Scanner Type Research Articles

Postpartum Stress and Neural Regulation of Emotion among First-Time Mothers.

Early parenting relies on emotion regulation capabilities, as mothers are responsible for regulating both their own emotional state and that of their infant during a time of new parenting-related neural plasticity and potentially increased stress. Previous research highlights the importance of frontal cortical regions in facilitating effective emotion regulation, but few studies have investigated the neural regulation of emotion among postpartum women. The current study employed a functional neuroimaging (fMRI) approach to explore the association between perceived stress, depressive symptoms, and the neural regulation of emotion in first-time mothers. Among 59 postpartum mothers, higher perceived stress during the postpartum period was associated with less self-reported use of cognitive reappraisal in everyday life, and greater use of emotion suppression. While viewing standardized aversive images during the Emotion Regulation Task (ERT), mothers were instructed to experience their natural emotional state (Maintain) or to decrease the intensity of their negative emotion by using cognitive reappraisal (Reappraise). Whole-brain analysis revealed a two-way interaction of perceived stress x condition in the right dorsolateral prefrontal cortex (DLPFC) at p < .05 cluster-wise corrected, controlling for postpartum months and scanner type. Higher levels of perceived stress were associated with heightened right DLPFC activity while engaging in cognitive reappraisal versus naturally responding to negative stimuli. Higher right DLPFC activity during Reappraise versus Maintain was further associated with elevated parenting stress. Findings suggest that stress and everyday reappraisal use is reflected in mothers' neural regulation of emotion and may have important implications for their adaptation to parenthood.

Focal unspecific bone uptake on [18F]-PSMA-1007 PET: a multicenter retrospective evaluation of the distribution, frequency, and quantitative parameters of a potential pitfall in prostate cancer imaging

PurposeImproved logistics and availability led to a rapid increase in the use of [18F]-PSMA-1007 for prostate cancer PET imaging. Initial data suggests increased uptake in benign lesions compared to [68 Ga]-PSMA-11, and clinical observations found increased unspecific bone uptake (UBU). We therefore investigate the frequency and characteristics of UBU in [18F]-PSMA-1007 PET.MethodsWe retrospectively analyzed [18F]-PSMA-1007 PET scans from four centers for the presence of UBU, defined as a focal mild-to-moderate uptake (SUVmax < 10.0) not obviously related to a benign or malignant cause. If present, up to three leading UBUs were quantified (SUVmax), localized, and correlated to clinical parameters, such as age, PSA, injected dose, Gleason score, tumor size (T1–T4), and type of PET scanner (analog vs. digital). Additionally, clinical and imaging follow-up results and therapeutic impact were evaluated.ResultsUBUs were identified in 179 out of 348 patients (51.4%). The most frequent localizations were ribs (57.5%) and pelvis (24.8%). The frequency of UBUs was not associated with PSA, Gleason score, tumor size, age, or the injected [18F]-PSMA-1007 dose. UBUs were significantly more frequent in images obtained with digital PET/CT scans (n = 74, 82%) than analog PET/CT scans (n = 221, 40.3%) (p = .0001) but not in digital PET/MR (n = 53, 51%) (p = .1599). In 80 out of 179 patients (44.7%), the interpretation of UBUs was critical for therapeutic management and therefore considered clinically relevant. For 65 UBUs, follow-ups were available: three biopsies, three radiotherapies with PSA follow-up, and 59 cases with imaging. After follow-up, UBUs were still considered unclear in 28 of 65 patients (43%), benign in 28 (43%), and malignant in nine (14%) patients.ConclusionUBUs occur in two-thirds of patients imaged with [18F]-PSMA-1007 PET/CT and are significantly more frequent on digital PET scanners than analog scanners. UBUs should be interpreted carefully to avoid over-staging.

Longitudinal reproducibility of attenuation measurements on virtual unenhanced images: multivendor dual-energy CT evaluation.

