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
Methods
In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis.Aims
Methods
