Beam Hardening Artifacts Research Articles

Mineral quantitative characterization method based on basis material decomposition model by dual-energy computed tomography.

Dual-energy computed tomography (DECT) can reconstruct electron density ρe and effective atomic number Zeff distribution for material discrimination. Image-domain basis material decomposition (IBMD) method is a widely used DECT method. However, IBMD method cannot be used for mineral identification directly due to limitations of complex basis material determination, beam hardening artifacts, and inherent errors caused by approximate empirical formulas. This study proposes an improved IBMD (IIBMD) method to overcome the above limitations. In IIBMD method, the composition of basis material is optimized to obtain accurate decomposition coefficients, which enables accurate ρe and Zeff distribution. Moreover, the thickness of basis material is optimized to reduce the effect of beam hardening. Furthermore, two formulas in place of empirical formulas are proposed to calculate ρe and Zeff. Finally, a threshold technique is applied to separate different mineral phases. Numerical simulations and practical experiments using a photon-counting detector CT system are implemented to verify IIBMD method. Results show that the relative errors of ρe and Zeff for seven common minerals are down to 5%, lower than most of the existing DECT methods for rocks. Reasonable volume fraction results of mineral phases are thus obtained through threshold segmentation. This study demonstrates that the proposed IIBMD method has high practical value in mineralogical identification.

Pediatric low-dose head CT: Image quality improvement using iterative model reconstruction.

To evaluate the differences in pediatric non-contrast low-dose head computed tomography (CT) between filtered-back projection and iterative model reconstruction using objective and subjective image quality evaluation. A retrospective study evaluated children undergoing low-dose non-contrast head CT. All CT scans were reconstructed using both filtered-back projection and iterative model reconstruction. Objective image quality analysis was performed using contrast and signal-to-noise ratios for the supra- and infratentorial brain regions of identical regions of interest on the two reconstruction methods. Two experienced pediatric neuroradiologists evaluated subjective image quality, visibility of structures, and artifacts. We evaluated 233 low-dose brain CT scans of 148 pediatric patients. There was a ∼2-fold improvement in the contrast-to-noise ratio between gray and white matter in the infra- and supratentorial regions (p < 0.001) using iterative model reconstruction compared to filtered-back projection. The white and gray matter signal-to-noise ratio improved more than 2-fold using iterative model reconstruction (p < 0.001). Furthermore, radiologists graded anatomical details, gray-white matter differentiation, beam hardening artifacts, and image quality using iterative model reconstructions as superior to filtered-back projection reconstructions. Iterative model reconstructions had better contrast-to-noise and signal-to-noise ratios with fewer artifacts in pediatric CT brain scans using low-dose radiation protocols. This image quality improvement was demonstrated in the supra- and infratentorial regions. This method thus comprises an important tool for reducing children's exposure while maintaining diagnostic capability.

Individualized Scan Protocol Based on Body Mass Index in Dual-Energy Computed Tomography Pulmonary Angiography to Reduce Radiation and Contrast Doses with Improved Image Quality.

BACKGROUND We evaluated an individualized dual-energy computed tomography (DECT) scan protocol by combining optimal monochromatic images with an appropriate ASIR-V reconstruction strength in computed tomography pulmonary angiography (CTPA) to reduce radiation and iodine doses and superior vena cava (SVC) artifacts. MATERIAL AND METHODS A total of 127 patients who underwent CTPA were prospectively enrolled and randomly divided into a standard (n=63) and individualized group (n=64). The standard group used 120 kVp, 150 mAs, and 60 mL contrast media at an injection rate of 5 mL/s; the individualized group used DECT imaging mode with tube current selected according to patients' BMI (BMI ≤20 kg/m², 200 mA; 20< BMI ≤23 kg/m², 240 mA; 23< BMI ≤25 kg/m², 280 mA; BMI >25 kg/m², 320 mA). Contrast media intake was 130 mgI/kg with an injection time of 7 s. The data in the individualized group was reconstructed to 55-70 keV (5 keV interval) monochromatic images combined with 40-80% ASIR-V (10% interval). Radiation dose, contrast dose, and image quality were compared between the groups. RESULTS There were no significant differences in patient habitus. Compared with the standard group, the individualized group significantly decreased radiation dose by 33.93% (3.31±0.57 mSv vs 5.01±0.34 mSv) and contrast dose by 56.95% (9.04±1.40 gI vs 21.00±0.00 gI). The 60 keV image with 80%ASIR-V in the individualized group provided the best image quality and further reduced SVC beam-hardening artifacts. CONCLUSIONS The use of BMI-dependent DECT protocol in CTPA further reduces radiation dose, contrast agent dose, and SVC artifacts, with the 60 keV images reconstructed using 80%ASiR-V having the best image quality.