The accuracy of virtual unenhanced (VUE) images has been extensively investigated, yet data on their longitudinal reproducibility is limited. The study purpose was to evaluate the longitudinal reproducibility of VUE attenuation measurements on three different dual-energy CT (DECT) scanner types. A total of 137 patients with repeated abdominal DECT either on a rapid kV switching (rsDECT; n = 46), a dual-layer detector (dlDECT; n = 43), or a dual-source scanner (dsDECT; n = 48) were retrospectively included. Attenuation was measured on VUE and corresponding contrast-enhanced images in the liver, spleen, kidneys, aorta, portal vein, and fat. Longitudinal reproducibility was evaluated by calculating the absolute inter-scan differences (HU) and the inter-scan variation (%). Measurement pairs with differences ≤ 10 HU were considered reproducible. Influence of contrast-enhanced attenuation on VUE reproducibility was analyzed using linear regression. The scanner-specific cohorts showed similar age (p-range: 0.35-0.99), sex (p-range: 0.68-1), body weight (p-range: 0.26-0.87), body diameter (p-range: 0.34-0.76), and inter-scan time (p-range: 0.52-0.83). In total, 94.9% of VUE measurements were reproducible for rsDECT, 93.8% for dlDECT, and 90.6% for dsDECT. Overall inter-scan variation was lowest in fat (4.0 (1.7-8.2)%) and highest in tissues with high contrast enhancement: the aorta (13.3 (4.6-21.3)%), portal vein (10.8 (5.7-19.8)%), and kidneys (10.7 (3.9-18.0)%). Significant differences in inter-scan variation were found between the scanner types for the aorta, portal vein, kidneys, and spleen. Inter-scan differences in contrast-enhanced attenuation significantly influenced inter-scan differences in VUE attenuation (p < 0.001; t-ratio: 4.34). Longitudinal reproducibility of VUE attenuation was high for all scanners, yet inter-scan variation of VUE attenuation was influenced by contrast enhancement, showing greatest magnitude and discrepancy between scanner types in vessels and the kidneys. • We found that 94.9% of attenuation measurements on virtual unenhanced images were reproducible for rapid kV switching DECT, 93.8% for dual-layer detector DECT, and 90.6% for dual-source DECT. • Inter-scan variation of attenuationin virtual unenhanced images was comparable between the three scanner types in the liver and fat, whereas inter-scan variation in the spleen, kidneys, portal vein,and aorta showed significant differences between scanner types (p < 0.05). • Inter-scan attenuation differences in contrast-enhanced images significantly influenced inter-scan differences in virtual unenhanced attenuation (p < 0.001, t-ratio: 4.34), suggesting a residual impact of contrast enhancement differences between examinations.

Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models.

The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results (r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems.

Structural volume and cortical thickness differences between males and females in cognitively normal, cognitively impaired and Alzheimer’s dementia population

We investigated differences due to sex in brain structural volume and cortical thickness in older cognitively normal (N=742), cognitively impaired (MCI; N=540) and Alzheimer’s Dementia (AD; N=402) individuals from the ADNI and AIBL datasets (861 Males and 823 Females). General linear models were used to control the effect of relevant covariates including age, intracranial volume, magnetic resonance imaging (MRI) scanner field strength and scanner types. Significant volumetric differences due to sex were observed within different cortical and subcortical regions of the cognitively normal group. The number of significantly different regions was reduced in the MCI group, and no region remained different in the AD group. Cortical thickness was overall thinner in males than females in the cognitively normal group, and likewise, the differences due to sex were reduced in the MCI and AD groups. These findings were sustained after including cerebrospinal fluid (CSF) Tau and phosphorylated tau (pTau) as additional covariates.

\u201cReal-world\u201d radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues

BackgroundMost MRI radiomics studies to date, even multi-centre ones, have used “pure” datasets deliberately accrued from single-vendor, single-field-strength scanners. This does not reflect aspirations for the ultimate generalisability of AI models. We therefore investigated the development of a radiomics signature from heterogeneous data originating on six different imaging platforms, for a breast cancer exemplar, in order to provide input into future discussions of the viability of radiomics in “real-world” scenarios where image data are not controlled by specific trial protocols but reflective of routine clinical practice.MethodsOne hundred fifty-six patients with pathologically proven breast cancer underwent multi-contrast MRI prior to neoadjuvant chemotherapy and/or surgery. From these, 92 patients were identified for whom T2-weighted, diffusion-weighted and contrast-enhanced T1-weighted sequences were available, as well as key clinicopathological variables. Regions-of-interest were drawn on the above image types and, from these, semantic and calculated radiomics features were derived. Classification models using a variety of methods, both with and without recursive feature elimination, were developed to predict pathological nodal status. Separately, we applied the same methods to analyse the information carried by the radiomic features regarding the originating scanner type and field strength. Repeated, ten-fold cross-validation was employed to verify the results. In parallel work, survival modelling was performed using random survival forests.ResultsPrediction of nodal status yielded mean cross-validated AUC values of 0.735 ± 0.15 (SD) for clinical variables alone, 0.673 ± 0.16 (SD) for radiomic features only, and 0.764 ± 0.16 (SD) for radiomics and clinical features together. Prediction of scanner platform from the radiomics features yielded extremely high values of AUC between 0.91 and 1 for the different classes examined indicating the presence of confounding features for the nodal status classification task. Survival analysis, gave out-of-bag prediction errors of 19.3% (clinical features only), 36.9–51.8% (radiomic features from different combinations of image contrasts), and 26.7–35.6% (clinical plus radiomics features).ConclusionsRadiomic classification models whose predictive ability was consistent with previous single-vendor, single-field strength studies have been obtained from multi-vendor, multi-field-strength data, despite clear confounding information being present. However, our sample size was too small to obtain useful survival modelling results.

