Defects in Metal Additive Manufacturing: Formation, Process Parameters, Postprocessing, Challenges, Economic Aspects, and Future Research Directions.

Metal additive manufacturing (AM) is a revolutionary technological advancement that has made significant inroads in a wide range of sectors, including aerospace, defense, automotive, health care, and engineering applications. It offers unprecedented design freedom, reduced material waste, and enhanced performance, in addition to significant enhancements to fabrication processes. Microstructural defects and internal stresses formed during fabrication directly affect the fabricated product's surface integrity, quality, and service life. Identification, characterization, and prediction of these defects help significant and direct production of defect-free structures with high density. This article provides detailed insights concerning the common defects, mitigation techniques, and challenges reported in both powder bed fusion-based and wire arc AM methods. Defects such as porosity may develop due to the powder sphericity, roughness of the powder, preheating, process parameters, build environment, postprocessing techniques, and environmental factors. Therefore, a critical study of the techniques, alloys, process parameter optimization, and different postprocessing techniques to tone down the defects is made from their formations.

Exploration of Fractional Flow Reservation Score Based on Artificial Intelligence Post-processing for Coronary Artery Lesions in Patients with Diabetes and Coronary Heart Disease.

In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reservation (FFR) in patients with different levels of CHD and diabetes, this paper used AI (artificial intelligence) post-processing technology to detect CHD and FFR. In this paper, 94 patients suspected of CHD who underwent coronary arteriography (CAG) in a hospital between December 2022 and February 2023 were examined by coronary computed tomography angiography (CCTA) and FFR. Based on CCTA, AI software is used to process CCTA images, diagnose coronary plaques, coronary stenosis, corresponding stenosis of different types of plaques, and FFR values. The diagnostic performance of AI was evaluated using expert diagnosis, CAG diagnosis, and FFR examination results as the "gold standard". According to the diagnosis results, the relationship between FFR and CHD patients with diabetes at different levels was studied. The research results showed that AI image diagnosis has high sensitivity, specificity, and accuracy, and has good diagnostic effects on coronary plaques, coronary stenosis, stenosis corresponding to different types of plaques, and FFR values. The fasting blood glucose levels and FFR values of three groups of CHD patients were statistically significant, and correlation analysis revealed a negative correlation between the two. Using AI for CCTA diagnosis can efficiently, conveniently, and accurately obtain the required data, improving clinical diagnostic efficiency and accuracy. The analysis of AI recognition results found that in patients with CHD, the FFR value of patients with diabetes decreased, and the FFR value was negatively correlated with the fasting blood glucose concentration, indicating that CHD patients may lead to myocardial ischemia in the blood supply area due to the decline of their coronary blood flow reserve. CHD patients with diabetes are very common. It is known that high blood sugar can cause coronary artery damage. Many CHD patients with diabetes have complex angiopathy, so the advantages and disadvantages of stent placement should be carefully considered in clinical practice. CAG is currently the most commonly used examination method in clinical practice, and is considered the "gold standard" for imaging evaluation and diagnosis of CHD. FFR evaluates the blood flow status in the coronary artery by measuring the pressure inside the coronary artery, and determines whether it has changed based on its changes (i.e. functional assessment). Diabetes is closely related to CHD and is a risk factor of CHD. The range of vascular lesions in diabetes patients is very wide, which can involve capillaries to large arteries, thereby damaging their microvascular function [1]. McKenzie-Sampson Safyer studied whether gestational diabetes would increase the risk of cardiovascular disease after more than 20 years [2]. Piche Marie-Eve discussed the interaction between obesity type 2 diabetes and cardiovascular disease [3]. Li Jing studied the relationship between pregnancy diabetes and long-term risk of cardiovascular disease [4]. Petrie John R discussed the pathophysiological characteristics of common diseases of diabetes and hypertension and related vascular complications [5]. Kemps Hareld proposed a physical exercise program suitable for type 2 diabetes with cardiovascular disease by analyzing the clinical characteristics of type 2 diabetes with cardiovascular disease [6]. Sattar Naveed analyzed the mortality and cardiovascular disease results of patients with or without type 2 diabetes [7]. Using FFR to study diabetes with CHD can help coronary artery remodeling and provide new ideas and methods for clinical treatment. Coronary artery CCTA is the preferred examination method for screening and diagnosis of CHD. However, the large number of coronary artery CCTA images requires doctors to perform post-processing, which brings a lot of workload to doctors. Doctors are prone to visual fatigue, and it can also lead to doctors missing out on coronary artery stenosis (CAS) segments and misevaluating the degree of stenosis [8]. AI technology have advantages such as speed, efficiency, repeatability, quantification, and low cost. The use of AI technology can appropriately reduce the workload of doctors in medical imaging diagnosis, which helps drive doctors to improve workflow and reduce the probability of errors [9]. von Knebel Doeberitz Philipp L studied the diagnostic efficacy of combining plaque markers generated by CCTA with deep learning (DL) based blood reserve scores [10]. Zhou Zhen further reduced the contrast agent dose for whole aortic CT angiography imaging using the enhanced period consistent adversarial framework algorithm [11]. AI can be used to measure CAS, plaque and FFR on CCTA images. This paper used AI to process CCTA images to assist in the study of diabetes with CHD. This article selected 94 patients with suspected CHD and uses AI for CCTA image analysis. The expert diagnosis results were used as the "gold standard" to evaluate the effectiveness of AI in identifying coronary plaques; using the CAG results as the "gold standard", the effectiveness of AI in identifying CAS was evaluated; using expert diagnosis and CAG results as the "gold standard", the effectiveness of AI in identifying stenosis corresponding to different types of plaques was evaluated; the effectiveness of AI in identifying myocardial ischemia was evaluated using FFR measurement results as the "gold standard". Based on the above diagnostic results, the relationship between FFR and the difference of CHD in diabetes patients with CHD was studied, and the correlation between FFR and the difference of CHD was discussed.

