Region Of Interest Research Articles

Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours’ histopathology for a small ROI was 0.86 (95% CI: 0.75–0.98), and for a large ROI, 0.83 (95% CI: 0.71–0.96). An ADC cut-off value of 1.06 × 10−3 mm2/s was able to distinguish malignant from benign tumours with 83% (68–98%) sensitivity and 75% (95% CI: 54–98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives.

Synergistic use of handcrafted and deep learning features for tomato leaf disease classification.

This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. The system's process encompasses preprocessing, feature extraction, feature fusion, and classification. It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images tomato leaves. These features based arranged in ABCD rule (Asymmetry, Borders, Colors, and Diameter) are integrated with outputs from a Convolutional Neural Network (CNN) pretrained on ImageNet. To address data imbalance, we introduced a novel evaluation method that has shown to improve classification accuracy by 15% compared to traditional methods, achieving an overall accuracy rate of 92% in field tests. By merging classical feature engineering with modern machine learning techniques under mutual information-based feature fusion, our system sets a new standard for precision in agricultural diagnostics. Specific performance metrics showcasing the effectiveness of our approach in automated detection and classifying of tomato leaf disease.

Geophysical and geospatial insights into surface and subsurface characteristics for groundwater potential analysis, Ras Sudr, West Sinai, Egypt

Ras Sudr has garnered significant interest owing to various initiatives aimed at expanding and developing this area. It holds considerable strategic importance, serving as a key development hub in Sinai and a focal point for tourism destination. Moreover, it has a remarkable event with flashfloods which can be utilized for groundwater or direct usage. Integrating geophysical and geospatial analyses to study the surface and subsurface characteristics as well as identify groundwater potential areas in Ras Sudr, west-central Sinai is the main objective of this study. Firstly, geophysical data including gravity and magnetic methods have a crucial importance in qualitative and quantitative interpretation of the subsurface elements. Filtering techniques were implemented to distinguish between regional and residual anomalies. Geophysical data were subjected to the radial average power spectrum technique and 3D Euler deconvolution to identify the depth of the subsurface sources. The structure pattern that characterizes the interested region was defined by employing bandpass filter and edge detection appoarches using residual anomaly maps, regional anomaly maps, tilt derivatives and total horizontal gradient maps reflecting four distinct structural trends; NW-SE parallel to the Gulf of Suez, NEE-SWW parallel to the Syrian arc system, N-S parallel to the the Nile Valley, and NNE-SSW parallel to the Gulf of Aqaba. A basement relief map was constructed using 3D magnetic modelling showing that the depth of the basement ranges from 1.6 to 6.3 km. Secondly, the remote sensing data including Sentienl-2 and SRTM datasets were employed to extract the surface analyses in the GIS environment to develop the occurrence of groundwater potentiality utilizing seven factors; LU-LC, soil, geology, slope, drainage network and lineament density and rainfall data of the study area which were ranked from 1 to 5 and weighted according to their effective contribution to the infiltration of groundwater using AHP-GIS based multi criteria method. The resulting Groundwater Potential zone (GWPZ) was categorized into five zones from poor to excellent and validated using 41 observed wells. A significant quantity of wells was identified in the areas of high potential located to the west of the region, while five wells were situated within the moderate potential zone. Thereby, the GWPZ map identifying locations with viable groundwater resources suitable for habitation, development and economic plans in Sinai for decision makers.

Food choice and neural reward systems in adolescents with anorexia nervosa and atypical anorexia nervosa.

Adolescence is a critical developmental period for the study of anorexia nervosa (AN), an illness characterized by extreme restriction of food intake. The maturation of the reward system during adolescence combined with recent neurobiological models of AN led to the hypothesis that early on in illness, restrictive food choices would be associated with activity in nucleus accumbens reward regions, rather than caudate regions identified among adults with AN. Healthy adolescents (HC, n = 41) and adolescents with AN or atypical AN (atypAN, n = 76) completed a Food Choice Task during fMRI scanning. Selection of high-fat foods and choice-related activation in nucleus accumbens and anterior caudate regions-of-interest (ROIs) were compared between individuals with AN/atypAN and HC. Associations were examined between choice-related activation and choice preferences among the AN group. Exploratory analyses examined associations between choice-related activation and psychological assessments among the patient group. Adolescents with AN or atypAN selected fewer high-fat foods than HC (t = -5.92, p < .001). Counter to predictions, there were no significant group differences in choice-related activation in the ROIs. Among individuals with AN or atypAN, choice-related neural activity in the anterior caudate was significantly negatively associated with high-fat food selections in the task (r = -.32, p = .024). In exploratory analyses, choice-related anterior caudate activation was positively associated with psychological measures of illness severity among patients (p's < .05, uncorrected). In this large cohort of adolescents with AN/atypAN, there was no evidence of altered reward system engagement during food choice. While there was no group difference in choice-related caudate activation, the associations with choices and psychological measures continue to suggest that this neural region is implicated in illness. Longitudinal analyses will clarify whether neural variability relates to longer-term course.

