Corrigendum: Ethnocultural empathy development of future language teachers through digital multiliteracy resources for low-literacy adult migrants.

[This corrects the article DOI: 10.3389/fpsyg.2024.1398457.].

Radiomics features for the discrimination of tuberculomas from high grade gliomas and metastasis: a multimodal study.

BACKGROUND: Tuberculomas are prevalent in developing countries and demonstrate variable signals on MRI resulting in the overlap of the conventional imaging phenotype with other entities including glioma and brain metastasis. An accurate MRI diagnosis is important for the early institution of anti-tubercular therapy, decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision. This study aims to assess the potential of radiomics features of regular contrast images including T1W, T2W, T2W FLAIR, T1W post contrast images, and ADC maps, to differentiate between tuberculomas, high-grade-gliomas and metastasis, the commonest intra parenchymal mass lesions encountered in the clinical practice. METHODS: This retrospective study includes 185 subjects. Images were resampled, co-registered, skull-stripped, and zscore-normalized. Automated lesion segmentation was performed followed by radiomics feature extraction, train-test split, and features reduction. All machine learning algorithms that natively support multiclass classification were trained and assessed on features extracted from individual modalities as well as combined modalities. Model explainability of the best performing model was calculated using the summary plot obtained by SHAP values. RESULTS: Extra tree classifier trained on the features from ADC maps was the best classifier for the discrimination of tuberculoma from high-grade-glioma and metastasis with AUC-score of 0.96, accuracy-score of 0.923, Brier-score of 0.23. CONCLUSION: This study demonstrates that radiomics features are effective in discriminating between tuberculoma, metastasis, and high-grade-glioma with notable accuracy and AUC scores. Features extracted from the ADC maps surfaced as the most robust predictors of the target variable.

Anomalous Origin of the Right Coronary Artery From the Pulmonary Artery: Advantages of Multimodality Imaging.

Coronary anomalies are one of the most surprising yet challenging pediatric cardiology diagnoses. The anomalous origin of the right coronary artery from the pulmonary artery (ARCAPA) is frequently underdiagnosed due to a lack of typical signs or symptoms. We present a case of ARCAPA in a healthy six-month-old girl during follow-up of a newly detected heart murmur. Echocardiography raised the suspicion of a coronary anomaly, but the diagnosis was unclear, so cardiac catheterization and computed tomography were performed, which posteriorly confirmed the diagnosis. The patient underwent surgical repair, and the short-term follow-up has been uneventful. Regular monitoring is essential due to the potential long-term complications of ARCAPA, including myocardial ischemia, heart failure, and sudden cardiac death, underscoring the importance of early diagnosis and continuous management.

[Percutaneous closure of a coronary-pulmonary fistula : a case report]. / Fermeture percutanée d'une fistule coronaro-pulmonaire : à propos d'un cas.

BACKGROUND: A coronary artery fistula is an abnormal connection between one or more coronary arteries and a cardiac chamber or great vessel, often discovered incidentally through cardiac imaging. Although coronary artery fistulas are typically asymptomatic during the first two decades of life, particularly when small, they can become clinically significant over time. CASE PRESENTATION: We present the case of a 71-year-old female patient with a history of exertional dyspnea. Diagnostic coronary angiography revealed a significant coronary artery fistula originating from the proximal right coronary artery and draining into the pulmonary artery trunk. Given the patient's symptoms and the anatomical features of the fistula, she was successfully treated with transcutaneous closure using a liquid embolic agent (Onyx). CONCLUSION: Although surgical intervention has historically been the primary treatment for CAF, minimally invasive techniques such as transcutaneous closure are proving to be effective alternatives.

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. METHODS: Using the open-source dataset MIMIC-IV and MIMICCXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. RESULTS: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. CONCLUSION: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments.

Case report and literature review: cardiac hematic cyst.

