[18F]Sodium fluoride PET-MRI detects increased metabolic bone response to whole-joint loading stress in osteoarthritic knees.

OBJECTIVE: Altered joint function is a hallmark of osteoarthritis (OA). Imaging techniques for joint function are limited, but [18F]sodium fluoride (NaF) PET-MRI may assess the acute joint response to loading stresses. [18F]NaF PET-MRI was used to study the acute joint response to exercise in OA knees, and compare relationships between regions of increased uptake after loading and structural OA progression two years later. METHODS: In this prospective study, 10 participants with knee OA (59 ± 8 years; 8 female) were scanned twice consecutively using a PET-MR system and performed a one-legged squat exercise between scans. Changes in tracer uptake measures in 9 bone regions were compared between knees that did and did not exercise with a mixed-effects model. Areas of focally large changes in uptake between scans (ROIfocal, ΔSUVmax > 3) were identified and the presence of structural MRI features was noted. Five participants returned two years later to assess structural change on MRI. RESULTS: There was a significant increase in [18F]NaF uptake in OA exercised knees (SUV P < 0.001, KiP = 0.002, K1P < 0.001) that differed by bone region. CONCLUSION: There were regional differences in the acute bone metabolic response to exercise and areas of focally large changes in the metabolic bone response that might be representative of whole-joint dysfunction.

A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis.

OBJECTIVE: The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis. METHOD: Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials. RESULTS: The 3D neural network predicted the peak KAM for all test steps with r2( Murray et al., 2012) 2 = 0.78. This model predicted individuals' average peak KAM during natural walking with r2( Murray et al., 2012) 2 = 0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy = 0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r2( Murray et al., 2012) 2 = 0.85) to the 3D neural network, but the sagittal plane neural network did not (r2( Murray et al., 2012) 2 = 0.14). CONCLUSION: Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis.