Presence Of Images Research Articles

Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets

ObjectivesExisting brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net–based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors.MethodsThis is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann–Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases.ResultsIn the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988–0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099–8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989–0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000–8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983–0.998) and HD of 8.972 mm (IQR, 6.164–13.710 mm).ConclusionsFor automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis.Clinical relevance statementThe proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments.Key Points• The nnU-Net–based model is capable of segmenting significant superficial structures in brain extraction.• The proposed model showed feasible performance, regardless of pathological types or tumor characteristics.• The model showed generalization in the public datasets.

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

Whole-Body Imaging Using Low Frequency Transmission Ultrasound

To indicate that 3D low-frequency ultrasound tomography with 3D data acquisition (volography) is a safe, low-cost, high-resolution, whole-body meso-scale medical imaging modality that gives high-resolution quantitatively accurate clinically relevant images. We compare the speed of sound accuracy in various organs in situ. We validate our 3D ultrasound tomography images using MRI and gross section anatomy as ground truth in 10-day old piglets. Data acquisition is accomplished with the QT Scanner at ∼1 MHz center frequency, and array transceivers for reflection data @3.6 MHz. Images are generated with unique model-based 3D ultrasound tomography algorithms. In reflection, we use 3D refraction-corrected ray tracing to allow 360° compounding with sub-mm resolution. Four 10-12 day old pigs were anesthetized and whole-body images were acquired via low-frequency transmitted ultrasound and 3T MRI. Tissue values were within an average of 1.07% (0.5%) of the literature values. We also show the detailed correlation of our images with MRI images in axial, coronal, and sagittal views. Volography images of a piglet show high resolution and quantitative accuracy, showing more contrast &resolution than 3T MRI, including the kidney showing medulla, cortex and fibrous cover, and small intestines with ileal lumen detail visible. We establish that 3D ultrasound tomography (volography), yields high-resolution quantitatively accurate images whole-body images in presence of bone and air which are potentially clinically useful but have not appeared in the literature.

Classification regularized dimensionality reduction improves ultrasound thyroid nodule diagnostic accuracy and inter-observer consistency

ProblemConvolutional Neural Networks (CNNs) for medical image analysis usually only output a probability value, providing no further information about the original image or inter-relationships between different images. Dimensionality Reduction Techniques (DRTs) are used for visualization of high dimensional medical image data, but they are not intended for discriminative classification analysis. AimWe develop an interactive phenotype distribution field visualization system for medical images to accurately reflect the pathological characteristics of lesions and their similarity to assist radiologists in diagnosis and medical research. MethodsWe propose a novel method, Classification Regularized Uniform Manifold Approximation and Projection (UMAP) referred as CReUMAP, combining the advantages of CNN and DRT, to project the extracted feature vector fused with the malignant probability predicted by a CNN to a two-dimensional space, and then apply a spatial segmentation classifier trained on 2614 ultrasound images for prediction of thyroid nodule malignancy and guidance to radiologists. ResultsThe CReUMAP embedding correlates well with the TI-RADS categories of thyroid nodules. The parametric version that embeds external test dataset of 303 images in presence of the training data with known pathological diagnosis improves the benign and malignant nodule diagnostic accuracy (p-value = 0.016) and confidence (p-value = 1.902 × 10−6) of eight radiologists of different experience levels significantly as well as their inter-observer agreements (kappa≥0.75). CReUMAP achieve 90.8% accuracy, 92.1% sensitivity and 88.6% specificity in test set. ConclusionCReUMAP embedding is well correlated with the pathological diagnosis of thyroid nodules, and helps radiologists achieve more accurate, confident and consistent diagnosis. It allows a medical center to generate its locally adapted embedding using an already-trained classification model in an updateable manner on an ever-growing local database as long as the extracted feature vectors and predicted diagnostic probabilities of the correspondent classification model can be outputted.

