Influence of windowing and metal artefact reduction algorithms on the volumetric dimensions of five different high-density materials: a cone-beam CT study

Objective: To assess the influence of windowing and metal artefact reduction (MAR) algorithms on the volumetric dimensions of high-density materials using two CBCT systems. Methods: Four cylinders of amalgam, cobalt-chromium, gutta-percha, titanium and zirconium, were manufactured and their physical volumes (PV) were measured. A polymethyl methacrylate phantom containing the cylinders was submitted to CBCT acquisitions with Picasso Trio and OP300 units with their MAR enabled and disabled. The tomographic volume (TV) of all the cylinders was obtained by semi-automatic segmentation using two windowing adjustments: W1—large window width and upper window level; W2—narrow window width and low window level. Volumetric distortion was expressed as the difference between TV and PV. Statistics comprised intraclass correlation coefficient (ICC) and analysis of variance (ANOVA) for repeated measures with Tukey post hoc test (α = 5%). Results: The ICC values ​​indicated excellent reproducibility of TV. Gutta-percha and titanium resulted in the smallest volumetric distortion. Using W1 provided less volumetric distortion for almost all experimental conditions (p < 0.05). Activating MAR algorithm of Picasso Trio underestimated gutta-percha and titanium TV (p < 0.05) and was inefficient in significantly reducing the volumetric distortion of the other materials (p > 0.05). Disabling MAR algorithm of OP300 resulted in smaller volumetric distortion for almost all experimental conditions (p < 0.05). Conclusions: The TV of gutta-percha and titanium were closer to the PV. In general, the MAR algorithms of both systems were inefficient in significantly reducing the volumetric distortion of high-density materials. We encourage the use of large window width and upper window level to evaluate high-density materials.

Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity

Temporal action detection in long, untrimmed videos is an important yet challenging task that requires not only recognizing the categories of actions in videos, but also localizing the start and end times of each action. Recent years, artificial neural networks, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) improve the performance significantly in various computer vision tasks, including action detection. In this paper, we make the most of different granular classifiers and propose to detect action from fine to coarse granularity, which is also in line with the people’s detection habits. Our action detection method is built in the ‘proposal then classification’ framework. We employ several neural network architectures as deep information extractor and segment-level (fine granular) and window-level (coarse granular) classifiers. Each of the proposal and classification steps is executed from the segment to window level. The experimental results show that our method not only achieves detection performance that is comparable to that of state-of-the-art methods, but also has a relatively balanced performance for different action categories.

The Effect of Window Width and Window-level Settings in Non-enhanced Head CT to Increase the Diagnostic Value of Subacute Ischemic Stroke

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PROPER DIFFUSION-WEIGHTED WINDOW LEVEL IN MR IMAGING IN ACUTE VIRAL ENCEPHALITIS AND HYPOXIC ISCHAEMIC ENCEPHALOPATHY

<bold>Introduction:</bold> Diffusion-weighted magnetic resonance (MR) imaging provides image contrast that is different from that provided by conventional MR techniques. Improper Paper Submission DateDiffusion weighted (DW) window level obscures the diffuse cortical abnormality on DW image, hence proper windowed Diffusion weighted images are must for evaluation of cortical & deep grey matter nuclei abnormality. The increased sensitivity of DWI sequences with regard to viral encephalitis/hypoxic ischaemic encephalopathy (HIE) has been shown in several studies This study was performed to evaluate the role of proper DWI window level (250/150) in the diagnosis of viral encephalitis & HIE vs improperly windowed (500-850/250-Paper Publication Date 400) DW images for evaluation of cortical & deep grey matter nuclei. <bold>Material and Methods:</bold> We performed conventional MRI including T1, T2-Weighted and DOI fluid attenuated inversion recovery (FLAIR) sequences and DWI in 16 patients with viral encephalitis & 02 patients of hypoxic ischaemic encephalopathy diagnosed on the basis of laboratory, clinical and radiologic findings. Gradient B value of diffusion was 0, 400 & 800. Properly windowed (250/150) DW image versus an improperly windowed (500-850/250-400) DW image were evaluated for evaluation of cortical & deep grey matter nuclei signal intensity. <bold>Results:</bold> Axial DW images were reviewed at a consistent window level of 250/150. DW image with proper window level of 250/150 shows that the cortical/ deep gray matter nuclei abnormality is more evident. Apparent diffusion coefficient (ADC) map further illustrates the cortical restricted diffusion. Although with improperly windowed DWI (500-850/250-400), there is accentuated grey–white matter differentiation, but improper window level obscures the diffuse cortical abnormality on DW image. With proper window level of DW images, diffusion restriction was picked up in all the 16 cases of acute viral encephalitis and 02 patients of HIE. <bold>Conclusion:</bold> Proper window level of DWI (250/150) is helpful in early diagnosis of acute viral encephalitis & hypoxic ischaemic encephalopathy.