Characterization of myocardial ischemia-reperfusion injury in a pig model with novel CW T1r and RAFF2 MRI methods in 1.5T

Abstract Introduction Myocardial ischemia-reperfusion injury (IRI) occurs after myocardial infarction when reperfusion aggravates the initial damage in myocardium by vast infiltration of inflammatory cells and elevated oxidative stress in myocardium that may lead to cardiac remodeling, heart failure and even death [1]. Magnetic resonance imaging (MRI) can accurately determine the anatomy of the heart and to characterize myocardial tissue. Novel endogenous rotating frame MRI methods, including continuous wave (CW) T1r and relaxation along a fictitious field with rank 2 (RAFF2), have been shown to characterize MI and other CVD in mouse and human myocardium with high sensitivity and specificity [2,3]. CW T1r and RAFF2 contrasts are occurring during radio frequency (RF) pulse (in kHz range) making them to be sensitive to slow molecular motions [2]. Comparing to conventional endogenous T1 and T2 methods, these are occurring after the RF pulse (in Larmor frequency (MHz) range) and therefore, these are non-selectively sensitive to different correlation times [4]. Golden standard to image MI is to use contrast agent (CA) in late gadolinium enhancement (LGE) method [2,4]. Using CA has drawbacks and therefore, there is an urgent need for optimizing the endogenous MRI methods in clinical practice. Purpose To characterize myocardial IRI in a pig model and to determine the sensitivity of both novel and conventional cardiac MRI methods. Methods IRI operation was done in 7 pig (30-35 kg) by occluding the LAD with a balloon catheter for 50 minutes. Imaging was done with 1.5T (MAGNETOM Aera) at 3 days and 28 days after the operation. Pig hearts were imaged with conventional native T1, T2, LGE, and with novel CW T1r and RAFF2 methods in both time points. These were imaged before CA injection and LGE was done 25 minutes after CA injection. Relaxation time mappings were done in a pixel-by-pixel manner with monoexponentially fitting. Region-of-interest (ROI) analysis was done to determine the relaxation times in IRI and remote areas. LGE was used to confirm the IRI areas in the myocardium. Relative relaxation time difference (RRTD)-values were calculated with the function of (((Relaxation Time of IRI area) – (Relaxation Time of Remote Area))/ (Relaxation Time of Remote Area)) to determine the contrast difference between IRI and remote areas. Results Relaxation times were elevated in IRI areas compared to remote areas (Figure 1A). Novel rotating frame RRTD-values were significantly higher than RRTD-values in conventional methods. This means that novel methods have larger contrast difference between IRI and remote areas compared to conventional methods, which indicates that novel methods are sensitive to characterize IRI area (Figure 1B). The location of MI areas was similar in both novel rotating frame and conventional methods compared to LGE (Figure 2). Conclusion Novel rotating frame relaxation time methods are sensitive to characterize the IRI area in pig model.Relaxation times, RRTD-valuesRelaxation times maps and LGE

Cardiac amyloidosis: a texture issue. a proof of concept study on quantification of granular sparkling in patients with transthyretin-related cardiac amyloidosis using radiomics and granulometry

