3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction.

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed tomography (CT) imaging. We introduce a method that can reduce both artifacts simultaneously. To validate the proposed method, we carried out CT scan experiments using plaster dental casts created from dental impressions. After the artifact correction, the CT image quality was greatly improved in terms of image uniformity, contrast-to-noise ratio (CNR), and edge sharpness. We examined the correction effects on the accuracy of the 3D models generated from the CT images. As referenced to the 3D models derived from the optical scan data, the root mean square (RMS) errors were reduced by 8.8~71.7% for three dental casts of different sizes and shapes. Our method offers a solution to challenges posed by artifacts in CT scanning of plaster dental casts, leading to enhanced 3D model accuracy. This advancement holds promise for dental professionals seeking precise digital modeling for diverse applications in dentistry.

Effect of Strain Rate on Hydrogen Embrittlement of Ti6Al4V Alloy.

The phenomenon of hydrogen embrittlement (HE) in metals and alloys, which determines the performance of components in hydrogen environments, has recently been drawing considerable attention. This study explores the interplay between strain rates and solute hydrogen in inducing HE of Ti6Al4V alloy. For the hydrogen-charged sample, as the strain rate was decreased from 10-2/s to 10-5/s, the ductility decreased significantly, but the HE effect on mechanical strength was negligible. The low strain rate (LSR) conditions facilitated the development of high-angle grain boundaries, providing more pathways for hydrogen diffusion and accumulation. The presence of solute hydrogen intensified the formation of nano/micro-voids and intergranular cracking tendencies, with micro-crack occurrences observed exclusively in the LSR conditions. These factors expanded the brittle hydrogen-damaged region more deeply into the interior of the lattice. This, in turn, accelerated both crack initiation and intergranular crack propagation, finally resulting in a considerable HE effect and a reduction in ductility at the LSR. The current study underscores the influence of strain rate on HE, enhancing the predictability of longevity and improving the reliability of components operating in hydrogen-rich environments under various loading conditions.

Deciphering Hydrogen Embrittlement Mechanisms in Ti6Al4V Alloy: Role of Solute Hydrogen and Hydride Phase.

Ti6Al4V (Ti64) is a versatile material, finding applications in a wide range of industries due to its unique properties. However, hydrogen embrittlement (HE) poses a challenge in hydrogen-rich environments, leading to a notable reduction in strength and ductility. This study investigates the complex interplay of solute hydrogen (SH) and hydride phase (HP) formation in Ti64 by employing two different current densities during the charging process. Nanoindentation measurements reveal distinct micro-mechanical behavior in base metal, SH, and HP, providing crucial insights into HE mechanisms affecting macro-mechanical behavior. The fractography and microstructural analysis elucidate the role of SH and HP in hydrogen-assisted cracking behaviors. The presence of SH heightens intergranular cracking tendencies. In contrast, the increased volume of HP provides sites for crack initiation and propagation, resulting in a two-layer brittle fracture pattern. The current study contributes to a comprehensive understanding of HE in Ti6Al4V, essential for developing hydrogen-resistant materials.

Optimization of Aging Temperature and Heat-Treatment Pathways in Additively Manufactured 17-4PH Stainless Steel.

This study investigates the tensile behaviors of additively manufactured (AM) 17-4PH stainless steels heat-treated within various temperature ranges from 400 °C to 700 °C in order to identify the effective aging temperature. Despite an aging treatment of 400-460 °C increasing the retained austenite content, an enhancement of the tensile properties was achieved without a strength-ductility trade-off owing to precipitation hardening by the Cu particles. Due to the intricate evolution of the microstructure, aging treatments above 490 °C led to a loss in yield strength and ductility. A considerable rise in strength and a decrease in ductility were brought about by the increase in the fraction of precipitation-hardened martensitic matrix in aging treatments over 640 °C. The impact of heat-treatment pathways on aging effectiveness and tensile anisotropy was then examined. Direct aging at 482 °C for an hour had hardly any effect on wrought 17-4PH, but it increased the yield strength of AM counterparts from 436-457 to 588-604 MPa. A solid-solution treatment at 1038 °C for one hour resulted in a significant drop in the austenite fraction, which led to an increase in the yield (from 436-457 to 841-919 MPa) and tensile strengths (from 1106-1127 to 1254-1256 MPa) with a sacrifice in ductility. Improved strength and ductility were realized by a solid-solution followed by an aging treatment, achieving 1371-1399 MPa. The tensile behaviors of AM 17-4PH were isotropic both parallel and perpendicular to the building direction.