Development of energy-dispersive diffraction and grayscale quantification in polychromatic synchrotron tomography

Synchrotron white-beam tomography has important applications for temporally evolving or highly absorbing samples. The polychromatic nature of the white beam results in beam hardening artifacts unless measures are taken to correct these artifacts. Moreover, while the absorption tomography data show the morphology of samples, for some applications, the ability to probe crystallographic phase would enhance both the interpretation of the tomography data and the overall understanding of the sample. This work will describe developments at the 7-BM beamline of the Advanced Photon Source to both effectively correct for the polychromatic nature of the white beam and to provide energy-dispersive diffraction data which can be coupled with white-beam tomography.

Sinogram domain metal artifact correction of CT via deep learning

PurposeMetal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning. MethodsWe present a model composed of three modules: (a) a Sinogram Metal Segmentation Network (Seg-Net), (b) a Sinogram Enhancement Network (Sino-Net), and (c) a Fusion Module. The model starts by using the Attention U-Net network to segment the metal regions in the sinogram. The segmented metal regions are then interpolated to obtain a sinogram image free of metal. The Sino-Net is then applied to compensate for the loss of organizational and artifact information in the metal regions. The corrected metal sinogram and the interpolated metal-free sinogram are then used to reconstruct the metal CT and metal-free CT images, respectively. Finally, the Fusion Module combines the two CT images to produce the result. ResultsOur proposed method shows strong performance in both qualitative and quantitative evaluations. The peak signal-to-noise ratio (PSNR) of the CT image before and after correction was 18.22 and 30.32, respectively. The structural similarity index measure (SSIM) improved from 0.75 to 0.99, and the weighted peak signal-to-noise ratio (WPSNR) increased from 21.69 to 35.68. ConclusionsOur proposed method demonstrates the reliability of high-accuracy correction of beam hardening artifacts.

Multi-material blind beam hardening correction in near real-time based on non-linearity adjustment of projections

Beam hardening (BH) is one of the major artifacts that severely reduces the quality of computed tomography (CT) imaging. This BH artifact arises due to the polychromatic nature of the X-ray source and causes cupping and streak artifacts. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. This is achieved by performing a wide sweep of the material based on an experimentally measured look-up table to obtain the closest estimate of the material. Then, the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated polychromatic projection is accurately derived using the least square estimation (LSE) method by minimizing the difference between the experimental projection and the linear combination of simulated polychromatic projections. As a result, an accurate non-linearity correction term is derived that leads to an accurate BH correction result. The simulated projections in this work are performed using a multi-GPU-accelerated forward projection model which ensures a fast BH correction in near real-time. To evaluate the proposed BH correction method, we have conducted extensive experiments on real-world CT data. It is shown that the proposed method results in images with improved contrast-to-noise ratio (CNR) in comparison to the images corrected from only the scatter artifacts and the BH-corrected images using the state-of-the-art empirical BH correction method.

