Clinical utility of postprocessed low-dose radiographs in skeletal imaging

Objectives: Radiography remains the mainstay of diagnostic and follow-up imaging. In view of the risks and the increasing use of ionizing radiation, dose reduction is a key issue for research and development. The introduction of digital radiography and the associated access to image postprocessing have opened up new opportunities to minimize the radiation dosage. These advances are contingent upon quality controls to ensure adequate image detail and maintenance of diagnostic confidence. The purpose of this study was to investigate the clinical applicability of postprocessed low-dose images in skeletal radiography. Methods: In our study setting, the median radiation dose for full dose x-rays was 9.61 dGy*cm2 for pelvis, 1.20 dGy*cm2 for shoulder and 18.64 dGy*cm2 for lumbar spine exams. Based on these values, we obtained 200 radiographs for each anatomic region in four consecutive steps, gradually reducing the dose to 84%, 71%, 60 and 50% of the baseline using an automatic exposure control (AEC). 549 patients were enrolled for a total of 600 images. All x-rays were postprocessed with a spatial noise reduction algorithm. Two radiologists assessed the diagnostic value of the radiographs by rating the visualization of anatomical landmarks and image elements on a five-point Likert scale. A mean-sum score was calculated by averaging the two reader’s total scores. Given the non-parametric distribution, we used the Mann-Whitney U test to evaluate the scores. Results: Median dosage at full dose accounted for 38.4%, 48 and 53.2% of the German reference dose area product for shoulder, pelvis and lumbar spine, respectively. The applied radiation was incrementally reduced to 21.5%, 18.4% and 18.7% of the respective reference value for shoulder, pelvis and lumbar spine. Throughout the study, we observed an estimable tendency of superior quality at higher dosage in overall image quality. Statistically significant differences in image quality were restricted to the 50% dose groups in shoulder and lumbar spine images. Regardless of the applied dosage, 598 out of 600 images were of sufficient diagnostic value. Conclusion: In digital radiography image postprocessing allows for extensive reduction of radiation dosage. Despite a trend of superior image detail at higher dose levels, overall quality and, more importantly, diagnostic utility of low-dose images was not significantly affected. Therefore, our results not only confirm the clinical utility of postprocessed low-dose radiographs, but also suggest a widespread deployment of this advanced technology to ensure further dose limitations in clinical practice. Advances in knowledge: The diagnostic image quality of postprocessed skeletal radiographs is not significantly impaired even after extensive dose reduction by up to 20% of the reference value.

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.

Optimal Precision and Accuracy in 4Pi-STORM using Dynamic Spline PSF Models

Dual-objective 4Pi fluorescence detection enables single molecule localization microscopy, e.g. PALM and STORM, with sub-10 nanometer spatial resolution in 3D. Despite its outstanding sensitivity, wider application of this technique has been hindered by complex instrumentation requirements and the challenging nature of the data analysis. The point spread function (PSF) of the 4Pi optical system is difficult to model, leading to periodic image artifacts and compromised resolution. In this work we report the development of a 4Pi-STORM microscope which obtains improved resolution and accuracy by modeling the 4Pi PSF dynamically, while using a simpler optical design. We introduce dynamic spline PSF models, which incorporate fluctuations in the modulation phase of the experimentally determined PSF, capturing the temporal dynamics of the optical system. Our method reaches the theoretical limits for localization precision while largely eliminating phase-wrapping artifacts, by making full use of the information content of the data. With a 3D precision as high as 2 - 3 nanometers, 4Pi-STORM achieves new levels of image detail, and extends the range of biological questions that can be addressed by fluorescence nanoscopy, as we demonstrate by investigating protein and nucleic acid organization in primary neurons and mammalian mitochondria.

Image De-Speckling Based on the Coefficient of Variation, Improved Guided Filter, and Fast Bilateral Filter

Images are widely used in engineering. Unfortunately, medical ultrasound images and synthetic aperture radar (SAR) images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, first, an optimized adaptive Wiener filter (OAWF) is proposed. OAWF can be applied to the input image without the need for logarithmic transform. In addition its performance is improved. Next, the coefficient of variation (CV) is computed from the input image. With the help of CV, the guided filter converts to an improved guided filter (IGF). Next, the improved guided filter is applied on the image. Subsequently, the fast bilateral filter is applied on the image. The proposed filter has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.

Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering

The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.

Design and Reconstruction of Visual Art Based on Virtual Reality

Because traditional methods generally lack the image preprocessing link, the effect of visual image detail processing is not good. In order to enhance the image visual effect, a visual art design method based on virtual reality is proposed. Wavelet transform method is used to denoise the visual image and the noise signal in the image is removed; a binary model of fuzzy space vision fusion is established, the space of the visual image is planned, and the spatial distribution information of the visual image is obtained. According to the principle of light and shadow phenomenon in visual image rendering, the Extend Shadow map algorithm is used to render the visual image. Virtual reality technology was used to reconstruct the preprocessed visual image, and the ant colony algorithm was used to optimize the visual image to realize the visual image design. The results show that the peak signal-to-noise ratio of the visual image processed by the proposed method is high, and the image detail processing effect is better.

Multimodal Medical Supervised Image Fusion Method by CNN

This article proposes a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis. It can implement different types of multimodal medical image fusion problems in batch processing mode and can effectively overcome the problem that traditional fusion problems that can only be solved by single and single image fusion. To a certain extent, it greatly improves the fusion effect, image detail clarity, and time efficiency in a new method. The experimental results indicate that the proposed method exhibits state-of-the-art fusion performance in terms of visual quality and a variety of quantitative evaluation criteria. Its medical diagnostic background is wide.

The Impulsive Nature of Lightning Initiation

<p>We report results from imaging the initiation region of lightning via 3D interferometric beamforming on data collected by the Netherlands-based core of the Low Frequency Array of Antennas (LOFAR). LOFAR achieves 1 nanosecond timing accuracy and meter-scale spatial precision in lightning imaging on pulses observed in the 30-80 MHz band via the 38 Dutch-based stations. This project complements and enhances the previous work of the LOFAR lightning group of Groningen [Hare, B.M., et al., Nature 568, 360363 (2019)], and [Scholten, O., et al., ESSOAr 10503153] in order to improve image detail in regions with weak sources. This project incorporates beamforming techniques to improve upon previously employed methods with the result of improving both spatial and time resolution of lightning sources. In doing so, we have located and imaged the first non-impulsive sources in lightning flashes. These sources are believed to be caused by a streamer-cascade-like initiation event leading to the formation of the first leader in two separate lightning flashes. The initiation starts from essentially background and within a tens of microseconds ramps up a few orders of magnitude before the first impulsive sources connected with lightning leaders are observed. The events are likely an analog of fast breakdown in narrow bipolar events, and here we report their ramp-up rate, propagation speed, and trajectories.</p>