A 49-year-old female patient, asymptomatic, presented to the cardiology office for a right atrial mass, identified incidentally in a non-electrocardiogram (ECG)-gated contrast-enhanced computed tomography, performed for follow-up of pulmonary tuberculosis. Echocardiography, surprisingly, showed an anechogenic ovoid mass in the right atrium measuring 40 × 40 mm2, implanted in the interatrial septum without affecting the tricuspid valve. ECG-gated computed tomography angiography (CTA), confirmed the dimensions of the mass, which presented homogeneous content, calcified areas, and a 12-mm pedicle implanted near the ostium of the coronary sinus. Additionally, contrast uptake and infiltration of adjacent structures were ruled out. In the surgical field, an encapsulated mass with blood content was found, which pathology reported as a hematic endocardial cyst (HEC). These are rare cardiac masses, constituting 1.5% of all primary cardiac tumors. It is usually an incidental finding, and its clinical presentation will depend on its dimensions and the intracardiac hemodynamic impact. A highlighting feature is its anechogenic content on ultrasound, however, multimodality imaging allows for making diagnostic assumptions, discerning between primary cardiac tumors, and provides morphological and hemodynamic information useful for therapeutic decision making. The age of the patient, the large size of the HEC, and its location in the interatrial septum make up a completely atypical presentation of this rare disease, which motivated this report.

Assessing Intra-Bundle Impregnation in Partially Impregnated Glass Fiber-Reinforced Polypropylene Composites Using a 2D Extended-Field and Multimodal Imaging Approach.

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-field images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Case Report: Left ventricular apical hypertrophy in a patient with Leopard syndrome mimicking a cardiac tumor: a diagnostic challenge resolved by multimodality imaging.

Background: LEOPARD syndrome (LS) is a rare genetic disorder presenting various clinical manifestations from childhood, complicating its diagnosis. In this study, we aim to refine the imaging presentation of LS and emphasize the importance of multimodality imaging in enhancing diagnostic accuracy and preventing serious cardiovascular events. Case: A 41-year-old woman was admitted to hospital with a suspected apical tumor detected by a transthoracic echocardiogram (TTE), which was later identified as apical myocardial hypertrophy through cardiac magnetic resonance imaging (CMR). She had abnormal electrocardiograms from the age of 2 years and freckles around the age of 4 years. In recent years, she has been experiencing exertional dyspnea. Supplemental coronary computer tomography angiography (CCTA) revealed diffuse coronary dilatation. Both multimodality imaging and clinical manifestations led to a suspicion of LS, which was confirmed by subsequent genetic testing. The patient declined further treatment. A 3-month follow-up CMR showed no significant change in the lesion. Conclusion: This report elucidates the diagnostic transition from an initial suspicion of an apical tumor by TTE to a definitive diagnosis of left ventricular apical hypertrophy by CMR in a 41-year-old woman with LS. It underscores the value of multimodality imaging (TTE, CCTA, CMR) in unraveling unusual cardiac manifestations in rare genetic disorders such as LS.

Caseous Calcification of the Mitral Annulus Associated with Severe Mitral Regurgitation: A Multimodality Diagnostic Approach.

Caseous calcification of the mitral annulus (CCMA) is a rare variant of mitral annular calcification, and a multimodality approach is advised to ensure an accurate diagnosis. We report a case of a patient with CCMA, associated with severe mitral regurgitation. An 82-year-old woman was admitted due to worsening heart failure. Transthoracic echocardiography revealed a fixed, hyperechogenic mass, accompanied by restriction of the posterior mitral leaflet, and subsequent severe mitral regurgitation. Transesophageal echocardiography demonstrated a restricted motion of the posterior mitral leaflet, because of a large, echogenic mass (15 mm × 11 mm), attached to the mitral annulus, vacuolated with a central echolucent aspect, lacking acoustic shadowing. Contrast-enhanced cardiac computed tomography identified a distinct oval mass (18 mm × 11 mm × 19 mm) presenting a central hypodense content and peripheral calcification, strongly suggestive of CCMA. Considering the patient's profile, surgical valvular replacement was considered unsuitable. Therefore, a transcatheter edge-to-edge repair was performed, resulting in mild residual regurgitation.

A Case Report of Aortic Intramural Hematoma: From Diagnosis to Endovascular Treatment Guided by Transesophageal Echocardiography.

Aortic intramural hematoma (IMH) accounts for approximately 10%-25% of acute aortic syndromes (AAS), and multi-slice computed tomography and magnetic resonance imaging are the leading techniques for diagnosis and classification. In this context, endovascular strategies provide a valid alternative to traditional open surgery and transesophageal echocardiography (TEE) could play a role in therapeutic decision-making and in endovascular repair procedure guidance. A 57-year-old female patient with IMH extending from the left subclavian artery to the upper tract of the abdominal aorta, underwent endovascular aortic repair using an unibody single-branched stent grafting in the aortic arch and descending aorta with a side branch inserted in the left common carotid artery. To restore proper flow in the left axillary artery, a carotid-subclavian bypass graft was performed. The procedure was guided by angiography and TEE. Intraoperative TEE revealed aortic IMH with a significant fluid component in the middle tunic of the aorta with a wall thickness of over 13 mm. TEE was useful in monitoring of all steps of the procedure, showing the presence of the guidewires into the true lumen, the advancement of the prosthesis, and the phases of release and anchoring. This case highlights the importance of using multimodality imaging techniques to evaluate AAS and demonstrates the growing potential of TEE in guiding endovascular repairs.