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting.

Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images.

Cone-beam dental CT can provide high-precision 3D images of the teeth and surrounding bones. From the 3D CT images, 3D models, also called digital impressions, can be computed for CAD/CAM-based fabrication of dental restorations or orthodontic devices. However, the cone-beam angle-dependent artifacts, mostly caused by the incompleteness of the projection data acquired in the circular cone-beam scan geometry, can induce significant errors in the 3D models. Using a micro-CT, we acquired CT projection data of plaster cast models at several different cone-beam angles, and we investigated the dependency of the model errors on the cone-beam angle in comparison with the reference models obtained from the optical scanning of the plaster models. For the 3D CT image reconstruction, we used the conventional Feldkamp algorithm and the combined half-scan image reconstruction algorithm to investigate the dependency of the model errors on the image reconstruction algorithm. We analyzed the mean of positive deviations and the mean of negative deviations of the surface points on the CT-image-derived 3D models from the reference model, and we compared them between the two image reconstruction algorithms. It has been found that the model error increases as the cone-beam angle increases in both algorithms. However, the model errors are smaller in the combined half-scan image reconstruction when the cone-beam angle is as large as 10 degrees.

Transient Phase-Driven Cyclic Deformation in Additively Manufactured 15-5 PH Steel.

The present work extends the examination of selective laser melting (SLM)-fabricated 15-5 PH steel with the 8%-transient-austenite-phase towards fully-reversed strain-controlled low-cycle fatigue (LCF) test. The cyclic-deformation response and microstructural evolution were investigated via in-situ neutron-diffraction measurements. The transient-austenite-phase rapidly transformed into the martensite phase in the initial cyclic-hardening stage, followed by an almost complete martensitic transformation in the cyclic-softening and steady stage. The compressive stress was much greater than the tensile stress at the same strain amplitude. The enhanced martensitic transformation associated with lower dislocation densities under compression predominantly governed such a striking tension-compression asymmetry in the SLM-built 15-5 PH.

Microstructural Evolution and Mechanical Properties of Non-Equiatomic (CoNi)74.66Cr17Fe8C0.34 High-Entropy Alloy.

In this study, we manufactured a non-equiatomic (CoNi)74.66Cr17Fe8C0.34 high-entropy alloy (HEA) consisting of a single-phase face-centered-cubic structure. We applied in situ neutron diffraction coupled with electron backscattered diffraction (EBSD) and transmission electron microscopy (TEM) to investigate its tensile properties, microstructural evolution, lattice strains and texture development, and the stacking fault energy. The non-equiatomic (CoNi)74.66Cr17Fe8C0.34 HEA revealed a good combination of strength and ductility in mechanical properties compared to the equiatomic CoNiCrFe HEA, due to both stable solid solution and precipitation-strengthened effects. The non-equiatomic stoichiometry resulted in not only a lower electronegativity mismatch, indicating a more stable state of solid solution, but also a higher stacking fault energy (SFE, ~50 mJ/m2) due to the higher amount of Ni and the lower amount of Cr. This higher SFE led to a more active motion of dislocations relative to mechanical twinning, resulting in severe lattice distortion near the grain boundaries and dislocation entanglement near the twin boundaries. The abrupt increase in the strain hardening rate (SHR) at the 1~3% strain during tensile deformation might be attributed to the unusual stress triaxiality in the {200} grain family. The current findings provide new perspectives for designing non-equiatomic HEAs.