Abstract Background Granular sparkling is a well-known echocardiographic feature found in patients with transthyretin-related cardiac amyloidosis (ATTR-CA). However, there is no objective technique for quantifying this feature, which therefore remains a qualitative, elusive and ultimately unreliable imaging characteristic. Recent evidence suggests that radiomics and, in particular, ultrasonomics could play an important role in the non-invasive tissue characterization of cardiomyopathies through the study of specific myocardial texture features. Purpose The aim of this study is to statistically and geometrically characterize granular sparkling as a volume-independent texture property of the myocardium in patients with ATTR-CA. Methods We retrospectively collected echocardiogram video-clips in parasternal long axis (PLAX) and 4-chamber (4CH) views of 229 patients with ATTR-CA, and 224 age- and gender-matched hypertensive patients without any known cardiac disease. From each video-clip, one end-diastole frame was extracted and annotated by an expert to identify a region-of-interest (ROI) within the interventricular septum. Left ventricle chamber masks were also extracted, and used as a brightness reference to enforce invariance w.r.t. the settings of the specific ultrasound system (Fig. 1). Because many established radiomic textural features are heavily volume-confounded, we analyzed the ROI texture by extracting a subset of volume-invariant radiomic features (i.e. ROI-independent), as well as morphological granulometry features. We then fitted a logistic regression classifier to discriminate between ATTR-CA and controls based on the computed textural features. Results 94 radiomic features were identified, of which 73 were volume-invariant. The texture-based classifier predicted the diagnosis with a cross-validated accuracy of 91%, a specificity of 90% and a sensitivity of 92% by utilizing 73 volume-invariant radiomic features on both PLAX and 4CH views (Fig. 2). The addition of granulometry and the further addition of 11 volume-confounded features and 9 shape features to the model did not significantly enhance its discriminative performance (Fig. 2). The top 10 volume-invariant discriminative features were largely consistent in PLAX and 4CH views (Fig. 2), with variables descriptive of greater heterogeneity (such as glszm_ZoneEntropy and glszm_RunEnNtropy) and a higher spatial rate of change (such as ngtdm_Complexity) in the texture of ATTR-CA frames as compared to control frames. Conclusions Our results confirm the ability of radiomics in detecting subtle differences in the myocardial texture of echocardiographic images between patients with ATTR-CA and hypertensive controls. High levels of accuracy are obtained with a purely texture-based, volume-independent set of features. These findings suggest that the diagnosis of ATTR-CA could be simplified and made earlier using a statistical model based on the extraction of radiomic features.Figure 1Figure 2

Exploring the combined impact of color and editing on emotional perception in authentic films: Insights from behavioral and neuroimaging experiments

Film color and editing are crucial elements in filmmaking, posing significant questions about their effective use in eliciting emotional responses from viewers. While prior research has explored the impacts of color and editing independently, their combined effect remains largely unexplored, leaving the open question of how these elements together affect emotional perception. This study investigates the combined impact of film color and editing on emotion perception, utilizing both subjective behavioral scales and objective functional magnetic resonance imaging (fMRI) techniques. It employs two experiments with a two-factor design to assess the interactions between film color (colored and black-and-white) and film editing (fearful, neutral, and happy levels) on emotional perceptions. Under the direction of a professional filmmaker, a series of film sequences was created for six experimental conditions. In Experiment 1, 117 participants watched the film sequences and subjectively rated the emotional valence of the neutral face in the sequence, uncovering a significant interaction effect between film color and editing on the valence rating of the neutral face. In Experiment 2, 67 participants watched similar film sequences in an MRI scanner to analyze their brain activation patterns. Distinct activations were identified in regions including the insula, anterior cingulate cortices (ACC), and middle frontal gyrus, where a region-of-interest (ROI) analysis of the left ACC revealed an interaction effect on neural responses. These results underscore the integral role of color and editing in influencing viewers’ emotion perception using two types of measurements. This research provides novel insights into the behavioral and neural responses to filmmaking elements that underpin film reception and offers practical implications for filmmakers, encouraging a more holistic approach to considering color and editing to further enrich the audience’s emotional experiences at the pre-production stage of filmmaking.

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach

IntroductionSex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN). MethodsUsing an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns. ResultsOur findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns. ConclusionThis study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Stereo imaging inspired by bionic optics.

