Aims. Hip arthroplasty aims to accurately recreate joint biomechanics. Considerable attention has been paid to vertical and horizontal offset, but femoral head centre in the anteroposterior (AP) plane has received little attention. This study investigates the accuracy of restoration of joint centre of rotation in the AP plane. Methods. Postoperative CT scans of 40 patients who underwent unilateral uncemented total hip arthroplasty were analyzed. Anteroposterior offset (APO) and femoral anteversion were measured on both the operated and non-operated sides. Sagittal tilt of the femoral stem was also measured. APO measured on axial slices was defined as the perpendicular distance between a line drawn from the anterior most point of the proximal femur (anterior reference line) to the centre of the femoral head. The anterior reference line was made parallel to the posterior condylar axis of the knee to correct for rotation. Results. Overall, 26/40 hips had a centre of rotation displaced posteriorly compared to the contralateral hip, increasing to 33/40 once corrected for sagittal tilt, with a mean posterior displacement of 7 mm. Linear regression analysis indicated that stem anteversion needed to be increased by 10.8° to recreate the head centre in the AP plane. Merely matching the native version would result in a 12 mm posterior displacement. Conclusion. This study demonstrates the significant incidence of posterior displacement of the head centre in
This study reports the results of 38 total hip arthroplasties (THAs) in 33 patients aged less than 50 years, using the JRI Furlong hydroxyapatite ceramic (HAC)-coated femoral component. We describe the survival, radiological, and functional outcomes of 33 patients (38 THAs) at a mean follow-up of 27 years (25 to 32) between 1988 and 2018.Aims
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This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.Aims
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