Evaluation of beam hardening artifacts around dental implants: CT study on bovine ribs

Background/Aim: The aim of this study was to evaluate beam hardening artifacts generated by Grade 4 and Grade 5 dental implants on computed tomography (CT) images at low and high kilovoltage peaks (kVp). Material and Methods: A total of 16 implants, 8 of which were Grade 4 and 8 were Grade 5, were inserted into bovine ribs. CT images of bovine ribs were acquired using two different exposure protocol: low kVp and high kVp. Beam hardening artifacts generated by Grade 4 and Grade 5 dental implants were calculated by the mean Hounsfield unit (HU) within a standardized region-of-interest (ROI). Results: Artifact in Grade 4 implants were greater than that in Grade 5 implants. Also, artifacts at the high kVp were lower than that at the low kVp. Conclusions: CT scans providing HU values can be used to evaluate the beam hardening artifact. Beam hardening artifacts decreased in the CT images with high kVp. Grade 5 dental implants have an advantage by producing less severe beam hardening artifacts.

Application of metal artifact reduction algorithm in reducing metal artifacts in post-surgery pediatric low radiation dose spine computed tomography (CT) images.

The commonly used methods for removing metal-induced beam hardening artifacts often rely on the use of high energy photons with either high tube voltage or high energy virtual monoenergetic images in dual-energy computed tomography (CT), the radiation dose was usually relatively high in order to generate adequate signals. This retrospective study is designed to evaluate the application of a metal artifact reduction (MAR) algorithm in reducing pedicle screw metal-caused beam hardening artifacts in post-surgery pediatric low radiation dose spine CT images. Seventy-seven children (3-15 years) who had undergone a low dose CT with 140 or 100 kV were enrolled. The radiation dose was 1.40 mGy for the 3-8 years old and 2.61 mGy for 9-15 years old children. There were 116 pedicle screws evaluated. The raw data were reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) at 50% strength, ASIR-V with MAR (AV-MAR), deep learning image reconstruction (DLIR) at high strength and DLIR with MAR (DL-MAR). The image quality concerning pedicle screws was evaluated objectively in terms of the length of beam-hardening artifact (LHA) and artifact index (AI), and subjectively using a 4-point scale (4 points: best, 3 points: acceptable). Both AV-MAR and DL-MAR significantly reduced metal-induced beam hardening artifacts with smaller LHA (15.76±10.12 mm, a reduction of 57.24% and 15.66±10.49 mm, a reduction of 57.40%, respectively), and AI value (62.50±33.51, a reduction of 64.65% and 61.03±32.61, a reduction of 65.01%, respectively) compared to ASIR-V and DLIR (all P<0.01), The subjective image quality scores concerning the screws were 3.37±0.49 and 3.47±0.50 with AV-MAR and DL-MAR, respectively, higher than the respective value of 1.73±0.44 and 1.76±0.43 without MAR (all P<0.01). MAR significantly reduces the low-density artifacts caused by metal screws in post-surgery pediatric low-dose spine CT images, across different tube voltages, radiation dose levels and reconstruction algorithms. Combining DL-MAR further improves the overall image quality under low radiation dose conditions.

A Deep-Learning-Based Method for Correction of Bone-Induced CT Beam-Hardening Artifacts

The x-ray attenuation coefficients generally decrease as the x-ray energy increases, which leads to beam hardening artifacts in CT. Due to the difference of dependence of the attenuation coefficients on energy for soft tissue and bone in human body, a simple water precorrection procedure was unable to correct the bone-induced artifacts. Conventional empirical beam hardening correction (EBHC) method reply on empirical image segmentation and data combination processes and may not be able to fully correct the artifacts. We developed a physics-driven deep learning-based method, which followed the workflow of the EBHC method, but replaced the empirical components of the EBHC method with neural networks. Numerical experiments were performed to validate the proposed method and benchmark its performance with the EBHC method and the end-to-end training strategies based on two popular neural networks, i.e., U-net and RED-CNN. Results demonstrate that the proposed method achieved the best performance in both qualitative and quantitative aspects.

Technical performance of a dual-energy CT system with a novel deep-learning based reconstruction process: Evaluation using an abdomen protocol.

