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

Abstract

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 (AUCVGC) quantified differences in image quality. Results Images with iterative scatter correction were generally rated significantly better (AUCVGC = 0.59, P < 0.01). This included both radiologists (AUCVGC = 0.61, P < 0.01) and surgeons (AUCVGC = 0.56, P < 0.01). The image-improving effect was significant for all body regions; in detail: chest (AUCVGC = 0.64, P < 0.01), knee (AUCVGC = 0.61, P < 0.01), pelvis (AUCVGC = 0.60, P = 0.01), shoulder (AUCVGC = 0.59, P = 0.02), and others close to the trunk (AUCVGC = 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.

Image processing setting adaptions according to image dose and radiologist preference can improve image quality in computed radiography of the equine distal limb: A cadaveric study

“…As a result, subjective contrast was restored, as demonstrated by thoracic radiography in humans 7,19 and animals 10 . A recent study reported the same beneficial effect on bone structure perception in human skeletal radiography, 30 which is consistent with our results. Disruptive scatter radiation occurs when object diameters are greater than 10 cm 13 ; thus, low amounts of scatter radiation are expected.…”

“…Disruptive scatter radiation occurs when object diameters are greater than 10 cm 13 ; thus, low amounts of scatter radiation are expected. However, previous studies have found that scatter correction software also shows a positive effect in anatomic regions where low amounts of scatter radiation are expected 30,19 . Physical antiscatter grids are not routinely used in the radiography of equine distal limbs, as they entail a substantial increase in exposure dose, and positioning is time‐consuming.…”

Enhanced visualization of mobile chest X-ray images in the intensive care setting using software scatter correction

“…Hence viewing via medical-grade screen would be necessary to determine radiological value. In the skeletal grid-less study by Lisson et al ( 11 ), a similar scoring methodology within the trauma room setting was adopted but focusing on bone features. Direct comparison with our study is, however, challenging due to the use of a medical-grade viewing screen and visual grading criteria based on visually sharp reproduction.…”

“…To analyze whether the population mean of the differences are different from zero, a paired t-test P value was calculated, under the null hypothesis that the mean of the difference is zero ( 26 ). To analyze whether the distribution of scores is different, the statistical analysis follows an analogous approach ( 11 ). The following two measures were used: (i) a Wilcoxon P value was calculated (under the null hypothesis that the distribution of the pairs is symmetric about zero) ( 27 ); and (ii) the area under the visual grading characteristic curve (AUC VGC ) was reported along with a 95% confidence interval (CI), which was calculated using a non-parametric boot strap with 5000 samples ( 28 , 29 ).…”

“…As an alternative to ASGs, software scatter correction estimates the spatial variation of background scatter incurred by the patient body composition, which is numerically subtracted from the image. Skyflow (Philips Healthcare, Hamburg, Germany) predicts scatter based on a pre-simulated Monte Carlo database for phantom CXR ( 10 ), skeletal imaging ( 11 ), and catheter tip visualization for bedside scans ( 12 ). SimGrid™ (Samsung Electronics Co. Ltd, Suwon, Republic of Korea) estimates scatter based on a trained convolutional neural network (CNN) for bedside CXR images ( 13 ).…”