Application of a Rhodamine-chromone Schiff base probe for the sensing of Fe3+, Al3+, Cr3+ at low concentration and exploration of the anticancer activity and bio-imaging

Strategic design of fluorescent probe for the recognition of ions/molecules at very low concentration by fluorescence sensing is one of the stimulating current research topics with reference to health and sustainability. Towards this objective, Rhodamine based chemosensors are playing extraordinary protagonist. In this work, a Rhodamine appended chromone Schiff base (RFC), with two fluorogenic motifs, has been characterized through various spectroscopic techniques. The probe exhibits efficient sensitivity towards Analytical Group IIIA ions (Al3+, Cr3+ and Fe3+) in existence of other competitive analytes. The fluorescent intensity is enhanced by 350 fold (Fe3+), 250 fold (Al3+) and 220 fold (Cr3+), respectively in methanol-water (99:1,v/v; HEPES buffer, pH, 7.2) medium. The limits of detection (LOD) (3σ/M method; σ = standard deviation; M = slope) are 0.0051 ppm (Fe3+), 0.0029 ppm (Al3+) and 0.0052 ppm (Cr3+); which are impressive and much below the WHO recommended data. In addition to this, the compound has been utilized for cell imaging in presence of these three cations. The action of probe on MCF-7 (human breast cancer) cell lines demonstrates IC50, 2.53 µM that is far better than activity against WI-38 (normal cell line) (IC50, 393.7 µM). The drug (RFC) minimizes the cellular proliferation and has significant wound healing capacity of the infected tissue with MCF-7 cells. The probe shows significant nuclear fragmentation with increase in the expression of apoptotic markers. Growth inhibition analysis in 3D tumor spheroid model showed the inhibition of breast cancer MCF-7 cells.

Quantitative susceptibility-weighted imaging in presence of strong susceptibility sources: Application to hemorrhage

PurposeTo optimize quantitative susceptibility-weighted imaging also known as true susceptibility-weighted imaging (tSWI) for strong susceptibility sources like hemorrhage and compare to standard susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM). MethodsTen patients with known intracerebral hemorrhage (ICH) were scanned using a 3D SWI sequence. The magnitude and phase images were utilized to compute QSM, tSWI and SWI images. tSWI parameters including the upper threshold for creating susceptibility-weighted masks and the multiplication factor were optimized for hemorrhage depiction. Combined tSWI was also computed with independent optimized parameters for both veins and hemorrhagic regions. tSWI results were compared to SWI and QSM utilizing region-of-interest measurements, Pearson's correlation and Kruskal-Wallis test. ResultsFifteen hemorrhages were found, with mean susceptibility 0.81 ± 0.37 ppm. Unlike SWI which utilizes a phase mask, tSWI uses a mask computed from QSM. In tSWI, the weighted mask required an extended upper threshold far beyond the standard level for more effective visualization of hemorrhage texture. The upper threshold was set to the mean maximum susceptibility in the hemorrhagic region (3.24 ppm) with a multiplication factor of 2. The blooming effect, seen in SWI, was observed to be larger in hemorrhages with higher susceptibility values (r = 0.78, p < 0.001) with reduced blooming on tSWI. On SWI, 4 out of 15 hemorrhages showed phase wrap artifacts in the hemorrhagic region and all patients showed some phase wraps in the air-tissue interface near the auditory and frontal sinuses. These phase wrap artifacts were absent on tSWI. In hemorrhagic regions, a higher correlation was observed between the actual susceptibility values and mean gray value for tSWI (r = −0.93, p < 0.001) than SWI (r = −0.87, p < 0.001). ConclusionIn hemorrhage, tSWI minimizes both blooming effects and phase wrap artifacts observed in SWI. However, unlike SWI, tSWI requires an altered upper threshold for best hemorrhage depiction that greatly differs from the standard value. tSWI can be used as a complementary technique for visualizing hemorrhage along with SWI.