Multiaxial 3D MRI of the Ankle: Advanced High-Resolution Visualization of Ligaments, Tendons, and Articular Cartilage.

MRI is a valuable tool for diagnosing a broad spectrum of acute and chronic ankle disorders, including ligament tears, tendinopathy, and osteochondral lesions. Traditional two-dimensional (2D) MRI provides a high image signal and contrast of anatomic structures for accurately characterizing articular cartilage, bone marrow, synovium, ligaments, tendons, and nerves. However, 2D MRI limitations are thick slices and fixed slice orientations. In clinical practice, 2D MRI is limited to 2 to 3 mm slice thickness, which can cause blurred contours of oblique structures due to volume averaging effects within the image slice. In addition, image plane orientations are fixated and cannot be changed after the scan, resulting in 2D MRI lacking multiplanar and multiaxial reformation abilities for individualized image plane orientations along oblique and curved anatomic structures, such as ankle ligaments and tendons. In contrast, three-dimensional (3D) MRI is a newer, clinically available MRI technique capable of acquiring high-resolution ankle MRI data sets with isotropic voxel size. The inherently high spatial resolution of 3D MRI permits up to five times thinner (0.5 mm) image slices. In addition, 3D MRI can be acquired image voxel with the same edge length in all three space dimensions (isotropism), permitting unrestricted multiplanar and multiaxial image reformation and postprocessing after the MRI scan. Clinical 3D MRI of the ankle with 0.5 to 0.7 mm isotropic voxel size resolves the smallest anatomic ankle structures and abnormalities of ligament and tendon fibers, osteochondral lesions, and nerves. After acquiring the images, operators can align image planes individually along any anatomic structure of interest, such as ligaments and tendons segments. In addition, curved multiplanar image reformations can unfold the entire course of multiaxially curved structures, such as perimalleolar tendons, into one image plane. We recommend adding 3D MRI pulse sequences to traditional 2D MRI protocols to visualize small and curved ankle structures to better advantage. This article provides an overview of the clinical application of 3D MRI of the ankle, compares diagnostic performances of 2D and 3D MRI for diagnosing ankle abnormalities, and illustrates clinical 3D ankle MRI applications.

Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.

PURPOSE: This study evaluates the performance of a kilovoltage x-ray image-guidance system equipped with a novel post-processing optimization algorithm on the newly introduced TAICHI linear accelerator (Linac). METHODS: A comparative study involving image quality tests and radiation dose measurements was conducted across six scanning protocols of the kV-cone beam computed tomography (CBCT) system on the TAICHI Linac. The performance assessment utilized the conventional Feldkamp-Davis-Kress (FDK) algorithm and a novel Non-Local Means denoising and adaptive scattering correction (NLM-ASC) algorithm. Image quality metrics, including spatial resolution, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were evaluated using a Catphan 604 phantom. Radiation doses for low-dose and standard protocols were measured using a computed tomography dose index (CTDI) phantom, with comparative measurements from the Halcyon Linac's iterative CBCT (iCBCT). RESULTS: The NLM-ASC algorithm significantly improved image quality, achieving a 300%-1000% increase in CNR and SNR over the FDK-only images and it also showed a 100%-200% improvement over the iCBCT images from Halcyon's head protocol. The optimized low-dose protocols yielded higher image quality than the standard FDK protocols, indicating potential for reduced radiation exposure. Clinical implementation confirmed the TAICHI system's utility for precise and adaptive radiotherapy. CONCLUSION: The kV-IGRT system on the TAICHI Linac, with its novel post-processing algorithm, demonstrated superior image quality suitable for routine clinical use, effectively reducing image noise without compromising other quality metrics.

Advancing sub-seasonal to seasonal multi-model ensemble precipitation prediction in east asia: Deep learning-based post-processing for improved accuracy.

The growing interest in Subseasonal to Seasonal (S2S) prediction data across different industries underscores its potential use in comprehending weather patterns, extreme conditions, and important sectors such as agriculture and energy management. However, concerns about its accuracy have been raised. Furthermore, enhancing the precision of rainfall predictions remains challenging in S2S forecasts. This study enhanced the sub-seasonal to seasonal (S2S) prediction skills for precipitation amount and occurrence over the East Asian region by employing deep learning-based post-processing techniques. We utilized a modified U-Net architecture that wraps all its convolutional layers with TimeDistributed layers as a deep learning model. For the training datasets, the precipitation prediction data of six S2S climate models and their multi-model ensemble (MME) were constructed, and the daily precipitation occurrence was obtained from the three thresholds values, 0 % of the daily precipitation for no-rain events, <33 % for light-rain, >67 % for heavy-rain. Based on the precipitation amount prediction skills of the six climate models, deep learning-based post-processing outperformed post-processing using multiple linear regression (MLR) in the lead times of weeks 2-4. The prediction accuracy of precipitation occurrence with MLR-based post-processing did not significantly improve, whereas deep learning-based post-processing enhanced the prediction accuracy in the total lead times, demonstrating superiority over MLR. We enhanced the prediction accuracy in forecasting the amount and occurrence of precipitation in individual climate models using deep learning-based post-processing.

Impact of Annealing in Various Atmospheres on Characteristics of Tin-Doped Indium Oxide Layers towards Thermoelectric Applications.

The aim of this work was to investigate the possibility of modifying the physical properties of indium tin oxide (ITO) layers by annealing them in different atmospheres and temperatures. Samples were annealed in vacuum, air, oxygen, nitrogen, carbon dioxide and a mixture of nitrogen with hydrogen (NHM) at temperatures from 200 °C to 400 °C. Annealing impact on the crystal structure, optical, electrical, thermal and thermoelectric properties was examined. It has been found from XRD measurements that for samples annealed in air, nitrogen and NHM at 400 °C, the In2O3/In4Sn3O12 share ratio decreased, resulting in a significant increase of the In4Sn3O12 phase. The annealing at the highest temperature in air and nitrogen resulted in larger grains and the mean grain size increase, while vacuum, NHM and carbon dioxide atmospheres caused the decrease in the mean grain size. The post-processing in vacuum and oxidizing atmospheres effected in a drop in optical bandgap and poor electrical properties. The carbon dioxide seems to be an optimal atmosphere to obtain good TE generator parameters-high ZT. The general conclusion is that annealing in different atmospheres allows for controlled changes in the structure and physical properties of ITO layers.

