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Article Abstract

Background: An accurate diagnosis for staging osteonecrosis of the femoral head (ONFH), particularly in early-stage collapse, is essential for determining therapeutic strategies. Various radiographic views in different femoral positions have been used to detect femoral head collapse. However, previous studies have not established the optimal femoral position that can sensitively detect initial collapse on plain radiography. This study aimed to identify the most sensitive radiographic view for visualizing collapse in early-stage ONFH by analyzing reconstructed frontal images of the femoral head at multiple femoral positions on computed tomography (CT).

Methods: This study included 30 hips with early-stage ONFH (10 hips without collapse and 20 hips with collapse [< three mm] based on the anteroposterior and lateral radiographic images). The presence or absence of collapse in 10 reconstructed frontal images of the femoral head on CT scans, corresponding to 10 different femoral positions, was classified. Furthermore, the ability to detect collapse in each image was compared.

Results: The reconstructed frontal image of the femoral head on CT scans at 45° flexion and 20° abduction had the highest sensitivity for detecting collapse among the analyzed positions. Hence, it had a significantly greater sensitivity than the neutral position (86 versus 53%, P < 0.01). Of 70% who did not present with collapse on plain radiography had collapse on the reconstructed frontal image at 45° flexion and 20° abduction.

Conclusions: The plain radiographic image taken at 45° flexion and 20° abduction, referred to as the 45° Dunn view, had a greater diagnostic potential for early collapse in ONFH compared with the anteroposterior radiographic image. Nevertheless, further research should be performed to comprehensively investigate the areas where collapse occurs in ONFH and to identify the most effective femoral position for detecting collapse on plain radiography.

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http://dx.doi.org/10.1016/j.arth.2025.05.068DOI Listing

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