A new tube voltage-switching dual-energy (DE) CT system using a novel deep-learning based reconstruction process has been introduced. Characterizing the performance of this DE approach can help demonstrate its benefits and potential drawbacks. To evaluate the technical performance of a novel DECT system and compare it to that of standard single-kV CT and a rotate/rotate DECT, for abdominal imaging. DE and single-kV images of four different phantoms were acquired on a kV-switching DECT system, and on a rotate/rotate DECT. The dose for the acquisitions of each phantom was set to that selected for the kV-switching DE mode by the automatic tube current modulation (ATCM) at manufacturer-recommended settings. The dose that the ATCM would have selected in single-kV mode was also recorded. Virtual monochromatic images (VMIs) from 40 to 130keV, as well as iodine maps, were reconstructed from the DE data. Single-kV images, acquired at 120kV, were reconstructed using body hybrid iterative reconstruction. All reconstructions were made at 0.5mm section thickness. Task transfer functions (TTFs) were determined for a Teflon and LDPE rod. Noise magnitude (SD), and noise power spectrum (NPS) were calculated using 240 and 320mm diameter water phantoms. Iodine quantification accuracy and contrast-to-noise ratios (CNRs) relative to water for 2, 5, 10, and 15mg I/ml were determined using a multi-energy CT (MECT) phantom. Low-contrast visibility was determined and the presence of beam-hardening artifacts and inhomogeneities were evaluated. The TTFs of the kV-switching DE VMIs were higher than that of the single-kV images for Teflon (20% TTF: 6.8 lp/cm at 40keV, 6.2 lp/cm for single-kV), while for LDPE the DE TTFs at 70keV and above were equivalent or higher than the single-kV TTF. All TTFs of the kV-switching DECT were higher than for the rotate/rotate DECT. The SD was lowest in the 70keV VMI (12.0 HU), which was lower than that of single-kV (18.3 HU). The average NPS frequency varied between 2.3 lp/cm and 4.2 lp/cm for the kV-switching VMIs and was 2.2 lp/cm for single-kV. The error in iodine quantification was at maximum 1mg I/ml (at 5mg I/ml). The highest CNR for all iodine concentrations was at 60keV, 2.5 times higher than the CNR for single-kV. At 70-90keV, the number of visible low contrast objects was comparable to that in single-kV, while other VMIs showed fewer objects. At manufacturer-recommended ATCM settings, the CTDIvol for the DE acquisitions of the water and MECT phantoms were 12.6 and 15.4 mGy, respectively, and higher than that for single-kV. The 70keV VMI had less severe beam hardening artifacts than single-kV images. Hyper- and hypo-dense blotches may appear in VMIs when object attenuation exceeds manufacturer recommended limits. At manufacturer-recommended ATCM settings for abdominal imaging, this DE implementation results in higher CTDIvol compared to single-kV acquisitions. However, it can create sharper, lower noise VMIs with up to 2.5 times higher iodine CNR compared to single-kV images acquired at the same dose.

Efficacy of low-dose cone beam computed tomography and metal artifact reduction tool for assessment of peri-implant bone defects: an in vitro study

BackgroundEarly accurate radiographic assessment of peri-implant bone condition is highly important to avoid excessive loss of supporting bone and implant failure. Cone beam computed tomography (CBCT) is the radiographic technique of choice if peri-implant dehiscence and fenestration defects are suspected. The higher radiation dose and the presence of beam hardening artifacts are the main drawbacks of CBCT imaging techniques. This study aims to evaluate the influence of low-dose cone beam computed tomography (LD-CBCT) and metal artifact reduction (MAR) tool on the assessment of peri-implant dehiscence and fenestration.MethodologyThirty titanium implants were inserted into bovine rib blocks. Twenty had standardized bone defects (10 with dehiscence and 10 with fenestration), while the remaining 10 were used as control group with no defects. Radiographic examinations held with high‐definition CBCT (HD-CBCT) and LD-CBCT with and without application of MAR tool. Images were assessed by four examiners for the presence or absence of peri-implant defects. The area under the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy were calculated for all radiographic protocols.ResultsIn the absence of MAR tool, there was no difference in AUC and diagnostic values between LD-CBCT and HD-CBCT for detection of both defects. When the MAR tool was applied, the AUC values, sensitivity, and accuracy were higher in HD-CBCT than in LD-CBCT for the detection of both defects, especially for the dehiscence, while specificity remained the same.ConclusionLD-CBCT can be used in the evaluation of peri-implant dehiscence and fenestration without any decrease in diagnostic accuracy. The application of MAR tool decrease the diagnostic ability of both defects, especially for the detection of dehiscence defects.