Influence of various cleaning solutions on the geometry, roughness, gloss, hardness, and flexural strength of 3D-printed zirconia.

This study aimed to investigate the impact of various cleaning solutions on the geometry, roughness, gloss, hardness, and flexural strength of 3D-printed zirconia. Cleaning solutions, including isopropyl alcohol (IPA, 99.9%), ethyl alcohol (EtOH, 99.9%), and tripropylene glycol monomethyl ether (TPM, ≥ 97.5%), were diluted to a concentration of 70% and categorized into six groups: IPA99, EtOH99, TPM97, IPA70, EtOH70, and TPM70. Zirconia discs, printed via digital light processing, were sintered after cleaning. The geometry, roughness, gloss, hardness, and flexural strength were analyzed. Statistical analysis was performed using one-way ANOVA with Tukey's post hoc test (p < 0.05). The thickness of TPM70 was the highest. The diameter of TPM70 was significantly larger than that of EtOH99 and IPA70 (p < 0.05). The weight of the TPM groups was significantly higher than that of IPA70 (p < 0.05). The roughness Ra of TPM70 was significantly greater than that of IPA99, EtOH99, and EtOH70 (p < 0.05). The differences in surface gloss, hardness, and flexural strength among the different groups were not statistically significant (p > 0.05). Different cleaning solutions did not affect the surface gloss, hardness, and flexural strength of 3D-printed zirconia. High and low concentrations of the same cleaning solution did not affect the surface gloss, hardness, and flexural strength. IPA70, TPM97, and EtOH can be considered viable post-printing cleaning alternatives to the traditional gold standard, IPA99.

Continuous High-Precision Positioning in Smartphones by FGO-Based Fusion of GNSS-PPK and PDR.

The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS-PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS-SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS-PPK via FGO.

Wettability Behaviour of Metal Surfaces after Sequential Nanosecond and Picosecond Laser Texturing.

This study examines the wettability behaviour of 304 stainless steel (304SS) and Ti-6Al-4V (Ti64) surfaces after sequential nanosecond (ns) and picosecond (ps) laser texturing; in particular, how the multi-scale surface structures created influence the lifecycle of surface hydrophobicity. The effect of different post-process treatments is also examined. Surfaces were analysed using Scanning Electron Microscopy (SEM), a white light interferometer optical profiler, and Energy Dispersive X-ray (EDX) spectroscopy. Wettability was assessed through sessile drop contact angle (CA) measurements, conducted at regular intervals over periods of up to 12 months, while EDX scans monitored elemental chemical changes. The results show that sequential (ns + ps) laser processing produced multi-scale surface texture with laser-induced periodic surface structures (LIPSS). Compared to the ns laser case, the (ns + ps) laser processed surfaces transitioned more rapidly to a hydrophobic state and maintained this property for much longer, especially when the single post-process treatment was ultrasonic cleaning. Some interesting features in CA development over these extended timescales are revealed. For 304SS, hydrophobicity was reached in 1-2 days, with the CA then remaining in the range of 120 to 140° for up to 180 days; whereas the ns laser-processed surfaces took longer to reach hydrophobicity and only maintained the condition for up to 30 days. Similar results were found for the case of Ti64. The findings show that such multi-scale structured metal surfaces can offer relatively stable hydrophobic properties, the lifetime of which can be extended significantly through the appropriate selection of laser process parameters and post-process treatment. The addition of LIPSS appears to help extend the longevity of the hydrophobic property. In seeking to identify other factors influencing wettability, from our EDX results, we observed a significant and steady rate of increase in the carbon content at the surface over the study period.