Prognostic value of left ventricular blood stasis in patients with acute myocardial infarction: A cardiac magnetic resonance study

BackgroundCerebrovascular accidents (CVAs) can complicate the follow-up of patients with acute myocardial infarction (AMI). Intracavitary blood stasis evaluation can be useful to stratify cardioembolic risk and optimize medical therapy. Cardiac magnetic resonance (CMR) imaging is increasingly used in AMI patients for prognostic purposes. Slow-flow artefact is common on T2-weighted images in presence of left ventricular (LV) blood stasis. Methods and resultsIn this observational retrospective study, all patients with AMI undergoing CMR in our hospital were included. T2-weighted images were used to assess the presence of LV blood stasis. Among the 209 patients enrolled (males 72%, mean age 61 ± 12 years), LV blood stasis was detected in 48 (23%) and was significantly more prevalent in patients with extensive anterior or antero-apical AMI, lower LV ejection fraction, greater infarct size and microvascular obstruction. During follow-up (54 months, IQR 41–70), 8 CVAs and 6 cardiovascular deaths occurred. LV blood stasis emerged as a significant risk factor for both endpoints (logRank p < 0.001 and p = 0.008, respectively) and remained independent predictor of CVAs (HR 9.819, 95% CI 1.733–55.617, p = 0.010). ConclusionsLV blood stasis identified with CMR predicts the occurrence of CVAs in patients with AMI. These results may be helpful to personalize antithrombotic therapy for prevention strategies.

Noise reduction and 3D image restoration of single photon counting LiDAR using adaptive gating

Single photon LiDAR is considered as one of the most important tools in acquiring target information with high accuracy under extreme imaging conditions, as it offers single photon sensitivity and picosecond timing resolution. However, such technique sense the scene with the photons reflected by the target, thus resulting in severe degradation of image in presence of strong noise. Range gating with high-speed electronics is an effective way to suppress the noise, unfortunately, such technique suffers from manually selecting the parameters and limited gating width. This paper presents a target information extracting and image restoration method under large observation window, which first obtain the depth distribution of the target and extract the information within the range by analyzing the model of signal and noise, then further improve the image quality by adopting advanced image restoration algorithm and henceforth shows better results than those denoising method that purely relying on hardware. In the experiment, photon-per-pixel (PPP) was as low as 3.020 and signal-to-background ratio (SBR) was as low as 0.106, the proposed method is able to improve SBR with a factor of 19.330. Compared to classical algorithm named cross correlation, the reconstruction signal to noise ratio (RSNR) increased 33.520dB by further cooperating with advanced image restoration algorithm, thus improved the ability of sensing accurate target information under extreme cases.

Prostate cancer biomarker citrate detection using triaminoguanidinium carbon dots, its applications in live cells and human urine samples

Citrate is a tricarboxylate, plays vital role in prostate cancer (PC) and the level of citrate is an indicator for PC identification. Herein, triaminoguanidine carbon dots (TAG-CDs) prepared by one step hydrothermal method and used as a citrate receptor. Notably the TAG-CDs without alkaline treatment were highly fluorescent at pH 7 with high quantum yield (11.3%). TAG-CDs were characterized through TEM, XRD, FT-IR, UV–vis and spectrofluorimetry. It is noted that the average size was of 2.8 nm, the presence of highly disordered carbon, retain the functionality of TAG. The absorbance maxima obtained at 294 nm and good emitting response observed at 396 nm. The Y-aromaticity of receptor guanidinium moiety acts as Lewis acid and have peculiar interaction with Lewis base citrate via electrostatic interaction and also protons in the TAG participate hydrogen bonds with citrate, which causes quenching of TAG-CDs. From the obtained linear quenching equation the LOD was found to be 4 nM. The probe expressed high selectivity, high interference tolerance (500 – fold), fast response in 15 mins and good biocompatible. Finally, TAG-CDs utilized for the intracellular imaging of citrate in live MCF-7 cells, it showed good cytotoxicity and delivered contrast images in presence, absence of citrate. TAG-CDs detected the citrate level in human urine samples, the obtained results are validated with HPLC method.

