J Hosp Palliat Nurs. 2025 Apr 2. doi: 10.1097/NJH.0000000000001120. Online ahead of print.

ABSTRACT

Underrepresented groups (URGs) in the United States, including African Americans, Latino/Hispanic Americans, Asian Pacific Islanders, and Native Americans, face significant barriers to accessing hospice and palliative care. Factors such as language barriers, cultural perceptions, and mistrust in healthcare systems contribute to the underutilization of these services. Recent advancements in artificial intelligence (AI) offer potential solutions to these challenges by enhancing cultural sensitivity, improving communication, and personalizing care. This article aims to synthesize the literature on AI in palliative/hospice care for URGs through the Technology Acceptance Model (TAM), highlighting current research and application in practice. The scoping review methodology, based on the framework developed by Arksey and O'Malley, was applied to rapidly map the field of AI in palliative and hospice care. A systematic search was conducted in 9 databases to identify studies examining AI applications in hospice and palliative care for URGs. Articles were independently assessed by 2 reviewers and then synthesized via narrative review through the lens of the TAM framework, which focuses on technology acceptance factors such as perceived ease of use and usefulness. Seventeen studies were identified. Findings suggest that AI has the potential to improve decision-making, enhance timely palliative care referrals, and bridge language and cultural gaps. Artificial intelligence tools were found to improve predictive accuracy, support serious illness communication, and assist in addressing language barriers, thus promoting equitable care for URGs. However, barriers such as limited generalizability, biases in data, and challenges in infrastructure were noted, hindering the full adoption of AI in hospice settings. Artificial intelligence has transformative potential to improve hospice care for URGs by enhancing cultural sensitivity, improving communication, and enabling more timely interventions. However, to fully realize its potential, AI solutions must address data biases, infrastructure limitations, and cultural nuances. Future research should prioritize developing culturally competent AI tools that are transparent, explainable, and scalable to ensure equitable access to hospice and palliative care services for all populations.

PMID:40179379 | DOI:10.1097/NJH.0000000000001120

PLoS Comput Biol. 2025 Apr 3;21(4):e1012881. doi: 10.1371/journal.pcbi.1012881. eCollection 2025 Apr.

ABSTRACT

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.

PMID:40179332 | DOI:10.1371/journal.pcbi.1012881

Anal Chem. 2025 Apr 3. doi: 10.1021/acs.analchem.4c05549. Online ahead of print.

ABSTRACT

Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy. However, most current methods only use 1-dimensional (1D) spectral data classification, instead of considering any neighboring information in space. Despite some successes, this type of methods wastes the 3-dimensional (3D) structure of Raman hyperspectral scans. Therefore, to investigate the feasibility of preserving the spatial information on Raman spectroscopy for data analysis, spatially aware deep learning algorithms were applied into a colorectal tissue data set with 3D Raman hyperspectral scans. This data set contains Raman spectra from normal, hyperplasia, adenoma, carcinoma tissues as well as artifacts. First, a modified version of 3D U-Net was utilized for segmentation; second, another convolutional neural network (CNN) using 3D Raman patches was utilized for pixel-wise classification. Both methods were compared with the conventional 1D CNN method, which worked as baseline. Based on the results of both epithelial tissue detection and colorectal cancer detection, it is shown that using spatially neighboring information on 3D Raman scans can increase the performance of deep learning models, although it might also increase the complexity of network training. Apart from the colorectal tissue data set, experiments were also conducted on a cholangiocarcinoma data set for generalizability verification. The findings in this study can also be potentially applied into future tasks regarding spectroscopic data analysis, especially for improving model performance in a spatially aware way.

PMID:40179245 | DOI:10.1021/acs.analchem.4c05549

PLoS One. 2025 Apr 3;20(4):e0320224. doi: 10.1371/journal.pone.0320224. eCollection 2025.

