Advances in multiscale image processing and its effects on image quality in skeletal radiography

Fully automatic system to detect and segment the proximal femur in pelvic radiographic images for Legg-Calvé-Perthes disease

This study aimed to develop a method using computer vision techniques to accurately detect and delineate the proximal femur in radiographs of Legg-Calvé-Perthes disease (LCPD) patients. Currently, evaluating femoral head deformity, a crucial predictor of LCPD outcomes, relies on unreliable categorical and qualitative classifications. To address this limitation, we employed the pretrained object detection model YOLOv5 to detect the proximal femur on over 2000 radiographs, including images of shoulders and chests, to enhance robustness and generalizability. Subsequently, we utilized the U-Net convolutional neural network architecture for image segmentation of the proximal femur in more than 800 manually annotated images of stage IV LCPD. The results demonstrate outstanding performance, with the object detection model achieving high accuracy (mean average precision of 0.99) and the segmentation model attaining an accuracy score of 91%, dice coefficient of 0.75, and binary IoU score of 0.85 on the held-out test set. The proposed fully automatic proximal femur detection and segmentation system offers a promising approach to accurately detect and delineate the proximal femoral bone contour in radiographic images, which is essential for further image analysis in LCPD patients. Clinical significance: This study highlights the potential of computer vision techniques for enhancing the reliability of Legg-Calvé-Perthes disease staging and outcome prediction.

Seven general radiography x-ray detectors with pixel sizes ranging from 175 to 76μm: technical evaluation with the focus on orthopaedic imaging

Aim. Flat panel detectors with small pixel sizes general can potentially improve imaging performance in radiography applications requiring fine detail resolution. This study evaluated the imaging performance of seven detectors, covering a wide range of pixel sizes, in the frame of orthopaedic applications.Material and methods. Pixel sizes ranged from 175 (detector A175) to 76μm (detector G76). Modulation transfer function (MTF) and detective quantum efficiency (DQE) were measured using International Electrotechnical Commission (IEC) RQA3 beam quality. Threshold contrast (CT) and a detectability index (d') were measured at three air kerma/image levels. Rabbit shoulder images acquired at 60 kV, over five air kerma levels, were evaluated in a visual grading study for anatomical sharpness, image noise and overall diagnostic image quality by four radiologists. The detectors were compared to detector E124.Results. The 10% point of the MTF ranged from 3.21 to 4.80 mm-1, in going from detector A175to detector G76. DQE(0.5 mm-1) measured at 2.38μGy/image was 0.50 ± 0.05 for six detectors, but was higher for F100at 0.62. High frequency DQE was superior for the smaller pixel detectors, howeverCTfor 0.25 mm discs correlated best with DQE(0.5 mm-1). Correlation betweenCTand the detectability model was good (R2= 0.964).CTfor 0.25 mm diameter discs was significantly higher for D150and F100compared to E124. The visual grading data revealed higher image quality ratings for detectors D125and F100compared to E124. An increase in air kerma was associated with improved perceived sharpness and overall quality score, independent of detector. Detectors B150, D125, F100and G76, performed well in specific tests, however only F100consistently outperformed the reference detector.Conclusion. Pixel size alone was not a reliable predictor of small detail detectability or even perceived sharpness in a visual grading analysis study.