Stereo imaging has been a focal point in fields such as robotics and autonomous driving. This Letter discusses the imaging mechanisms of jumping spiders and human eyes from a biomimetic perspective and proposes a monocular stereo imaging solution with low computational cost and high stability. The stereo imaging mechanism of jumping spiders enables monocular imaging without relying on multiple viewpoints, thus avoiding complex large-scale feature point matching and significantly conserving computational resources. The foveal imaging mechanism of the human eye allows for complex imaging tasks to be completed only on the locally interested regions, resulting in more efficient execution of various visual tasks. By combining these two advantages, we have developed a more computationally efficient monocular stereo imaging method that can achieve stereo imaging on only the locally interested regions without sacrificing the performance of wide field-of-view (FOV) imaging. Finally, through experimental validation, we demonstrate that the method proposed in this Letter exhibits excellent stereo imaging performance.

Genetic associations with neural reward responsivity to food cues in children.

To test associations of candidate obesity-related single nucleotide polymorphisms (SNPs) and obesity polygenic risk scores (PRS) with neural reward reactivity to food cues. After consuming a pre-load meal, 9-12-year-old children completed a functional magnetic resonance imaging (fMRI) paradigm with exposure to food and non-food commercials. Genetic exposures included FTO rs9939609, MC4R rs571312, and a pediatric-specific obesity PRS. A targeted region-of-interest (ROI) analysis for 7 bilateral reward regions and a whole-brain analysis were conducted. Independent associations between each genetic factor and reward responsivity to food cues in each ROI were evaluated using linear models. Analyses included 151 children (M = 10.9 years). Each FTO rs9939609 obesity risk allele was related to a higher food-cue-related response in the right lateral hypothalamus after controlling for covariates including the current BMI Z-score (p < 0.01), however, the association did not remain significant after applying the multiple testing correction. MC4R rs571312 and the PRS were not related to heightened food-cue-related reward responsivity in any examined regions. The whole-brain analysis did not identify additional regions of food-cue-related response related to the examined genetic factors. Children genetically at risk for obesity, as indicated by the FTO genotype, may be predisposed to higher food-cue-related reward responsivity in the lateral hypothalamus in the sated state, which, in turn, could contribute to overconsumption. https://clinicaltrials.gov/study/NCT03766191, identifier NCT03766191.

Association of in vivo retention of [18f] flortaucipir pet with tau neuropathology in corresponding brain regions

[18F]Flortaucipir is an FDA-approved tau-PET tracer that is increasingly utilized in clinical settings for the diagnosis of Alzheimer’s disease. Still, a large-scale comparison of the in vivo PET uptake to quantitative post-mortem tau pathology and to other co-pathologies is lacking. Here, we examined the correlation between in vivo [18F]flortaucipir PET uptake and quantitative post-mortem tau pathology in corresponding brain regions from the AVID A16 end-of-life study (n = 63). All participants underwent [18F]flortaucipir PET scans prior to death, followed by a detailed post-mortem neuropathological examination using AT8 (tau) immunohistochemistry. Correlations between [18F]flortaucipir standardized uptake value ratios (SUVRs) and AT8 immunohistochemistry were assessed across 18 regions-of-interest (ROIs). To assess [18F]flortaucipir specificity and level of detection for tau pathology, correlations between [18F]flortaucipir SUVR and neuritic plaque score and TDP-43 stage were also computed and retention was further assessed in individuals with possible primary age-related tauopathy (PART), defined as Thal phase ≤ 2 and Braak stage I–IV. We found modest-to-strong correlations between in vivo [18F]flortaucipir SUVR and post-mortem tau pathology density in corresponding brain regions in all neocortical regions analyzed (rho-range = 0.61–0.79, p < 0.0001 for all). The detection threshold of [18F]flortaucipir PET was determined to be 0.85% of surface area affected by tau pathology in a temporal meta-ROI, and 0.15% in a larger cortical meta-ROI. No significant associations were found between [18F]flortaucipir SUVRs and post-mortem tau pathology in individuals with possible PART. Further, there was no correlation observed between [18F]flortaucipir and level of amyloid-β neuritic plaque load (rho-range = – 0.16–0.12; p = 0.48–0.61) or TDP-43 stage (rho-range = – 0.10 to – 0.30; p = 0.18–0.65). In conclusion, our in vivo vs post-mortem study shows that the in vivo [18F]flortaucipir PET signal primarily reflects tau pathology, also at relatively low densities of tau proteinopathy, and does not bind substantially to tau neurites in neuritic plaques or in individuals with PART.