Atom-by-atom chemical identification from scanning transmission electron microscopy images in presence of noise and residual aberrations

The simple dependence of the intensity in annular dark field scanning transmission electron microscopy images on the atomic number provides (to some extent) chemical information about the sample, and even allows an elemental identification in the case of light-element single-layer samples. However, the intensity of individual atoms and atomic columns is affected by residual aberrations and the confidence of an identification is limited by the available signal to noise. Here, we show that matching a simulation to an experimental image by iterative optimization provides a reliable analysis of atomic intensities even in presence of residual non-round aberrations. We compare our new method with other established approaches demonstrating its high reliability for images recorded at limited dose and with different aberrations. This is of particular relevance for analyzing moderately beam-sensitive materials, such as most 2D materials, where the limited sample stability often makes it difficult to obtain spectroscopic information at atomic resolution.

Surface discharge imaging in presence of deposited space charges in non‐uniform DC electric field

Surface discharge imaging in presence of deposited space charges in non‐uniform DC electric field

Endogenous T1\u03c1 cardiovascular magnetic resonance in hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) is characterized by increased left ventricular wall thickness, cardiomyocyte hypertrophy, and fibrosis. Adverse cardiac risk characterization has been performed using late gadolinium enhancement (LGE), native T1, and extracellular volume (ECV). Relaxation time constants are affected by background field inhomogeneity. T1ρ utilizes a spin-lock pulse to decrease the effect of unwanted relaxation. The objective of this study was to study T1ρ as compared to T1, ECV, and LGE in HCM patients.MethodsHCM patients were recruited as part of the Novel Markers of Prognosis in Hypertrophic Cardiomyopathy study, and healthy controls were matched for comparison. In addition to cardiac functional imaging, subjects underwent T1 and T1ρ cardiovascular magnetic resonance imaging at short-axis positions at 1.5T. Subjects received gadolinium and underwent LGE imaging 15–20 min after injection covering the entire heart. Corresponding basal and mid short axis LGE slices were selected for comparison with T1 and T1ρ. Full-width half-maximum thresholding was used to determine the percent enhancement area in each LGE-positive slice by LGE, T1, and T1ρ. Two clinicians independently reviewed LGE images for presence or absence of enhancement. If in agreement, the image was labeled positive (LGE + +) or negative (LGE −−); otherwise, the image was labeled equivocal (LGE + −).ResultsIn 40 HCM patients and 10 controls, T1 percent enhancement area (Spearman’s rho = 0.61, p < 1e-5) and T1ρ percent enhancement area (Spearman’s rho = 0.48, p < 0.001e-3) correlated with LGE percent enhancement area. T1 and T1ρ percent enhancement areas were also correlated (Spearman’s rho = 0.28, p = 0.047). For both T1 and T1ρ, HCM patients demonstrated significantly longer relaxation times compared to controls in each LGE category (p < 0.001 for all). HCM patients also showed significantly higher ECV compared to controls in each LGE category (p < 0.01 for all), and LGE −− slices had lower ECV than LGE + + (p = 0.01).ConclusionsHyperenhancement areas as measured by T1ρ and LGE are moderately correlated. T1, T1ρ, and ECV were elevated in HCM patients compared to controls, irrespective of the presence of LGE. These findings warrant additional studies to investigate the prognostic utility of T1ρ imaging in the evaluation of HCM patients.

Working Length Determination Using Apex Locator and Cone Beam Computed Tomography in Presence of Three Irrigating Solutions in Primary Teeth: An in Vitro Comparative Study

Working Length Determination Using Apex Locator and Cone Beam Computed Tomography in Presence of Three Irrigating Solutions in Primary Teeth: An in Vitro Comparative Study

On age prediction from facial images in presence of facial expressions

On age prediction from facial images in presence of facial expressions

On age prediction from facial images in presence of facial expressions

Predicting age and gender from facial images is a fundamental research problem that has many applications in major research areas. The state-of-the-art online APIs can predict age and gender but th...

Entropy-like Divergence Based Kernel Fuzzy Clustering for Robust Image Segmentation

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise.

Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT.

Purpose: To evaluate diagnostic accuracy of conventional radiography (CXR) and machine learning enhanced CXR (mlCXR) for the detection and quantification of disease-extent in COVID-19 patients compared to chest-CT. Methods: Real-time polymerase chain reaction (rt-PCR)-confirmed COVID-19-patients undergoing CXR from March to April 2020 together with COVID-19 negative patients as control group were retrospectively included. Two independent readers assessed CXR and mlCXR images for presence, disease extent and type (consolidation vs. ground-glass opacities (GGOs) of COVID-19-pneumonia. Further, readers had to assign confidence levels to their diagnosis. CT obtained ≤ 36 h from acquisition of CXR served as standard of reference. Inter-reader agreement, sensitivity for detection and disease extent of COVID-19-pneumonia compared to CT was calculated. McNemar test was used to test for significant differences. Results: Sixty patients (21 females; median age 61 years, range 38–81 years) were included. Inter-reader agreement improved from good to excellent when mlCXR instead of CXR was used (k = 0.831 vs. k = 0.742). Sensitivity for pneumonia detection improved from 79.5% to 92.3%, however, on the cost of specificity 100% vs. 71.4% (p = 0.031). Overall, sensitivity for the detection of consolidation was higher than for GGO (37.5% vs. 70.4%; respectively). No differences could be found in disease extent estimation between mlCXR and CXR, even though the detection of GGO could be improved. Diagnostic confidence was better on mlCXR compared to CXR (p = 0.013). Conclusion: In line with the current literature, the sensitivity for detection and quantification of COVID-19-pneumonia was moderate with CXR and could be improved when mlCXR was used for image interpretation.

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Subclass Heterogeneity Aware Loss for Cross-Spectral Cross-Resolution Face Recognition

One of the most challenging scenarios of face recognition is matching images in presence of multiple covariates such as cross-spectrum and cross-resolution. In this paper, we propose a Subclass Heterogeneity Aware Loss (SHEAL) to train a deep convolutional neural network model such that it produces embeddings suitable for heterogeneous face recognition, both single and multiple heterogeneities. The performance of the proposed SHEAL function is evaluated on four databases in terms of the recognition performance as well as convergence in time and epochs. We observe that SHEAL not only yields state-of-the-art results for the most challenging case of Cross-Spectral Cross-Resolution face recognition, it also achieves excellent performance on homogeneous face recognition.

Predicting cannulation difficulty in endoscopic retrograde cholangiopancreatography using CT image findings: a decision-tree analysis.

Difficult cannulation during endoscopic retrograde cholangiopancreatography (ERCP) is associated with increased complications; therefore, its prediction is important. To identify radiologic risk factors of difficult cannulation during ERCP based on computed tomography (CT) findings and to develop a predictive model for a difficult cannulation. A total of 171 patients with native papilla who underwent both enhanced CT and ERCP were recruited. Two radiologists independently measured the distal common bile duct (CBD) diameter and choledochoduodenal (CD) angle and analyzed CT images for presence of CBD stone and papilla bulging, size and type of periampullary diverticulum (PAD), and duodenal segment in which major papilla was located. Multivariate logistic regression analysis and decision-tree analysis were performed to identify risk factors for difficult cannulation. Thirty-nine patients underwent a difficult cannulation. The multivariate logistic regression analysis revealed that a smaller CBD diameter, presence of papilla bulging, location of the major papilla other than the descending duodenum, a smaller CD angle, and a higher worrisome PAD score were statistically relevant factors for difficult cannulation (P < 0.049). In the decision-tree analysis, a higher worrisome PAD score was the strongest predictor of difficult cannulation, followed by the presence of papilla bulging, smaller CD angle, and a smaller CBD diameter. The predictive model had an 82.5% overall predictive accuracy. The CT findings-based decision-tree analysis model showed a high accuracy in predicting cannulation difficulty and may be helpful for making pre-ERCP strategy.