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Simple SummaryRecent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect.Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.

The influence of digital PET/CT on diagnostic certainty and interrater reliability in [68Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer

ObjectiveTo investigate the impact of digital PET/CT on diagnostic certainty, patient-based sensitivity and interrater reliability.MethodsFour physicians retrospectively evaluated two matched cohorts of patients undergoing [68Ga]Ga-PSMA-11 PET/CT on a digital (dPET/CT n = 65) or an analogue scanner (aPET/CT n = 65) for recurrent prostate cancer between 11/2018 and 03/2019. The number of equivocal and pathological lesions as well as the frequency of discrepant findings and the interrater reliability for the two scanners were compared.ResultsdPET/CT detected more lesions than aPET/CT (p < 0.001). A higher number of pathological scans were observed for dPET/CT (83% vs. 57%, p < 0.001). The true-positive rate at follow-up was 100% for dPET/CT compared to 84% for aPET/CT (p < 0.001). The proportion of lesions rated as non-pathological as a total of all PSMA-avid lesions detected for dPET/CT was comparable to aPET/CT (61.8% vs. 57.0%, p = 0.99). Neither a higher rate of diagnostically uncertain lesions (11.5% dPET/CT vs. 13.7% aPET/CT, p = 0.95) nor discrepant scans (where one or more readers differed in opinion as to whether the scan is pathological) were observed (18% dPET/CT vs. 17% aPET/CT, p = 0.76). Interrater reliability for pathological lesions was excellent for both scanner types (Cronbach’s α = 0.923 dPET/CT; α = 0.948 aPET/CT) and interrater agreement was substantial for dPET/CT (Krippendorf’s α = 0.701) and almost perfect in aPET/CT (α = 0.802).ConclusionsA higher detection rate for pathological lesions for dPET/CT compared with aPET/CT in multiple readers was observed. This improved sensitivity was coupled with an improved true-positive rate and was not associated with increased diagnostic uncertainty, rate of non-specific lesions, or reduced interrater reliability.Key Points• New generation digital scanners detect more cancer lesions in men with prostate cancer.• When using digital scanners, the doctors are able to diagnose prostate cancer lesions with better certainty• When using digital scanners, the doctors do not disagree with each other more than with other scanner types.

A graph neural network framework for causal inference in brain networks

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Evaluation of the Trueness of Digital Implant Impressions According to the Implant Scan Body Orientation and Scanning Method

This study investigated the trueness of a digital implant impression according to the orientation of the implant scan body (ISB) and the scanning method. With the flat surface of the ISB facing either the buccal or proximal direction, the ISB was scanned using one tabletop scanner (T500) and three types of intraoral scanner (TRIOS 3, CS3600, and i500). The effects of differences in the scanning method and ISB orientation were assessed. Postalignment data were subsequently obtained with the abutments generated using a digital library, and superimposed with reference data using a best-fit algorithm, followed by root-mean-square error (RMSE) analysis. The RMSE was lower in the buccal groups (28.15 ± 8.87 μm, mean ± SD) than in the proximal groups (31.94 ± 8.95 μm, p = 0.031), and lower in the full-scan groups (27.92 ± 10.80 μm) than in the partial-scan groups (32.16 ± 6.35 μm, p = 0.016). When using the tabletop scanner, the trueness was higher when the ISB was connected buccally (14.34 ± 0.89 μm) than when it was connected proximally (29.35 ± 1.15 μm, p &lt; 0.001). From the findings of this study it can be concluded that the operator should connect the ISB so that its flat surface faces the buccal direction, and attempt to scan all areas. Additionally, it is advantageous to connect an ISB buccally when using a tabletop scanner.