18F-Fluorodeoxyglucose (18F-FDG) PET/CT Appearance of Baastrup's Disease and Its Multimodality Correlation.

Baastrup's disease (BD), commonly known as "kissing spine syndrome," presents a significant cause of lower back pain, predominantly affecting the lumbar region. Diagnosis is often challenging due to its symptomatology and radiographic presentation. Herein, we present a case series demonstrating the utility of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in diagnosing BD accurately, particularly in oncologic settings where it may mimic metastatic lesions. Through a series of cases, we demonstrate the distinctive imaging features of BD on 18F-FDG PET/CT and its differentiation from malignancies. In addition, we emphasize the importance of clinical awareness and proper correlation with CT or MRI to avoid misinterpretation. Furthermore, we discuss the pathophysiology, clinical presentation, and diagnostic modalities of BD, highlighting its underdiagnosis and potential to mimic metastasis on imaging. By enhancing recognition of BD's appearance on 18F-FDG PET/CT, this study aims to prevent misdiagnoses, reduce unnecessary investigations, and ultimately improve patient care in oncologic practice.

Evidence modeling for reliability learning and interpretable decision-making under multi-modality medical image segmentation.

Reliability learning and interpretable decision-making are crucial for multi-modality medical image segmentation. Although many works have attempted multi-modality medical image segmentation, they rarely explore how much reliability is provided by each modality for segmentation. Moreover, the existing approach of decision-making such as the softmax function lacks the interpretability for multi-modality fusion. In this study, we proposed a novel approach named contextual discounted evidential network (CDE-Net) for reliability learning and interpretable decision-making under multi-modality medical image segmentation. Specifically, the CDE-Net first models the semantic evidence by uncertainty measurement using the proposed evidential decision-making module. Then, it leverages the contextual discounted fusion layer to learn the reliability provided by each modality. Finally, a multi-level loss function is deployed for the optimization of evidence modeling and reliability learning. Moreover, this study elaborates on the framework interpretability by discussing the consistency between pixel attribution maps and the learned reliability coefficients. Extensive experiments are conducted on both multi-modality brain and liver datasets. The CDE-Net gains high performance with an average Dice score of 0.914 for brain tumor segmentation and 0.913 for liver tumor segmentation, which proves CDE-Net has great potential to facilitate the interpretation of artificial intelligence-based multi-modality medical image fusion.

Gestures, Objects, and Spaces: Exploring Teachers' Multimodal Communication in Nursery Schools.

This study builds on the increasing evidence that the multimodal nature of adult-child interactions and the use of objects play an important role in early linguistic development. Most of these studies analyzed dyadic interactions at home, whereas few research has been conducted in early childhood education and care settings. In this paper, we characterized the multimodal nature of teachers' communicative bids during classroom-based group interactions in nursery schools. Observational data of circle-time activities was collected from 16 Spanish nursery school classrooms, comprising 16 teachers and 161 children between two and three years of age. We analyzed teachers' communicative bids (i.e., verbal utterances and verbal-gestural bids) considering the frequency of use of different types of gestures, to whom are they addressed (i.e., the whole group or a single child), the extent to which they involve the use of objects, the classroom layout, and the relationship between the communicative bids and the number of children that participated in each classroom. Teachers' communication with toddlers is highly multimodal and rely on different types of gestures, although the use of objects in our sample was scarce. Descriptive analysis suggest that certain classroom layouts may favor teachers' use of some types of gestures over others. In this article, we discuss the implications of both the use of objects and space for understanding how adults shape the linguistic contexts of young children, and the potential opportunities and limitations they pose for classroom interactions.

IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models.

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision-language correlation from image-text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists' opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.

Background and purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. Results: Significant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. Conclusions: Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Tri-modality in vivo imaging for tumor detection with combined ultrasound, photoacoustic, and photoacoustic elastography.

