Validation of a 3D CT imaging method for quantifying implant migration following anatomic total shoulder arthroplasty

Geometric accuracy of low-dose CT scans for use in shoulder musculoskeletal research applications

Computed tomography (CT) imaging is frequently employed in a variety of musculoskeletal research applications. Although research studies often use imaging protocols developed for clinical applications, lower dose protocols are likely possible when the goal is to reconstruct 3D bone models. Our purpose was to describe the dose-accuracy trade-off between incrementally lower-dose CT scans and the geometric reconstruction accuracy of the humerus, scapula, and clavicle. Six shoulder specimens were acquired and scanned using 5 helical CT protocols: 1) 120 kVp, 450 mA (full-dose); 2) 120 kVp, 120 mA; 3) 120 kVp, 100 mA; 4) 100 kVp, 100 mA; 5) 80 kVp, 80 mA. Scans were segmented and reconstructed into 3D surface meshes. Geometric error was assessed by comparing the surfaces of the low-dose meshes to the full-dose (gold standard) mesh and was described using mean absolute error, bias, precision, and worst-case error. All low-dose protocols resulted in a >70 % reduction in the effective dose. Lower dose scans resulted in higher geometric errors; however, error magnitudes were generally <0.5 mm. These data suggest that the effective dose associated with CT imaging can be substantially reduced without a significant loss of geometric reconstruction accuracy.

Shoulder Bone Segmentation with DeepLab and U-Net

Evaluation of 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in place of computed tomography. Segmentation of shoulder anatomy, particularly humeral head and acetabulum, is needed for detailed assessment of each anatomy and for pre-surgical preparation. In this study we compared performance of two popular deep learning models based on Google's DeepLab and U-Net to perform automated segmentation on ZTE MRI of human shoulders. Axial ZTE images of normal shoulders (n=31) acquired at 3-Tesla were annotated for training with a DeepLab and 2D U-Net, and the trained model was validated with testing data (n=13). While both models showed visually satisfactory results for segmenting the humeral bone, U-Net slightly over-estimated while DeepLab under-estimated the segmented area compared to the ground truth. Testing accuracy quantified by Dice score was significantly higher (p<0.05) for U-Net (88%) than DeepLab (81%) for the humeral segmentation. We have also implemented the U-Net model onto an MRI console for a push-button DL segmentation processing. Although this is an early work with limitations, our approach has the potential to improve shoulder MR evaluation hindered by manual post-processing and may provide clinical benefit for quickly visualizing bones of the glenohumeral joint.

Accuracy of radiostereometric analysis using a motorized Roentgen system in a pilot study for clinical simulation

Radiostereometric analysis (RSA) is routinely implemented with two paired Roentgen tubes for three-dimensional (3D) implant migration measurements. A conventional set-up of one stationary tube and one mobile could be time-consuming. Utilizing two customized ceiling-mounted tubes is normally associated with investment costs. Thus, a pilot set-up of a motorized system (single Roentgen source) for radiostereometric image acquisition may be a time-saving and space-efficient alternative. RSA using the motorized system is feasible in this study as a non-synchronized image acquisition technique, however, patient motion may occur and influence the assessment of implant migration. The phantom study aimed to assess accuracy of RSA using the motorized Roentgen system in this in vitro study. Accuracy values of translations and rotations were ±0.29 mm and ±0.48° for the single Roentgen source RSA set-up and ±0.26 mm and ±0.48° for the conventional RSA set-up. This study was also performed to simulate potential patient motion during exposure intervals between paired image acquisition. RSA using the motorized system is able to implement RSA with acceptable accuracy. In general, RSA with synchronized image acquisition is the gold standard to access in vivo implant migration with the highest accuracy. Patient motion exists in non-synchronized image acquisition techniques and results in RSA-related motion artifacts. Then we introduced what RSA-related motion artifacts are. The uniplanar calibration cage applied in the study has a few fiducial and control markers, and some of the markers were occluded in radiographs. Whereas, the number of markers in the calibration cage is correlated with accuracy of 3D implant reconstruction.