Reslice3Dto2D: Introduction of a software tool to reformat 3D volumes into reference 2D slices in cardiovascular magnetic resonance imaging.

OBJECTIVE: Cardiovascular magnetic resonance enables the quantification of functional and morphological parameters with an impact on therapeutical decision making. While quantitative assessment is established in 2D, novel 3D techniques lack a standardized approach. Multi-planar-reformatting functionality in available software relies on visual matching location and often lacks necessary functionalities for further post-processing. Therefore, the easy-to-use Reslice3Dto2D software tool was developed as part of another research project to fill this gap and is now introduced with this work. RESULTS: The Reslice3Dto2D reformats 3D data at the exact location of a reference slice with a two-step-based interpolation in order to reflect in-plane discretization and through-plane slice thickness including a slice profile selection. The tool was successfully validated on an artificial dataset and tested on 119 subjects with different underlying pathologies. The exported reformatted data could be imported into three different post-processing software tools. The quantified image sharpness by the Frequency Domain Image Blur Measure was significantly decreased by around 40% on rectangular slice profiles with 7 mm slice thickness compared to 0 mm due to partial volume effects. Consequently, Reslice3Dto2D enables the quantification of 3D data with conventional post-processing tools as well as the comparison of 3D acquisitions with their established 2D version.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing.

BACKGROUND: The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations. METHODS: Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV. RESULTS: Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing. CONCLUSION: Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

Surface Treatment of Additively Manufactured Polyetheretherketone (PEEK) by Centrifugal Disc Finishing Process: Identification of the Key Parameters.

Polyetheretherketone is a promising material for implants due to its good mechanical properties and excellent biocompatibility. Its accessibility to a wide range of applications is facilitated by the ability to process it with an easy-to-use manufacturing process such as fused filament fabrication. The elimination of disadvantages associated with the manufacturing process, such as a poor surface quality, is a main challenge to deal with. As part of the mass finishing process, centrifugal disc finishing has demonstrated good results in surface optimization, making it a promising candidate for the post-processing of additively manufactured parts. The objective of this study is to identify the key parameters of the centrifugal disc finishing process on the waviness of additively manufactured PEEK specimens, which has not been investigated previously. The waviness of the specimen was investigated by means of confocal laser scanning microscopy (CLSM), while weight loss was additionally tracked. Six parameters were investigated: type, amount and speed of media, use of compound, amount of water and time. Type of media, time and speed were found to significantly influence waviness reduction and weight loss. Surface electron microscopy images demonstrated the additional effects of deburring and corner rounding. Results on previous studies with specimens made of metal showed similar results. Further investigation is required to optimize waviness reduction and polish parts in a second post-processing step.

Characterization of myocardial perfusion imaging systems - an extension of quality metrics.

PURPOSE: The aim of this study was to evaluate the performance of myocardial perfusion imaging (MPI) systems in detecting perfusion defects (PDs). The defect perfusion index (DPI) was introduced to extend and further advance the current MPI quality metrics. METHODS: An anthropomorphic phantom simulating normal and pathological myocardial perfusion conditions was imaged by various NaI-crystal detector systems with and without corrections for scatter (SC) and attenuation (AC) (Symbia, Symbia + SC, Symbia IQ + SCAC, Symbia IQ), and cadmium-zinc-telluride detector systems without corrections (DSPECT, D530c). The extent of PD and the summed score (SS) were obtained by comparing polar maps with ad hoc normal databases created for each MPI system by using phantom polar maps with normal perfusion. The segmental uptake (SU) and the global uniformity (GU) were evaluated. The DPI was calculated on segments included in the PD to minimize attenuation artifacts outside the PD. The 17 segmental model was used. RESULTS: The highest level of uniformity of polar map was obtained for Symbia IQ + SCAC. D530c showed the highest extent of PD and dependence of the extent on the PD position. It showed in general the lowest SU values and the highest GU due to attenuation artifacts. Nevertheless, D530c outperforms other MPI systems in terms of PD detection, showing the highest DPI value. DSPECT system showed the lowest SS value, and DPI values comparable to NaI-crystal detector systems. CONCLUSION: The DPI can be evaluated to investigate the intrinsic ability of MPI systems to detect PDs, whatever the quantitative post-processing software used.