Accurate Image Reconstruction in Dual-Energy CT with Limited-Angular-Range Data Using a Two-Step Method.

Dual-energy CT (DECT) with scans over limited-angular ranges (LARs) may allow reductions in scan time and radiation dose and avoidance of possible collision between the moving parts of a scanner and the imaged object. The beam-hardening (BH) and LAR effects are two sources of image artifacts in DECT with LAR data. In this work, we investigate a two-step method to correct for both BH and LAR artifacts in order to yield accurate image reconstruction in DECT with LAR data. From low- and high-kVp LAR data in DECT, we first use a data-domain decomposition (DDD) algorithm to obtain LAR basis data with the non-linear BH effect corrected for. We then develop and tailor a directional-total-variation (DTV) algorithm to reconstruct from the LAR basis data obtained basis images with the LAR effect compensated for. Finally, using the basis images reconstructed, we create virtual monochromatic images (VMIs), and estimate physical quantities such as iodine concentrations and effective atomic numbers within the object imaged. We conduct numerical studies using two digital phantoms of different complexity levels and types of structures. LAR data of low- and high-kVp are generated from the phantoms over both single-arc (SA) and two-orthogonal-arc (TOA) LARs ranging from 14∘ to 180∘. Visual inspection and quantitative assessment of VMIs obtained reveal that the two-step method proposed can yield VMIs in which both BH and LAR artifacts are reduced, and estimation accuracy of physical quantities is improved. In addition, concerning SA and TOA scans with the same total LAR, the latter is shown to yield more accurate images and physical quantity estimations than the former. We investigate a two-step method that combines the DDD and DTV algorithms to correct for both BH and LAR artifacts in image reconstruction, yielding accurate VMIs and estimations of physical quantities, from low- and high-kVp LAR data in DECT. The results and knowledge acquired in the work on accurate image reconstruction in LAR DECT may give rise to further understanding and insights into the practical design of LAR scan configurations and reconstruction procedures for DECT applications.

Dedicated breast computed-tomography in women with a personal history of breast cancer: A proof-of-concept study

PurposeTo compare the subjective image quality assessment using B-CT and digital mammography in women with personal history of breast cancer (PHBC). MethodIn this retrospective study 32 patients with PHBC were included. Each patient had undergone a B-CT examination and a previous mammogram in a time interval of less than 18 months between the two examinations. Two radiologists evaluated the two examinations independently with regard to the presence of lesions, BI-RADS classification, level of confidence for the overall exam interpretation, scar evaluation and image quality including image degradation due to clip artifacts. Level of confidence and image quality were assessed using a 5-point Likert scale. A p-value of less than 0.01 was considered statistically significant. ResultsThirty-seven operated and 27 non-operated breasts were included. Confidence for the overall interpretation with B-CT was equal or superior to mammography in 63 cases (98.4 %) for reader 1 and in 58 cases (90.6 %) for reader 2 (p <.001). Confidence for scar evaluation with B-CT was equal or superior to mammography in all cases for reader 1 and in 34 cases (91.9 %) for readers 2 (p <.001). One case with local recurrence in B-CT was identified by both readers and no false positive findings were reported. A moderate to high image degradation due to beam-hardening artifacts has been reported by both readers in 29.4 % of cases due to surgical clips in the B-CT volume. ConclusionsB-CT in patients with PHBC provides high quality images that can be evaluated with confidence equal or superior to mammography.