Full-arch accuracy of five intraoral scanners: In vivo analysis of trueness and precision

ObjectiveTo evaluate the trueness and precision of full-arch scans acquired using five intraoral scanners and investigate the factors associated with the dimensional accuracy of the intraoral scan data.MethodsNine adult participants (mean age, 34.3 ± 8.3 years) were recruited. Four zirconium spheres (Ø 6 mm) were bonded to the canines and the molars. Following acquisition of reference scans using an industrial-grade scanner, five intraoral scanners, namely i500, CS3600, Trios 3, iTero, and CEREC Omnicam, were used to scan the arches. Linear distances between the four reference spheres were automatically calculated, and linear mixed model analysis was performed to compare the trueness and precision of the intraoral scan data among the different scanners.ResultsThe absolute mean trueness and precision values for all intraoral scanners were 76.6 ± 79.3 and 56.6 ± 52.4 µm, respectively. The type of scanner and the measured linear distances had significant effects on the accuracy of the intraoral scan data. With regard to trueness, errors in the intermolar dimension and the distance from the canine to the contralateral molar were greater with Omnicam than with the other scanners. With regard to precision, the error in the linear distance from the canine to the molar in the same quadrant was greater with Omnicam and CS3600 than with the other scanners.ConclusionsThe dimensional accuracy of intraoral scan data may differ significantly according to the type of scanner, with the amount of error in terms of trueness being clinically significant.

White matter integrity after cannabidiol administration for treatment resistant epilepsy

ObjectiveThe effects of individual cannabinoids on white matter integrity are unclear. Human studies have shown white matter maturation alterations in regular recreational cannabis users with the magnitude of these effects dependent on the age of exposure. However, studies have yet to determine which phytocannabinoids are most responsible for these changes. In the current study, we analyzed the effects of pharmaceutical grade cannabidiol oral solution (CBD; Epidiolex® in the U.S.; Epidyolex® in the EU; 100 mg/mL oral solution) on white matter integrity using diffusion MRI in patients with treatment resistant epilepsy (TRE). Methods15 patients with TRE underwent 3 T diffusion MRI prior to receiving CBD and then again approximately 12 weeks later while on a stable dose of CBD for at least two weeks. DTI analyzes were conducted using DSI Studio and tract-based spatial statistics (TBSS). ResultsDTI analysis using DSI Studio showed significant increases in fractional anisotropy (FA) in the right medial lemniscus (p = 0.03), right superior cerebellar peduncle (p = 0.03) and the pontine crossing tract (p = 0.04); decreased mean diffusivity (MD) in the left uncinate fasciculus (p = 0.02) and the middle cerebellar peduncle (p = 0.04); decreased axial diffusivity (AD) in the left superior cerebellar peduncle (p = 0.05), right anterior limb of the internal capsule (p = 0.03), and right posterior limb of the internal capsule (p = 0.02); and decreased radial diffusivity (RD) in the middle cerebellar peduncle (p = 0.03) and left uncinate fasiculus (p = 0.01). The follow-up ANCOVA also yielded significant results when controlling for covariates of CBD dosage, age, sex, change in seizure frequency, and scanner type: FA increased in the pontine crossing tract (p = 0.03); RD decreased in the middle cerebellar peduncle (p = 0.04) and left uncinate fasciculus (p = 0.04). Subsequent TBSS analysis controlling for the same variables yielded no significant white matter differences between groups. ConclusionThese findings indicate relatively minor short-term effects of highly-purified plant-derived CBD on white matter structural integrity in patients with TRE.

Abstract PO-033: The impact of the variation of CT scanner on the prediction of HPV status in head &amp; neck cancer patients

Abstract Radiomics involves the use of high-dimensional quantitative imaging features for predictive purposes. However, studies showed that these radiomic features are sensitive to the variability of imaging parameters (e.g., scanner model). One of the major challenges in radiomics lies in improving the robustness of quantitative features against the variation in the imaging dataset in multi-center studies. Here, we assess the impact of scanner choice on the computed tomography (CT)-derived radiomic features to predict association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV), which has a well-established impact on CT-derived radiomic features. This experiment was performed on CT image datasets acquired with two different scanner types. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments revealed the effect of scanner type on the robustness of the radiomic features, and the extent of this dependency is reflected on the performance of HPV prediction models. The result of this study highlighted the importance of implementing an appropriate approach to reduce the impact of the imaging domain radiomic features and consequently on the machine learning models. Citation Format: Reza Reiazi, Collin Arrowsmith, Farnoosh Abbas-Aghababazadeh, Christopher Eles, Aria Rezaie, Scott V. Bratman, Andrew J. Hope, Benjamin Haibe-Kains. The impact of the variation of CT scanner on the prediction of HPV status in head &amp; neck cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-033.

Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

PurposeTo develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results.MethodsOne hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features.ResultsPSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods.ConclusionThe WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

In Vitro Comparison of Marginal and Internal Fit of Zirconia Copings Fabricated by One CAD/CAM System with Two Different Scanners.

Objectives: Marginal and internal fit of restorations are two important clinical factors for assessing the quality and durability of computer-aided design/computer-aided manufacturing (CAD/CAM)-fabricated monolithic zirconia restorations. The purpose of this study was to evaluate the marginal and internal fit of CAD/CAM zirconia crowns with two different scanners (i3D scanner and 3Shape D700). Materials and Methods: Twelve extracted sound human posterior teeth were prepared for full zirconia crowns. Two different extraoral scanners namely i3D scanner and 3Shape D700 were used to digitize type IV gypsum casts poured from impressions. The crowns were milled from presintered monolithic zirconia blocks by a 5-axis milling machine. The replica technique and MIP4 microscopic image analysis software were utilized to measure the marginal and internal fit by a stereomicroscope at ×40 magnification. The collected data were analyzed by paired t-test. Results: The mean marginal gap was 203.62 μm with 3Shape D700 scanner and 241.07 μm with i3D scanner. The mean internal gap was 192.30 μm with 3Shape D700 scanner and 196.06 μm with i3D scanner. The results of paired t-test indicated that there was a statistically significant difference between the two scanners in marginal fit (P=0.04); while, there was no statistically significant difference in internal fit (P=0.761). Conclusion: Within the limitations of this study, the results showed that type of extraoral scanner affected the marginal fit of CAD/CAM fabricated crowns; however, it did not have a significant effect on their internal fit.

Aircraft skin gap and flush measurement based on seam region extraction from 3D point cloud

Gap and flush measurement is a challenging task in the process of aircraft assembly, which significantly affects the reliability of aircraft aerodynamic shape. To ensure the quality of aircraft assembly, an accurate gap and flush measurement method is essential. This paper presents a novel simple yet effective method, which is suitable for various kinds of scanner data. Given an unstructured scanned point cloud, our method first detects and segments the seam area based on the local point density discrepancy. In the second stage, a projection operation is applied based on the segmentation results to convert 3D measurement to 2D measurement to reduce the computational complexity and improve the accuracy of the measurement. According to the geometric distribution of the projection points generated in stage two, the feature points defined by the gap and flush calculation model are extracted to complete the computation. Experimental results show that the accuracy is comparable to some state-of-the-art methods. Concerning the robustness to different types of scanner devices data, the proposed method clearly outperforms the others.

Accuracy of Implant Position Reproduction According to Exposed Length of the Scan Body during Optical Scanning: An In Vitro Study

Accuracy of implant position reproduction according to various types of scanners, implant placement depths, and tooth positions are unknown. The purpose of this study was to compare the accuracy of implant position reproduction for differences in the exposed length of the implant scan body according to scanner type and tooth position. Implants were placed at the positions of the lateral incisor, first premolar, and first molar in the study model at the bone level and submerged 1.5 mm, 3.0 mm, 4.5 mm, and 6.0 mm. The completed models were scanned with one type of tabletop scanner and three types of intraoral scanners (TRIOS 3, i500, and CS3600). A matching process was performed for all scan data to superimpose abutment library data on the scan body, and the root mean square errors were analyzed in three dimensions to evaluate the position reproducibility of the replaced abutment library. In the trueness analysis, the error increased rapidly for an implant placement depth of 4.5 mm, and was largest for a submersion of 6.0 mm. The precision analysis confirmed that the error increased for depths of at least 3.0 mm. The analysis by position identified that the accuracy was lowest for an implant placed at the position of the lateral incisor. These findings indicate that special care is required when making an impression of a deep implant with an optical scanner.

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

BackgroundAn increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems.ObjectiveThe objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects.MethodsWe trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%.ResultsA mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin).ConclusionsBecause all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols.

MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols.