Improvement of image quality applying iterative scatter correction for grid-less skeletal radiography in trauma room setting

Background Iterative reconstruction is well established for CT. Plain radiography also takes advantage of iterative algorithms to reduce scatter radiation and improve image quality. First applications have been described for bedside chest X-ray. A recent experimental approach also provided proof of principle for skeletal imaging. Purpose To examine clinical applicability of iterative scatter correction for skeletal imaging in the trauma setting. Material and Methods In this retrospective single-center study, 209 grid-less radiographs were routinely acquired in the trauma room for 12 months, with imaging of the chest (n = 31), knee (n = 111), pelvis (n = 14), shoulder (n = 24), and other regions close to the trunk (n = 29). Radiographs were postprocessed with iterative scatter correction, doubling the number of images. The radiographs were then independently evaluated by three radiologists and three surgeons. A five-step rating scale and visual grading characteristics analysis were used. The area under the VGC curve (AUC VGC ) quantified differences in image quality. Results Images with iterative scatter correction were generally rated significantly better (AUC VGC  = 0.59, P < 0.01). This included both radiologists (AUC VGC  = 0.61, P < 0.01) and surgeons (AUC VGC  = 0.56, P < 0.01). The image-improving effect was significant for all body regions; in detail: chest (AUC VGC  = 0.64, P < 0.01), knee (AUC VGC  = 0.61, P < 0.01), pelvis (AUC VGC  = 0.60, P = 0.01), shoulder (AUC VGC  = 0.59, P = 0.02), and others close to the trunk (AUC VGC  = 0.59, P < 0.01). Conclusion Iterative scatter correction improves the image quality of grid-less skeletal radiography in the clinical setting for a wide range of body regions. Therefore, iterative scatter correction may be the future method of choice for free exposure imaging when an anti-scatter grid is omitted due to high risk of tube-detector misalignment.