ABSTRACT

Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease transmission. Thus, effective classification of sandfly species and the corresponding sex identification are important for disease surveillance and control, managing breeding/populations, research and development, and conducting epidemiological studies. This is typically performed manually by observing internal morphological features, which maybe an error-prone tedious process. In this work, we developed a deep learning artificial intelligence system to determine the gender and to differentiate between three species of two sandfly subgenera (i.e., Phlebotomus alexandri, Phlebotomus papatasi, and Phlebotomus sergenti). Using locally field-caught and prepared samples over a period of two years, and based on convolutional neural networks, transfer learning, and early fusion of genital and pharynx images, we achieved exceptional classification accuracy (greater than 95%) across multiple performance metrics and using a wide range of pre-trained convolutional neural network models. This study not only contributes to the field of medical entomology by providing an automated and accurate solution for sandfly gender identification and taxonomy, but also establishes a framework for leveraging deep learning techniques in similar vector-borne disease research and control efforts.

PMID:40179129 | DOI:10.1371/journal.pone.0320224

PLoS One. 2025 Apr 3;20(4):e0319773. doi: 10.1371/journal.pone.0319773. eCollection 2025.

ABSTRACT

Enterprise risk management is a key element to ensure the sustainable and steady development of enterprises. However, traditional risk management methods have certain limitations when facing complex market environments and diverse risk events. This study introduces a deep learning-based risk management model utilizing the XGBoost-CNN-BiLSTM framework to enhance the prediction and detection of risk events. This model combines the structured data processing capabilities of XGBoost, the feature extraction capabilities of CNN, and the time series processing capabilities of BiLSTM to more comprehensively capture the key characteristics of risk events. Through experimental verification on multiple data sets, our model has achieved significant advantages in key indicators such as accuracy, recall, F1 score, and AUC. For example, on the S&P 500 historical data set, our model achieved a precision rate of 93.84% and a recall rate of 95.75%, further verifying its effectiveness in predicting risk events. These experimental results fully demonstrate the robustness and superiority of our model. Our research is of great significance, not only providing a more reliable risk management method for enterprises, but also providing useful inspiration for the application of deep learning in the field of risk management.

PMID:40179109 | DOI:10.1371/journal.pone.0319773

IEEE Trans Pattern Anal Mach Intell. 2025 Apr 3;PP. doi: 10.1109/TPAMI.2025.3557498. Online ahead of print.

ABSTRACT

Deep learning has significantly propelled the development of photometric stereo by handling the challenges posed by unknown reflectance and global illumination effects. However, how supervised learning-based photometric stereo networks resolve these challenges remains to be elucidated. In this paper, we aim to reveal how existing methods address these challenges by revisiting their deep features, deep feature encoding strategies, and network architectures. Based on the insights gained from our analysis, we propose ESSENCE-Net, which effectively encodes deep shading features with an easy-first-encoding strategy, enhances shading features with shading supervision, and accurately decodes normal with spatial context-aware attention. The experimental results verify that the proposed method outperforms state-of-the-art methods on three benchmark datasets, whether with dense or sparse inputs. The code is available at https://github.com/wxy-zju/ESSENCE-Net.

PMID:40178960 | DOI:10.1109/TPAMI.2025.3557498

IEEE Trans Med Imaging. 2025 Apr 3;PP. doi: 10.1109/TMI.2025.3557430. Online ahead of print.

ABSTRACT

Cephalometric landmark detection is essential for orthodontic diagnostics and treatment planning. Nevertheless, the scarcity of samples in data collection and the extensive effort required for manual annotation have significantly impeded the availability of diverse datasets. This limitation has restricted the effectiveness of deep learning-based detection methods, particularly those based on large-scale vision models. To address these challenges, we have developed an innovative data generation method capable of producing diverse cephalometric X-ray images along with corresponding annotations without human intervention. To achieve this, our approach initiates by constructing new cephalometric landmark annotations using anatomical priors. Then, we employ a diffusion-based generator to create realistic X-ray images that correspond closely with these annotations. To achieve precise control in producing samples with different attributes, we introduce a novel prompt cephalometric X-ray image dataset. This dataset includes real cephalometric X-ray images and detailed medical text prompts describing the images. By leveraging these detailed prompts, our method improves the generation process to control different styles and attributes. Facilitated by the large, diverse generated data, we introduce large-scale vision detection models into the cephalometric landmark detection task to improve accuracy. Experimental results demonstrate that training with the generated data substantially enhances the performance. Compared to methods without using the generated data, our approach improves the Success Detection Rate (SDR) by 6.5%, attaining a notable 82.2%. All code and data are available at: https://um-lab.github.io/cepha-generation/.

