Accurate measurement of knee alignment, quantified by the hip-knee-ankle (HKA) angle (varus-valgus), serves as an essential biomarker in the diagnosis of various orthopaedic conditions and selection of appropriate therapies. Such angular deformities are assessed from standing X-ray panoramas. However, the limited field-of-view of traditional X-ray imaging systems necessitates the acquisition of several sector images to capture an individual's standing posture, and their subsequent 'stitching' to reconstruct a panoramic image. Such panoramas are typically constructed manually by an X-ray imaging technician, often using various external markers attached to the individual's clothing and visible in two adjacent sector images. To eliminate human error, user-induced variability, improve consistency and reproducibility, and reduce the time associated with the traditional manual 'stitching' protocol, here we propose an automatic panorama construction method that only relies on anatomical features reliably detected in the images, eliminating the need for any external markers or manual input from the technician. The method first performs a rough segmentation of the femur and the tibia, then the sector images are registered by evaluating a distance metric between the corresponding bones along their medial edge. The identified translations are then used to generate the standing panorama image. The method was evaluated on 95 patient image datasets from a database of X-ray images acquired across 10 clinical sites as part of the screening process for a multi-site clinical trial. The panorama reconstruction parameters yielded by the proposed method were compared to those used for the manual panorama construction, which served as gold-standard. The horizontal translation differences were 0:43 ± 1:95 mm 0:26 ± 1:43 mm for the femur and tibia respectively, while the vertical translation differences were 3:76 ± 22:35 mm and 1:85 ± 6:79 mm for the femur and tibia, respectively. Our results showed no statistically significant differences between the HKA angles measured using the automated vs. the manually generated panoramas, and also led to similar decisions with regards to the patient inclusion/exclusion in the clinical trial. Thus, the proposed method was shown to provide comparable performance to manual panorama construction, with increased efficiency, consistency and robustness.
Keywords: Long-limb X-ray; axial deformity at the knee; hip-knee-ankle angle; image registration; panorama reconstruction; segmentation.