Fit comparison of interim crowns manufactured with open and proprietary 3D printing modes versus milling technology: An in vitro study.

OBJECTIVES: This study aimed to assess the fit of interim crowns produced using DLP-based 3D printing with different manufacturing workflows-open and proprietary-versus milling technology. METHODS: A total of 120 crowns were evaluated using the replica technique. The control group (Mill, n = 30) was manufactured via subtractive technology. Experimental groups were printed using a DLP printer (SprintRay Pro95). In the proprietary mode (SR100, n = 30), manufacturer resin was used with a 100-µm layer thickness (LT) and a splashing cleaning postprocessing. In the open mode, validated resin was used. Group B100 (n = 30) had a 100-µm LT, and group B50 (n = 30) had a 50-µm followed by postprocessing in an ultrasonic bath with full immersion in isopropyl alcohol. Kruskal-Wallis tests with Bonferroni correction was applied after normal analysis (α = 0.05). RESULTS: Group B50 exhibited the best overall fit (123.87 ± 67.42 µm), which was comparable to the gold standard Milling group, which demonstrated the lowest marginal fit (p = 0.760). SR100 showed significantly poorer performance compared to Mill, B50, and B100 (p < 0.001). CONCLUSIONS: 3D printed and milled interim crowns generally demonstrated clinically acceptable fit, with the exception of the SR100 group. Postprocessing notably influenced crown fit, with the open mode with total immersion in isopropyl alcohol being superior. CLINICAL SIGNIFICANCE: The present study demonstrates that the selection of an optimal manufacturing and postprocessing workflow results in superior fit for interim crowns. This enables dental professionals to evaluate protocols and ensure reliable outcomes with improved clinical outcomes in interim crown fabrication.

Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map.

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system.

Clinical characteristics and multimodal imaging can help diagnosing and treating mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy.

OBJECTIVE: Mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) is a recently described, histopathologically and molecularly defined (SLC35A2-mutated) type of cortical malformation. Although increasingly recognized, the diagnosis of MOGHE remains a challenge. We present the characteristics of the first six patients diagnosed in Bulgaria, with the aim to facilitate identification, proper presurgical evaluation, and surgical treatment approach in this disease. METHODS: Revision of histopathological specimens of 202 patients operated on for drug-resistant focal epilepsy identified four cases with MOGHE. Another two were suggested, based on clinical characteristics and subsequently, were histologically confirmed. Sanger SLC35A2 sequencing on paraffin-embedded or fresh-frozen brain tissue was performed. Analysis of seizure types, neuropsychological profiles, electroencephalographic (EEG), imaging features and epilepsy surgery outcomes was done. RESULTS: Three out of the six cases (50%) harbored pathogenic SLC35A2 mutations. One patient had a heterozygous somatic variant with uncertain significance. Clinical characteristics included epilepsy onset in infancy (in 100% under 3 years of age), multiple seizure types, and moderate or severe intellectual/developmental delay. Epileptic spasms with hypsarrhythmia on EEG were the initial seizure type in five patients. The subsequent seizure types resembled those in Lennox-Gastaut syndrome. The majority of the patients (n = 4) presented prominent and persisting autistic features. Magnetic resonance imaging (MRI) showed multilobar (n = 6) and bilateral (n = 3) lesions, affecting the frontal lobes (n = 5; bilaterally in three) and characterized by increased signal on T2/fluid-attenuated inversion recovery (FLAIR). Voxel-based morphometric MRI post-processing and positron emission tomography helped determining the localization and extent of the lesions and presumed epileptogenic zones. After surgery, four patients (66.7%) were seizure-free ≥2 years. Interestingly, all seizure-free patients carried somatic SLC35A2-alterations. SIGNIFICANCE: Epileptic spasms, early prominent neuropsychological disturbances, MRI-T2/FLAIR hyperintense lesions with cortico-subcortical blurring, frequently multilobar and especially frontal, can preoperatively help to suspect MOGHE. Epilepsy surgery is still the only successful treatment option in MOGHE.