PMID:40178956 | DOI:10.1109/TMI.2025.3557430

Respir Care. 2025 Apr 3. doi: 10.1089/respcare.12540. Online ahead of print.

ABSTRACT

Patient-ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient respiratory effort. PVA is highly prevalent and associated with adverse clinical outcomes, including increased work of breathing, oxygen consumption, and risk of barotrauma. Artificial intelligence (AI) is a potentially transformative solution offering capabilities for automated detection of PVA. This narrative review characterizes the landscape of AI models designed for PVA detection and quantification. A comprehensive literature search identified 13 studies, spanning diverse settings and patient populations. Machine learning (ML) techniques, derivation datasets, types of asynchronies detected, and performance metrics were assessed to provide a contemporary view of AI in this domain. We reviewed 166 articles published between 1989 and April 2024, of which 13 were included, encompassing 332 participants and analyzing >5.8 million breaths. Patient counts ranged between 8 and 107 and breath data ranged between 1,375 and 4.2 M. The reason for invasive mechanical ventilation use was given as ARDS in three articles, whereas the remainder had different invasive mechanical ventilation indications. Various ML methods as well as newer deep learning techniques were used to address PVA types. Sensitivity and specificity of 10 of the 13 models were >0.9, and 8 models reported accuracy of >0.9. AI models have significant potential to address PVA in invasive mechanical ventilation, displaying high accuracy across various populations and asynchrony types. This showcases their potential to accurately detect and quantify PVA. Future work should focus on model validation in diverse clinical settings and patient populations.

PMID:40178919 | DOI:10.1089/respcare.12540

J Clin Invest. 2025 Apr 3:e180570. doi: 10.1172/JCI180570. Online ahead of print.

ABSTRACT

Hyaluronan (HA) in the extracellular matrix promotes epithelial-to-mesenchymal transition (EMT) and metastasis; however, the mechanism by which the HA network constructed by cancer cells regulates cancer progression and metastasis in the tumor microenvironment (TME) remains largely unknown. In this study, inter-alpha-trypsin inhibitor heavy chain 2 (ITIH2), an HA-binding protein, was confirmed to be secreted from mesenchymal-like lung cancer cells when co-cultured with cancer-associated fibroblasts. ITIH2 expression is transcriptionally upregulated by the EMT-inducing transcription factor ZEB1, along with HA synthase 2 (HAS2), which positively correlates with ZEB1 expression. Depletion of ITIH2 and HAS2 reduced HA matrix formation and the migration and invasion of lung cancer cells. Furthermore, ZEB1 facilitates alternative splicing and isoform expression of CD44, an HA receptor, and CD44 knockdown suppresses the motility and invasiveness of lung cancer cells. Using a deep learning-based drug-target interaction algorithm, we identified an ITIH2 inhibitor (sincalide) that inhibited HA matrix formation and migration of lung cancer cells, preventing metastatic colonization of lung cancer cells in mouse models. These findings suggest that ZEB1 remodels the HA network in the TME through the regulation of ITIH2, HAS2, and CD44, presenting a strategy for targeting this network to suppress lung cancer progression.

PMID:40178908 | DOI:10.1172/JCI180570

STAR Protoc. 2025 Apr 1;6(2):103721. doi: 10.1016/j.xpro.2025.103721. Online ahead of print.

ABSTRACT

Autotaxin (ATX), a secreted lysophospholipase D responsible for the extracellular production of the bioactive phospholipid lysophosphatidic acid (LPA), is a therapeutic target in idiopathic pulmonary fibrosis and pancreatic cancer, among other disorders, promoting the synthesis of novel ATX inhibitors. Here, we present a protocol for detecting and characterizing ATX inhibitors using a fluorometry-based microplate assay. We describe steps for a first screening of compounds, half-maximal inhibitory concentration (IC50) quantification of initial hits, screening for false positives, and identification of the hits' mode of inhibition. For complete details on the use and execution of this protocol, please refer to Stylianaki et al.1.