A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials

Many methods have been proposed for multienergy computed tomography (CT) imaging based on traditional CT systems. Usually, either prior knowledge of the X-ray spectra distribution or materials or the segmentation of the projection or reconstructed image is needed. To avoid these requirements, a multienergy CT method is proposed in this paper. A CT image can be seen as a linear combination of energy-dependent components and spatially dependent components. The latter components are the base images, while the former components are the coefficients. A blind decomposition model is constructed to decompose the multivoltage projections to obtain the base images and the energies. Multienergy CT images are computationally synthesized with the base images and the energies. Multivoltage projections can be acquired based on one scan with stepped voltages. X-ray scattering is considered an important factor in imaging errors and appears as a low-frequency signal. The variance is used to describe the low-frequency features and is minimized as the optimized objective function of the decomposition model. The solution of the model uses Karush–Kuhn–Tucker (KKT) conditions. In the experiments, the images reconstructed with the proposed method exhibit weak beam-hardening artifacts. Additionally, the X-ray energies of the different materials represented have small relative errors. Therefore, the reconstructed images have narrow energy intervals. This shows the effectiveness of the proposed method.

CBCT artefact-burden of zirconia-based as compared to titanium implants for different beam energies: an analytical approach

Beam hardening artefacts induced by highly-dense material (e.g. metal) is a common quality issue in maxillofacial Cone Beam Computed Tomography (CBCT-) images. This experimental and analytical study investigated attenuation patterns of two typical dental implant materials: zirconia-ceramic and pure titanium. By application of different x-ray beam energies (60, 70, 80, 90 [kVp]) energy-dependent attenuation of these materials is assessed and the resulting artefact induction in the resulting CBCT-images evaluated. A zirconia (Y-TZP-) implant (varnothing: 4.1 mm) and a pure titanium rod (varnothing: 4.0 mm) were exposed in a commercial CBCT (3D Accuitomo 170). The raw two-dimensional (2D) projection radiographs the CBCT utilizes for three-dimensional reconstruction applied for acquisition of attenuation profiles through the circular central slice of the implant-phantom images. Distances the x-rays traverse through the implant-phantoms at this location were computed. Using this information and the linear attenuation coefficient, transmission and attenuation was computed for each material and beam energy. These data were related to beam hardening artefacts that were assessed in the axial reconstructions of the implants’ CBCT images. Transmission of titanium for all peak kilovoltages (kVp) was higher and approximately 200% that of Y-TZP at 60 kVp versus 530% at 90 kVp. At 4 mm diameter transmission for Y-TZP was only approximately 5 % for all four beam-energies. In agreement with this finding, beam hardening artefacts for Y-TZP could not be reduced using higher energies, whereas for titanium they decreased with increasing energy. For the energy spectrum used in this study (60–90 kVp), beam hardening caused by titanium can be reduced using higher energies while this is not the case for zirconia-ceramic (Y-TZP).

Non-convex optimization based optimal bone correction for various beam-hardening artifacts in CT imaging.

Tube of X-ray computed tomography (CT) system emitting a polychromatic spectrum of photons leads to beam hardening artifacts such as cupping and streaks, while the metal implants in the imaged object results in metal artifacts in the reconstructed images. The simultaneous emergence of various beam-hardening artifacts degrades the diagnostic accuracy of CT images in clinics. Thus, it should be deeply investigated for suppressing such artifacts. In this study, data consistency condition is exploited to construct an objective function. Non-convex optimization algorithm is employed to solve the optimal scaling factors. Finally, an optimal bone correction is acquired to simultaneously correct for cupping, streaks and metal artifacts. Experimental result acquired by a realistic computer simulation demonstrates that the proposed method can adaptively determine the optimal scaling factors, and then correct for various beam-hardening artifacts in the reconstructed CT images. Especially, as compared to the nonlinear least squares before variable substitution, the running time of the new CT image reconstruction algorithm decreases 82.36% and residual error reduces 55.95%. As compared to the nonlinear least squares after variable substitution, the running time of the new algorithm decreases 67.54% with the same residual error.

