PHOTON-COUNTING CT-BASED TRABECULAR BONE ANALYSIS IN THE KNEE: A COMPARATIVE STUDY OF ADVANCED OSTEOARTHRITIS AND HEALTHY CONTROLS

INTRODUCTION

The advent of photon counting CT is a major advance in the development of CT technology. Its enhanced spatial resolution, compared to conventional CT, and its much-reduced radiation dose make it a promising tool for in vivo assessment of bone microarchitecture in clinical settings. For example, prior studies relying on HR-pQCT and Micro CT have shown greater volumetric bone mineral density (vBMD) and trabecular (Tb) thickness (Th) were significantly higher in the medial compartment and associated with increased disease severity. There is no data on trabecular bone structure using photon counting CT in patients with osteoarthritis (OA).

OBJECTIVE

To compare High-Resolution PCCT-defined trabecular bone microstructure between patients with advanced OA versus healthy controls.

METHODS

We used data from the ongoing DIAMOND knee study which investigates the role of neuroinflammation in chronic postoperative pain after TKR. To date, 9 healthy controls and 36 patients with advanced knee OA scheduled for total knee replacements have been recruited, including 7 patients who underwent unilateral PCCT. All other patients and healthy controls had bilateral knee scans. We used a Naeotom 144 Alpha PCCT scanner manufactured by Siemens Healthineers (Erlangen, Germany). Scans were performed with a tube voltage of (120 keV) and, to provide maximum scan performance and minimum noise deterioration, slice increments of 0.2 were used. We also utilized a slice thickness of 0.2 mm, rotation time 0.5 seconds, and pitch 0.85 Images were reconstructed with sharp bone kernel Br89 and matrix 1024 × 1024.. The field of view varied depending on the patient’s size, thus resulting in a variable voxel in plane dimension (0.2-0.4 mm). Regions of interests were defined for the proximal tibia and distal femur in a stack height defined by slices equivalent to 1/6^th to 1/4^th of the measured joint width, prescribed distally or proximally from the joint line, respectively. Images were analyzed using a previously reported iterative threshold-seeking algorithm with 3D connectivity check to separate trabecular bone from marrow. Apparent structural parameters were derived from bone volume (BV), bone surface (BS), and total volume (TV) according to equations by Parfitt’s model of parallel plates (Tb.Th, Tb.Separation, BV/TV). These trabecular bone measures were compared between OA and healthy knees using independent sample t-test or non-parametric Wilcoxon tests, depending on normality assumptions. All of the analyses were performed compartment-wise in all four ROIs. These images analyses steps were derived from methods previously published by Wong et al. (DOI: https://doi.org/10.1016/j.jocd.2018.04.001).

RESULTS

We analyzed data from 12 knees of 12 patients with advanced knee OA (mean age 66.0 ± 9.4 years, 67% female) and 17 knees from 9 healthy controls (mean age 60.8 ± 10.7 years, 56% female). Total Tb volume was consistently greater in OA knees compared to controls in both the medial (OA: M = 267.15 mm³, SD = 31.53; HC: M = 245.26 mm³, SD = 26.51) and lateral (OA: M = 278.45 mm³, SD = 43.83; HC: M = 252.99 mm³, SD = 30.54) tibial compartments. Although differences in other bone parameters were not consistent across the four compartments, OA knees tended to show slightly higher trabecular thickness and lower BV/TV. Variability between compartments was observed, particularly in the femur, where group differences were less apparent, though none of these measures reached statistical significance.

CONCLUSION

In this preliminary study using high-resolution PCCT, knees with advanced OA consistently exhibited larger trabecular regions in the tibia compared to healthy controls. Subtle differences in bone structure were also observed, which may reflect early subchondral bone remodeling in response to altered joint loading and mechanical stress. However, interpretation of these microstructural changes is limited by the small sample size and variability in voxel size across scans, both of which could affect the precision of morphometric estimates.