Tailoring grain sizes of the biodegradable iron-based alloys by pre-additive manufacturing microalloying.

We demonstrated the design of pre-additive manufacturing microalloying elements in tuning the microstructure of iron (Fe)-based alloys for their tunable mechanical properties. We tailored the microalloying stoichiometry of the feedstock to control the grain sizes of the metallic alloy systems. Two specific microalloying stoichiometries were reported, namely biodegradable iron powder with 99.5% purity (BDFe) and that with 98.5% (BDFe-Mo). Compared with the BDFe, the BDFe-Mo powder was found to have lower coefficient of thermal expansion (CTE) value and better oxidation resistance during consecutive heating and cooling cycles. The selective laser melting (SLM)-built BDFe-Mo exhibited high ultimate tensile strength (UTS) of 1200 MPa and fair elongation of 13.5%, while the SLM-built BDFe alloy revealed a much lower UTS of 495 MPa and a relatively better elongation of 17.5%, indicating the strength enhancement compared with the other biodegradable systems. Such an enhanced mechanical behavior in the BDFe-Mo was assigned to the dominant mechanism of ferrite grain refinement coupled with precipitate strengthening. Our findings suggest the tunability of outstanding strength-ductility combination by tailoring the pre-additive manufacturing microalloying elements with their proper concentrations.

Tunable Mechanical and Electrical Properties of Coaxial Electrospun Composite Nanofibers of P(VDF-TrFE) and P(VDF-TrFE-CTFE).

The coaxial core/shell composite electrospun nanofibers consisting of relaxor ferroelectric P(VDF-TrFE-CTFE) and ferroelectric P(VDF-TrFE) polymers are successfully tailored towards superior structural, mechanical, and electrical properties over the individual polymers. The core/shell-TrFE/CTFE membrane discloses a more prominent mechanical anisotropy between the revolving direction (RD) and cross direction (CD) associated with a higher tensile modulus of 26.9 MPa and good strength-ductility balance, beneficial from a better degree of nanofiber alignment, the increased density, and C-F bonding. The interfacial coupling between the terpolymer P(VDF-TrFE-CTFE) and copolymer P(VDF-TrFE) is responsible for comparable full-frequency dielectric responses between the core/shell-TrFE/CTFE and pristine terpolymer. Moreover, an impressive piezoelectric coefficient up to 50.5 pm/V is achieved in the core/shell-TrFE/CTFE composite structure. Our findings corroborate the promising approach of coaxial electrospinning in efficiently tuning mechanical and electrical performances of the electrospun core/shell composite nanofiber membranes-based electroactive polymers (EAPs) actuators as artificial muscle implants.

Half-scan artifact correction using generative adversarial network for dental CT.

Half-scan image reconstruction with Parker weighting can correct motion artifacts in dental CT images taken with a slow scan-based dental CT. Since the residual half-scan artifacts in the dental CT images appear much stronger than those in medical CT images, the artifacts often persist to the extent that they compromise the surface-rendered bone and tooth images computed from the dental CT images. We used a variation of generative adversarial network (GAN), so-called U-WGAN, to correct half-scan artifacts in dental CT images. For the generative network of GAN, we used a U-net structure of five stages to take advantage of its high computational efficiency. We trained the network using the Wasserstein loss function on the dental CT images of 40 patients. We tested the network with comparing its output images to the half-scan images corrected with other methods; Parker weighting and the other two popular GANs, that is, SRGAN and m-WGAN. For the quantitative comparison, we used the image quality metrics measuring the similarity of the corrected images to the full-scan images (reference images) and the noise level on the corrected images. We also compared the visual quality of the surface-rendered bone and tooth images. We observed that the proposed network outperformed Parker weighting and other GANs in all the image quality metrics. The computation time for the proposed network to process 336×336×336 3D images on a GPU-equipped personal computer was about 3 s, which was much shorter than those of SRGAN and m-WGAN, 50 s and 54 s, respectively.