Towards a Customizable, SLA 3D-Printed Biliary Stent: Optimizing a Commercially Available Resin and Predicting Stent Behavior with Accurate In Silico Testing.

Inflammation of the bile ducts and surrounding tissues can impede bile flow from the liver into the intestines. If this occurs, a plastic or self-expanding metal (SEM) stent is placed to restore bile drainage. United States (US) Food and Drug Administration (FDA)-approved plastic biliary stents are less expensive than SEMs but have limited patency and can occlude bile flow if placed spanning a duct juncture. Recently, we investigated the effects of variations to post-processing and autoclaving on a commercially available stereolithography (SLA) resin in an effort to produce a suitable material for use in a biliary stent, an FDA Class II medical device. We tested six variations from the manufacturer's recommended post-processing and found that tripling the isopropanol (IPA) wash time to 60 min and reducing the time and temperature of the UV cure to 10 min at 40 °C, followed by a 30 min gravity autoclave cycle, yielded a polymer that was flexible and non-cytotoxic. In turn, we designed and fabricated customizable, SLA 3D-printed polymeric biliary stents that permit bile flow at a duct juncture and can be deployed via catheter. Next, we generated an in silico stent 3-point bend test to predict displacements and peak stresses in the stent designs. We confirmed our simulation accuracy with experimental data from 3-point bend tests on SLA 3D-printed stents. Unfortunately, our 3-point bend test simulation indicates that, when bent to the degree needed for placement via catheter (~30°), the peak stress the stents are predicted to experience would exceed the yield stress of the polymer. Thus, the risk of permanent deformation or damage during placement via catheter to a stent printed and post-processed as we have described would be significant. Moving forward, we will test alternative resins and post-processing parameters that have increased elasticity but would still be compatible with use in a Class II medical device.

PostBP: A Python library to analyze outputs from wildfire growth models.

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.

An Improved Postprocessing Method to Mitigate the Macroscopic Cross-Slice B0 Field Effect on R2* Measurements in the Mouse Brain at 7T.

The MR transverse relaxation rate, R2*, has been widely used to detect iron and myelin content in tissue. However, it is also sensitive to macroscopic B0 inhomogeneities. One approach to correct for the B0 effect is to fit gradient-echo signals with the three-parameter model, a sinc function-weighted monoexponential decay. However, such three-parameter models are subject to increased noise sensitivity. To address this issue, this study presents a two-stage fitting procedure based on the three-parameter model to mitigate the B0 effect and reduce the noise sensitivity of R2* measurement in the mouse brain at 7T. MRI scans were performed on eight healthy mice. The gradient-echo signals were fitted with the two-stage fitting procedure to generate R2corr_t*. The signals were also fitted with the monoexponential and three-parameter models to generate R2nocorr* and R2corr*, respectively. Regions of interest (ROIs), including the corpus callosum, internal capsule, somatosensory cortex, caudo-putamen, thalamus, and lateral ventricle, were selected to evaluate the within-ROI mean and standard deviation (SD) of the R2* measurements. The results showed that the Akaike information criterion of the monoexponential model was significantly reduced by using the three-parameter model in the selected ROIs (p = 0.0039-0.0078). However, the within-ROI SD of R2corr* using the three-parameter model was significantly higher than that of the R2nocorr* in the internal capsule, caudo-putamen, and thalamus regions (p = 0.0039), a consequence partially due to the increased noise sensitivity of the three-parameter model. With the two-stage fitting procedure, the within-ROI SD of R2corr* was significantly reduced by 7.7-30.2% in all ROIs, except for the somatosensory cortex region with a fast in-plane variation of the B0 gradient field (p = 0.0039-0.0078). These results support the utilization of the two-stage fitting procedure to mitigate the B0 effect and reduce noise sensitivity for R2* measurement in the mouse brain.