case of periarticular bone loss, the FE axis must be visually estimated, as the necessary anatomical landmarks may not be available. Hence, referencing the uninjured elbow anatomy may prove beneficial in accurately defining this axis. However, this is contingent on the morphological features being similar between the two sides. Our objective was to compare distal humeral morphology between paired specimens. Our hypothesis was that anthropometric measurements from the distal humerus would be similar to the contralateral side.

Method: CT Images of 25 paired distal humeri were obtained. A right-to-left surface registration was then performed on each pair using the iterative closest point (icp) least-squares algorithm, thus placing each specimen in the same coordinate system.. Anthropometric characteristics measured (and compared between the left and right sides) included the angles of the FE and epicondylar axes in both the coronal and transverse planes, the anterior offset of the FE axis with respect to the humeral shaft axis, the length of the FE axis and the radius of curvature of the capitellum and trochlea.

Results: There was no statistically significant difference between the left and right humeri for the eight anthropometric characteristics studied (p > 0.05). The mean difference in magnitude for the FE axis angle was approximately 1.0° in both the coronal and transverse planes and the difference in magnitude for 80% of the paired specimens was less than 1.5°.

Conclusion: The anthropometric features of the distal humerus that are typically employed during elbow surgery are similar from side to side. Preoperative imaging of the contralateral normal elbow should be considered in patients with periarticular bone loss where referencing anatomical landmarks of the injured side is not possible. This information can be used as part of a preoperative plan to determine the ideal position of the implant, ligament reconstruction or external fixator during surgery. Contralateral imaging should be particularly useful when combined with computer-assisted elbow surgery.

Accurate implant alignment with the flexion-extension axis of the elbow is likely critical for optimal function and durability following elbow replacement arthroplasty. Implant alignment can be optimised by imaging the contralateral normal elbow prior to surgery and transferring this information to the diseased elbow in the operating room through registration. Successful registration is dependent on the presence of unique anatomical landmarks. Bone loss can create a challenge for registration as key anatomical landmarks are absent, limiting the number of sampling areas. This study investigated the effect of intraoperative sampling area on registration accuracy. We hypothesised that a low registration error can be achieved by acquiring surface data from areas unlikely compromised due to injury and readily available to the surgeon during typical surgical exposures.

CT images of twenty cadaveric distal humeri were acquired. Surface data was acquired from nineteen anatomical landmarks of the distal humerus using a hand-held laser scanner (FastSCANTM, Polhemus). Registration to the CT image was performed for thirty-nine landmark combinations. Only six combinations are discussed for succinctness.

Combining data from the anterior articular surface and humeral shaft, the lowest registration error was achieved in translation (0.8±0.3 mm) and rotation (0.3±0.2°). However, using data from the posterior shaft and proximal medial supracondylar column, a registration error of 1.1±0.2 mm and 0.4±0.2° was achieved.

Based on the results of this study, a low registration error can be achieved by acquiring data from two areas that are located proximal to the articular surface (the proximal medial supracondylar column and posterior humeral shaft), readily available surgically, and unlikely compromised due to distal humeral fractures, non-unions or bone loss due to severe erosive arthritis. Registration error was similar to the reported resolution of the laser scanner. Overall, this study demonstrates the promise for a successful registration of the contralateral normal elbow to physical surface data of the diseased or injured elbow using only a small portion of undamaged bone structure.