Unravelling thermal history during additive manufacturing of martensitic stainless steel.

In-situ thermal cycling neutron diffraction experiments were employed to unravel the effect of thermal history on the evolution of phase stability and internal stresses during the additive manufacturing (AM) process. While the fully-reversible martensite-austenite phase transformation was observed in the earlier thermal cycles where heating temperatures were higher than Af, the subsequent damped thermal cycles exhibited irreversible phase transformation forming reverted austenite. With increasing number of thermal cycles, the thermal stability of the retained austenite increased, which decreased the coefficient of thermal expansion. However, martensite revealed higher compressive residual stresses and lower dislocation density, indicating inhomogeneous distributions of the residual stresses and microstructures on the inside and on the surface of the AM component. The compressive residual stresses that acted on the martensite resulted preferentially from transformation strain and additionally from thermal misfit strain, and the decrease in the dislocation density might have been due to the strong recovery effect near the Ac1 temperature.

Image denoising by transfer learning of generative adversarial network for dental CT.

The successful development of the image denoising techniques for low-dose computed tomography (LDCT) was largely owing to the public-domain availability of spatially-aligned high- and low-dose CT image pairs. Even though low-dose CT scans are also highly desired in dental imaging, public-domain databases of dental CT image pairs have not been established yet. In this paper, we propose a dental CT image denoising method based on the transfer learning of a generative adversarial network (GAN) from the public-domain CT images. We trained a generative adversarial network with the Wasserstein loss function (WGAN) using 5,100 high- and low-dose medical CT image pairs of human chest and abdomen. For the generative network of GAN, we used the U-net structure of five stages to exploit its high computational efficiency. After training the proposed network, named U-WGAN, we fine-tuned the network with 3,006 dental CT image pairs of two different human skull phantoms. For the high- and low-dose scans of the phantoms, we set the tube current of the dental CT to 10 mA and 4 mA, respectively, with the tube voltage set to 90 kVp in both scans. We applied the trained network to denoising of low-dose dental CT images of dental phantoms and adult humans. The U-net processed images showed over-smoothing effects even though U-net had a good performance in the quantitative metrics. U-WGAN showed similar denoising performance to WGAN, but it reduced the computation time of WGAN by a factor of 10. The fine-tuning procedure in the transfer learning scheme enhanced the network performance in terms of the quantitative metrics, and it also improved visual appearance of the processed images. Even though the number of fine-tuning images was very limited in this study, we think the transfer learning scheme can be a good option for developing deep learning networks for dental CT image denoising.

U-net based metal segmentation on projection domain for metal artifact reduction in dental CT.

Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.

A motion artifact reduction method for dental CT based on subpixel-resolution image registration of projection data.

High-resolution imaging is essential in three-dimensional (3D) CT image-based digital dentistry. A small amount of head motion during a CT scan can degrade the spatial resolution of the images to the extent where the efficacy of 3D image-based digital dentistry is greatly compromised. We introduce a retrospective motion artifact reduction (MAR) method for dental CTs that eliminates the necessity for any external motion tracking devices. Assuming that rigid-body motions are dominant in a dental scan of a human head, we extracted motion information from the projection data. By taking the cross-correlation between two successive projection data for every projection view, we determined the displacement of the projection data at each view. We experimentally found that any motion of the imaging object during the scan resulted in displacement of the projection data proportional to the motion amplitude. We decomposed the displacement into two components, one caused by translational motion and the other caused by rotational motion. The displacement components were used to correct the projection data before CT image reconstruction. We experimentally verified the MAR method using the projection data of a few phantoms acquired through a clinical dental CT machine. When the MAR performance was evaluated by the structural similarity index (SSIM) and the normalized absolute error (NAE) in reference to the motion-less images, the SSIM improved to 99% while the NAE was reduced by 80-90%.

A head motion estimation algorithm for motion artifact correction in dental CT imaging.