A comprehensive understanding of a tumor is required for accurate diagnosis and effective treatment. However, currently, there is no single imaging modality that can provide sufficient information. Photoacoustic (PA) imaging is a hybrid imaging technique with high spatial resolution and detection sensitivity, which can be combined with ultrasound (US) imaging to provide both optical and acoustic contrast. Elastography can noninvasively map the elasticity distribution of biological tissue, which reflects pathological conditions. In this study, we incorporated PA elastography into a commercial US/PA imaging system to develop a tri-modality imaging system, which has been tested for tumor detection using four mice with different physiological conditions. The results show that this tri-modality imaging system can provide complementary information on acoustic, optical, and mechanical properties. The enabled visualization and dimension estimation of tumors can lead to a more comprehensive tissue characterization for diagnosis and treatment.

Development and first implementation of a novel multi-modality cardiac motion and dosimetry phantom for radiotherapy applications.

BACKGROUND: Cardiac applications in radiation therapy are rapidly expanding including magnetic resonance guided radiation therapy (MRgRT) for real-time gating for targeting and avoidance near the heart or treating ventricular tachycardia (VT). PURPOSE: This work describes the development and implementation of a novel multi-modality and magnetic resonance (MR)-compatible cardiac phantom. METHODS: The patient-informed 3D model was derived from manual contouring of a contrast-enhanced Coronary Computed Tomography Angiography scan, exported as a Stereolithography model, then post-processed to simulate female heart with an average volume. The model was 3D-printed using Elastic50A to provide MR contrast to water background. Two rigid acrylic modules containing cardiac structures were designed and assembled, retrofitting to an MR-safe programmable motor to supply cardiac and respiratory motion in superior-inferior directions. One module contained a cavity for an ion chamber (IC), and the other was equipped with multiple interchangeable cavities for plastic scintillation detectors (PSDs). Images were acquired on a 0.35 T MR-linac for validation of phantom geometry, motion, and simulated online treatment planning and delivery. Three motion profiles were prescribed: patient-derived cardiac (sine waveform, 4.3 mm peak-to-peak, 60 beats/min), respiratory (cos4 waveform, 30 mm peak-to-peak, 12 breaths/min), and a superposition of cardiac (sine waveform, 4 mm peak-to-peak, 70 beats/min) and respiratory (cos4 waveform, 24 mm peak-to-peak, 12 breaths/min). The amplitude of the motion profiles was evaluated from sagittal cine images at eight frames/s with a resolution of 2.4 mm × 2.4 mm. Gated dosimetry experiments were performed using the two module configurations for calculating dose relative to stationary. A CT-based VT treatment plan was delivered twice under cone-beam CT guidance and cumulative stationary doses to multi-point PSDs were evaluated. RESULTS: No artifacts were observed on any images acquired during phantom operation. Phantom excursions measured 49.3 ± 25.8%/66.9 ± 14.0%, 97.0 ± 2.2%/96.4 ± 1.7%, and 90.4 ± 4.8%/89.3 ± 3.5% of prescription for cardiac, respiratory, and cardio-respiratory motion profiles for the 2-chamber (PSD) and 12-substructure (IC) phantom modules respectively. In the gated experiments, the cumulative dose was <2% from expected using the IC module. Real-time dose measured for the PSDs at 10 Hz acquisition rate demonstrated the ability to detect the dosimetric consequences of cardiac, respiratory, and cardio-respiratory motion when sampling of different locations during a single delivery, and the stability of our phantom dosimetric results over repeated cycles for the high dose and high gradient regions. For the VT delivery, high dose PSD was <1% from expected (5-6 cGy deviation of 5.9 Gy/fraction) and high gradient/low dose regions had deviations <3.6% (6.3 cGy less than expected 1.73 Gy/fraction). CONCLUSIONS: A novel multi-modality modular heart phantom was designed, constructed, and used for gated radiotherapy experiments on a 0.35 T MR-linac. Our phantom was capable of mimicking cardiac, cardio-respiratory, and respiratory motion while performing dosimetric evaluations of gated procedures using IC and PSD configurations. Time-resolved PSDs with small sensitive volumes appear promising for low-amplitude/high-frequency motion and multi-point data acquisition for advanced dosimetric capabilities. Illustrating VT planning and delivery further expands our phantom to address the unmet needs of cardiac applications in radiotherapy.