Automated recognition and rebar dimensional assessment of prefabricated bridge components from low-cost 3D laser scanner

Dimension quality inspections for rebar spacing and length of prefabricated bridge components are critical for subsequent efficient assembly on-site. Currently, the traditional manual inspection method is time-consuming, labor-intensive, and error-prone. The existing commercial 3D LiDAR-based methods are difficult in making balances between inspection cost, efficiency, and accuracy. Thus, the automated recognition and segmentation method of 3D point clouds acquired by a self-developed low-cost LiDAR is proposed to evaluate the rebar spacing and length of bridge prefabricated components. Due to the high-cost commercial 3D laser scanner, a spinning 2D LiDAR-based low-cost 3D laser scanner device and geometric calibration model were developed to acquire the 3D spatial point cloud. Then, two-stage automated point cloud segmentation framework is proposed with coarse extraction of the point cloud in the interest region and fine recognition of point cloud using machine learning methods, including plane segmentation and circle fitting employing random sample consensus (RANSAC) algorithm, and rebars segmentation utilizing Gaussian mixture model (GMM) and density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed system was evaluated in two prefabricated concrete columns and shows the potential for improving the quality inspection efficiency and accuracy.

Enhancing Cone-Beam CT Image Quality in TIPSS Procedures Using AI Denoising.

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Consistent and Scalable Bayesian Joint Variable and Graph Selection for Disease Diagnosis Leveraging Functional Brain Network

We consider the joint inference of regression coefficients and the inverse covariance matrix for covariates in high-dimensional probit regression, where the predictors are both relevant to the binary response and functionally related to one another. A hierarchical model with spike and slab priors over regression coefficients and the elements in the inverse covariance matrix is employed to simultaneously perform variable and graph selection. We establish joint selection consistency for both the variable and the underlying graph when the dimension of predictors is allowed to grow much larger than the sample size, which is the first theoretical result in the Bayesian literature. A scalable Gibbs sampler is derived that performs better in high-dimensional simulation studies compared with other state-of-art methods. We illustrate the practical impact and utilities of the proposed method via a functional MRI dataset, where both the regions of interest with altered functional activities and the underlying functional brain network are inferred and integrated together for stratifying disease risk.

Enhanced Early Detection of Thyroid Abnormalities using a Hybrid Deep Learning Model: A Sequential CNN and K-Means Clustering Approach

The thyroid gland, often referred to as the butterfly gland due to its shape, is located in the neck and plays a crucial role in regulating metabolism. It is susceptible to various health conditions, including hypothyroidism, hyperthyroidism, thyroid cancer, and thyroid nodules. Early detection of these conditions is essential for accurate diagnosis and effective treatment. Detecting thyroid nodules using machine learning and deep learning techniques presents a challenging yet promising research avenue. The choice of model depends on the characteristics of the patient's thyroid data, the dataset size, and the available computational resources. Hybrid models can be employed to handle complex data more effectively. In this study, a sequential Convolutional Neural Network (CNN) model was developed due to its capability to automate feature extraction and focus on Regions-of-Interest (ROIs) for detecting thyroid abnormalities. The proposed model achieved an accuracy of 81.5%, with a precision of 97.4% and a sensitivity of 83.1%, indicating its robustness in classifying images as benign or malignant. The confusion matrix provided further performance insights. Data segmentation was enhanced using K-means clustering for its scalability and efficiency in processing large medical image datasets. Compared to traditional models, the proposed hybrid approach demonstrated a significant improvement in diagnostic accuracy and precision, achieving performance gains of approximately 15-20% over baseline methods. These advancements underscore the potential of integrating machine learning and deep learning in medical diagnostics, paving the way for more reliable and efficient diagnostic tools for healthcare professionals.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging.

Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

Quantitative diagnosis of carotid blowout syndrome with CT perfusion: Carotid blowout syndrome CTP quantitative diagnosis

