Anode heel effect: Does it impact image quality in digital radiography? A systematic literature review

Solvent Cavitation during Ambient Pressure Drying of Silica Aerogels

Ambient-pressure drying of silica gels stands out as an economical and accessible process for producing monolithic silica aerogels. Gels experience significant deformations during drying due to the capillary pressure generated at the liquid-vapor interface in submicron pores. Proper control of the gel properties and the drying rate is essential to enable reversible drying shrinkage without mechanical failure. Recent in operando microcomputed X-ray tomography (μCT) imaging revealed the kinetics of the phase composition during drying and spring-back. However, to fully explain the underlying mechanisms, spatial resolution is required. Here we show evidence of evaporation by hexane cavitation during the ambient-pressure drying of silylated silica gels by spatially resolved quantitative analysis of μCT data supported by wide-angle X-ray scattering measurements. Cavitation consists of the rupture of the pore liquid put under tension by capillary pressure, creating vapor bubbles within the gels. We found the presence of a homogeneously distributed vapor-air phase in the gels well ahead of the maximum shrinkage. The onset of this vapor/air phase corresponded to a pore volume shrinkage of ca. 50 vol % that was attributed to a critical stiffening of the silica skeleton enabling cavitation. Our results provide new aspects of the relation between the shape changes of silica gels during drying and the evaporation mechanisms. We conclude that stress release by cavitation may be at the origin of the resistance of the silica skeleton to drying stresses. This opens the path toward producing larger monolithic silica aerogels by fine-tuning the drying conditions to exploit cavitation.

Computed vs. film-based radiographs' contour artifacts influence diagnosis of secondary caries

To test local grey-scale changes on dental bitewing radiographs near filling margins for image acquisition. Forty approximal preparations in caries-free amalgam filled teeth and bitewing radiographs were acquired under standardized conditions applying four techniques. Film-based analog radiographs were digitized using flat-bed scanner (FDR). Phosphor-plate computed radiographs (PCR) were directly acquired by scanning VistaScan imaging plates. Image quality was tested using Preset Filter (PF) or manually applied IntraOral Fine Filter (IF) to enhance digital images. Local changes from digital imaging processing were assessed by comparing the margin-near (MN) and margin-far (MF) zone by a multivariate repeated measurements analysis. All images were acquired with 8-bit depth (256 shades). Dentine was displayed in 79 shades for FDR and 54 shades for PCR. PF or IF locally modify bitewing radiographs by darkening marginal dentine by 8 or 29 shades, respectively. The sharpest display of the margin (shades per pixel) from dentine to filling was found for IF (26.2), followed by FDR (23.2), PF (15.3) and PCR (8.3). Computed radiography with phosphor plates generate more homogeneous images compared to flatbed-digitized film-based radiographs. The filling margin was sharpest represented with the IF filter at the detriment of an artificial darkening of the dentine near the margin of the filling. Contour artifacts by filters have the potential to confound diagnosis of secondary caries. Algorithms and filters for sensor data processing, causing local changes above 2% of the dynamic range by non-continuous mathematical functions, should only be applied with caution, manually when diagnosing and reversibly.

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters-An Experimental Study

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

Evaluation of Non-Uniform Image Quality Caused by Anode Heel Effect in Digital Radiography Using Mutual Information

Anode heel effects are known to cause non-uniform image quality, but no method has been proposed to evaluate the non-uniform image quality caused by the heel effect. Therefore, the purpose of this study was to evaluate non-uniform image quality in digital radiographs using a novel circular step-wedge (CSW) phantom and normalized mutual information (nMI). All X-ray images were acquired from a digital radiography system equipped with a CsI flat panel detector. A new acrylic CSW phantom was imaged ten times at various kVp and mAs to evaluate overall and non-uniform image quality with nMI metrics. For comparisons, a conventional contrast-detail resolution phantom was imaged ten times at identical exposure parameters to evaluate overall image quality with visible ratio (VR) metrics, and the phantom was placed in different orientations to assess non-uniform image quality. In addition, heel effect correction (HEC) was executed to elucidate the impact of its effect on image quality. The results showed that both nMI and VR metrics significantly changed with kVp and mAs, and had a significant positive correlation. The positive correlation is suggestive that the nMI metrics have a similar performance to the VR metrics in assessing the overall image quality of digital radiographs. The nMI metrics significantly changed with orientations and also significantly increased after HEC in the anode direction. However, the VR metrics did not change significantly with orientations or with HEC. The results indicate that the nMI metrics were more sensitive than the VR metrics with regards to non-uniform image quality caused by the anode heel effect. In conclusion, the proposed nMI metrics with a CSW phantom outperformed the conventional VR metrics in detecting non-uniform image quality caused by the heel effect, and thus are suitable for quantitatively evaluating non-uniform image quality in digital radiographs with and without HEC.