The long journey to set up artificial intelligence (AI) data for automatic detection and localization of missed mesial-buccal (MB2) canals in endodontically-treated maxillary molars with cone beam computed tomography

<h3>Objective</h3> To review the process and challenges of data preparation and validation for AI-assisted detection of unobturated MB2 canals in endodontically-treated maxillary molar teeth. <h3>Study Design</h3> A retrospective review of existing CBCT scans (n=400) of endodontically treated maxillary molars with clinically confirmed missed MB2 canals was performed. Studies with significant beam hardening artifact and calcified canals limiting visualization of the missed MB2 canals were excluded. The studies were then anonymized and transferred to a HIPAA-compliant server. Segmentation of the canals and roots was done manually along serial axial cross-sections using ITK-SNAP (open-source free software version 3.8.0). Two sets of samples, training and test, were designated. The validation of the test samples was created using an iterative approach. Upon completion of the training, the performance of the AI will be tested on 20 obturated maxillary molar roots utilizing 10 scans without MB2 canals and 10 scans with MB2 canals. <h3>Results</h3> The challenges of data preparation were two-fold: 1) Data selection: Of the 400 scans, 350 studies were excluded due to incomplete clinical information, beam hardening artifacts, or calcification of the MB2 canal. 2) Segmentation: Manual segmentation of the canal was limited by anatomical variations and intermittent calcifications along the length of the canal. The AI training is currently in progress and definitive results will be presented at the AAOMR meeting. <h3>Conclusion</h3> Conducting an AI study is exciting and impactful, yet the limitations due to preparing data and limitation of the segmentation software in localizing MB2 canals require substantial time and patience. <b>Statement of Ethical Review</b> Ethical review was sought and study was exempted from ethical review

Effects of different virtual monoenergetic CT image data on chest wall post-processing "unfolded ribs" and proposal of an algorithm improvement.

To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80keV.

High Speed X-ray Tomography with Submicron Resolution for FA and Reverse Engineering of Packages, PCBs, and 300 mm Wafers

Abstract This article provides an overview of a commercial 3D X-ray system, explaining how it acquires high-resolution images of submicron defects in large intact samples. It presents examples in which the system is used to reveal cracks in thin redistribution layers, voids in organic substrates, and variations in TSV metallization on 300-mm wafers. As the authors explain, each scan can be done in as little as a few minutes regardless of sample size, and the resulting images are clear of the beam hardening artifacts that often cause problems in failure analysis and reverse engineering.

Modeling Flow in Cerebral Aneurysm After Coils Embolization Treatment: A Realistic Patient-Specific Porous Model Approach.

Computational fluid dynamics (CFD) has been used to evaluate the efficiency of endovascular treatment in coiled cerebral aneurysms. The explicit geometry of the coil mass cannot typically be incorporated into CFD simulations since the coil mass cannot be reconstructed from clinical images due to its small size and beam hardening artifacts. The existing methods use imprecise porous medium representations. We propose a new porous model taking into account the porosity heterogeneity of the coils deployed in the aneurysm. The porosity heterogeneity of the coil mass deployed inside two patients' cerebral aneurysm phantoms is first quantified based on 3D X-ray synchrotron images. These images are also used to compute the permeability and the inertial factor arising in porous models. A new homogeneous porous model (porous crowns model), considering the coil's heterogeneity, is proposed to recreate the flow within the coiled aneurysm. Finally, the validity of the model is assessed through comparisons with coil-resolved simulations. The strong porosity gradient of the coil measured close to the aneurysmal wall is well captured by the porous crowns model. The permeability and the inertial factor values involved in this model are closed to the ideal homogeneous porous model leading to a mean velocity in the aneurysmal sac similar as in the coil-resolved model. The porous crowns model allows for an accurate description of the mean flow within the coiled cerebral aneurysm.