Background and purposeCarotid blowout syndrome (CBS) is a potentially fatal disease. The CBS diagnosis mainly relies on subjective observations and the quantitative diagnotic method was not well established. This study aimed to diagnose CBS severity by computed tomography perfusion (CTP) parameters with different region-of-interest (ROI) models. Materials and MethodWe prospectively recruited CBS patients between February 1, 2018 and July 31, 2023 in a tertiary medical center, and CTP was performed using the same 128-detector CT machine. Digital subtraction angiography (DSA) and elective endovascular intervention were performed within 3 days post-CTP for diagnosis confirmation and treatments. CBS severity was classified into ongoing (threatened + impending) or acute CBS based on DSA findings and clinical features. Pericarotid soft-tissue (PCST) CTP parameters, including blood flow (BF), blood volume (BV), mean transit time (MTT) and flow extraction product (FEP), were evaluated and correlated on DSA. We depicted models A, B and C for the small focal lesion in 1 cm of PCST, 1 cm around PCST and the whole PCST respectively. ResultsCTP images of 110 patients (77 ongoing (45 threatened + 32 impending); 33 acute) were analyzed. Pericarotid BV (1.8 ± 1.2vs.3.5 ± 2.0; p < 0.001) in Model A and BF in Model B (42.6 ± 11.0vs.50.9 ± 20.4; p = 0.031) were lower in acute-CBS than in ongoing-CBS patients. Subgroup analysis demonstrated lower BV in acute (1.8 ± 1.2) compared with threatened (3.7 ± 2.3; p < 0.001; p < 0.001) and impending (3.2 ± 1.6; p = 0.009) CBS patients in Model A. ConclusionCBS severity can be quantitatively diagnosed by pericarotid soft-tissue CTP parameters. In Model A (small focus), BV was capable of differentiating acute CBS from other subtypes, demonstrating its potential role as a CBS imaging biomarker.

Comparison of Optimization Methods for the Attitude Control of Satellites

The definition of multiple operational modes in a satellite is of vital importance for the adaptation of the satellite to the operational demands of the mission and environmental conditions. In this work, three optimization methods were implemented for the initial calibration of an attitude controller based on fuzzy logic with the purpose of performing an initial exploration of optimal regions of the design space: a multi-objective genetic algorithm (GAMULTIOBJ), a particle swarm optimization (PSO), and a multi-objective particle swarm optimization (MOPSO). The performance of the optimizers was compared in terms of energy cost, accuracy, computational cost, and convergence capabilities of each algorithm. The results show that the PSO algorithm demonstrated superior computational efficiency compared to the others. Concerning the exploration of optimum regions, all algorithms exhibited similar exploratory capabilities. PSO’s low computational cost allowed for thorough scanning of specific interest regions, making it ideal for detailed exploration, whereas MOPSO and GAMULTIOBJ provided more balanced performance with constrained Pareto front elements.

Effects of different VV ECMO blood flow rates on lung perfusion assessment by hypertonic saline bolus-based electrical impedance tomography

ObjectiveOur study aimed to investigate the effects of different extracorporeal membrane oxygenation (ECMO) blood flow rates on lung perfusion assessment using the saline bolus-based electrical impedance tomography (EIT) technique in patients on veno-venous (VV) ECMO.MethodsIn this single-centered prospective physiological study, patients on VV ECMO who met the ECMO weaning criteria were assessed for lung perfusion using saline bolus-based EIT at various ECMO blood flow rates (gradually decreased from 4.5 L/min to 3.5 L/min, 2.5 L/min, 1.5 L/min, and finally to 0 L/min). Lung perfusion distribution, dead space, shunt, ventilation/perfusion matching, and recirculation fraction at different flow rates were compared.ResultsFifteen patients were included. As the ECMO blood flow rate decreased from 4.5 L/min to 0 L/min, the recirculation fraction decreased significantly. The main EIT-based findings were as follows. (1) Median lung perfusion significantly increased in region-of-interest (ROI) 2 and the ventral region [38.21 (34.93–42.16)% to 41.29 (35.32–43.75)%, p = 0.003, and 48.86 (45.53–58.96)% to 54.12 (45.07–61.16)%, p = 0.037, respectively], whereas it significantly decreased in ROI 4 and the dorsal region [7.87 (5.42–9.78)% to 6.08 (5.27–9.34)%, p = 0.049, and 51.14 (41.04–54.47)% to 45.88 (38.84–54.93)%, p = 0.037, respectively]. (2) Dead space significantly decreased, and ventilation/perfusion matching significantly increased in both the ventral and global regions. (3) No significant variations were observed in regional and global shunt.ConclusionsDuring VV ECMO, the ECMO blood flow rate, closely linked to recirculation fraction, could affect the accuracy of lung perfusion assessment using hypertonic saline bolus-based EIT.