A small head motion of the patient can compromise the image quality in a dental CT, in which a slow cone-beam scan is adopted. We introduce a retrospective head motion estimation method by which we can estimate the motion waveform from the projection images without employing any external motion monitoring devices. We compute the cross-correlation between every two successive projection images, which results in a sinusoid-like displacement curve over the projection view when there is no patient motion. However, the displacement curve deviates from the sinusoid-like form when patient motion occurs. We develop a method to estimate the motion waveform with a single parameter derived from the displacement curve with aid of image entropy minimization. To verify the motion estimation method, we use a lab-built micro-CT that can emulate major head motions during dental CT scans, such as tilting and nodding, in a controlled way. We find that the estimated motion waveform conforms well to the actual motion waveform. To further verify the motion estimation method, we correct the motion artifacts with the estimated motion waveform. After motion artifact correction, the corrected images look almost identical to the reference images, with structural similarity index values greater than 0.81 in the phantom and rat imaging studies.

Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography.

PURPOSE: In a dental CT scan, the presence of dental fillings or dental implants generates severe metal artifacts that often compromise readability of the CT images. Many metal artifact reduction (MAR) techniques have been introduced, but dental CT scans still suffer from severe metal artifacts particularly when multiple dental fillings or implants exist around the region of interest. The high attenuation coefficient of teeth often causes erroneous metal segmentation, compromising the MAR performance. We propose a metal segmentation method for a dental CT that is based on dual-energy imaging with a narrow energy gap. METHODS: Unlike a conventional dual-energy CT, we acquire two projection data sets at two close tube voltages (80 and 90 kVp ), and then, we compute the difference image between the two projection images with an optimized weighting factor so as to maximize the contrast of the metal regions. We reconstruct CT images from the weighted difference image to identify the metal region with global thresholding. We forward project the identified metal region to designate metal trace on the projection image. We substitute the pixel values on the metal trace with the ones computed by the region filling method. The region filling in the metal trace removes high-intensity data made by the metallic objects from the projection image. We reconstruct final CT images from the region-filled projection image with the fusion-based approach. We have done imaging experiments on a dental phantom and a human skull phantom using a lab-built micro-CT and a commercial dental CT system. RESULTS: We have corrected the projection images of a dental phantom and a human skull phantom using the single-energy and dual-energy-based metal segmentation methods. The single-energy-based method often failed in correcting the metal artifacts on the slices on which tooth enamel exists. The dual-energy-based method showed better MAR performances in all cases regardless of the presence of tooth enamel on the slice of interest. We have compared the MAR performances between both methods in terms of the relative error (REL), the sum of squared difference (SSD) and the normalized absolute difference (NAD). For the dental phantom images corrected by the single-energy-based method, the metric values were 95.3%, 94.5%, and 90.6%, respectively, while they were 90.1%, 90.05%, and 86.4%, respectively, for the images corrected by the dual-energy-based method. For the human skull phantom images, the metric values were improved from 95.6%, 91.5%, and 89.6%, respectively, to 88.2%, 82.5%, and 81.3%, respectively. CONCLUSIONS: The proposed dual-energy-based method has shown better performance in metal segmentation leading to better MAR performance in dental imaging. We expect the proposed metal segmentation method can be used to improve the MAR performance of existing MAR techniques that have metal segmentation steps in their correction procedures.

Dual-modal cancer detection based on optical pH sensing and Raman spectroscopy.

A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification.

Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease.

The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid ß, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD.

Influence of water content on Raman spectroscopy characterization of skin sample.

We report that the Raman spectrum obtained from porcine skin varies significantly with the change of skin water content. At different water contents from 40 to 55 wt.%, the Raman spectra results using confocal Raman spectroscopy show that the spectral variation of porcine skin is highly affected by skin water content. Experimental data are consistent with the Monte Carlo calculation and it is proved that the intensity of the Raman spectrum depends on the angle distribution and collection efficiency of backscattered light from the sample surface for a varied water content. It is suggested that water content for a given skin sample should be controlled carefully to minimize errors and deviations in the Raman peak analyses.