PMID:40178971 | DOI:10.1016/j.xpro.2025.103721

J Am Soc Mass Spectrom. 2025 Apr 3. doi: 10.1021/jasms.4c00467. Online ahead of print.

ABSTRACT

Targeted quantification metabolomics provides dynamic insights across various domains within the life sciences. Nevertheless, maintaining high-quality data obtained through liquid chromatography-mass spectrometry presents ongoing challenges. It is essential to develop normalization methods to correct for unwanted variations in metabolomic profiling such as batch effects and analytical drift. In this study, we assessed the normalization efficacy of Norm ISWSVR in targeted quantification metabolomics by comparing it with IS normalization and SERRF normalization. Consequently, Norm ISWSVR demonstrated exceptional efficacy in mitigating batch effects and reducing the relative standard deviation of quality control samples, in addition to correcting signal drift. Following normalization with Norm ISWSVR, the number of metabolites suitable for quantification increased with high precision. Collectively, Norm ISWSVR proves to be a robust and reliable method for enhancing data quality in targeted metabolomics, establishing itself as a promising approach for metabolomics research.

PMID:40179246 | DOI:10.1021/jasms.4c00467

Science. 2025 Apr 4;388(6742):52-59. doi: 10.1126/science.ads7951. Epub 2025 Apr 3.

ABSTRACT

Most phenotype-associated genetic variants map to noncoding regulatory regions of the human genome, but their mechanisms remain elusive in most cases. We developed a highly efficient strategy, Perturb-multiome, to simultaneously profile chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of master transcription factors (TFs). We examined the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within TF-sensitive accessible chromatin regions in erythroid differentiation, although representing <0.3% of the genome, show a ~100-fold enrichment for blood cell phenotype heritability, which is substantially higher than that for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

PMID:40179192 | DOI:10.1126/science.ads7951

J Agric Food Chem. 2025 Apr 3. doi: 10.1021/acs.jafc.5c00568. Online ahead of print.

ABSTRACT

Natural acylsucrose, often found in the glandular trichomes of Solanaceae plants, has potential applications in many industries, including food, cosmetics, and pharmaceuticals. In this study, we engineered an Escherichia coli strain to complete the biosynthesis of acylsucroses through whole-cell transformation. Using acylsucrose acyltransferases and CoA ligases, acylsucroses, including monoacylsucrose S1:5 ("S" represents an acylsucrose backbone, the number before the colon indicates the number of acyl chains, and the number after the colon indicates the sum of carbons in all acyl chains), diacylsucrose S2:10, triacylsucrose S3:14, and triacylsucrose S3:15 were synthesized from the substrate sucrose and short branched-chain fatty acids by the engineered E. coli EcoSE07, of which S3:15 was the primary product. Several strategies were applied to improve acylsucrose production, including codon optimization, constitutive promoter replacement, and serial resting cell assays. The use of fed-batch fermentation with an engineered E. coli strain of EcoSE22 containing a constitutive promoter further improved the production of acylsucroses. Serial resting cell assays with an optical density of 50 at 600 nm significantly increased the production of acylsucroses S3:15 and S2:10. These findings will facilitate the synthesis of natural acylsucroses through whole-cell transformations and provide the potential for future industrial applications.

PMID:40179051 | DOI:10.1021/acs.jafc.5c00568

Rev Med Suisse. 2025 Apr 2;21(912):660-663. doi: 10.53738/REVMED.2025.21.912.660.

ABSTRACT

In everyday practice, many patients experience reactions to substances from different drug families. Multiple Drug Hypersensitivity Syndrome (MDHS) and Multiple Drug Intolerance Syndrome (MDIS) both involve reactions to several unrelated drugs but differ in pathophysiology and clinical presentation. Rare and immune-mediated, MDHS involves T lymphocytes and presents with severe delayed exanthems, eosinophilia, and moderate hepatic cytolysis triggered by at least two chemically distinct drugs. In contrast, MDIS is a diagnosis of exclusion, characterized by adverse reactions-often pseudoallergic and immediate-to three or more unrelated drugs, occurring upon separate exposures with negative allergy tests.

PMID:40176614 | DOI:10.53738/REVMED.2025.21.912.660

Interdiscip Sci. 2025 Apr 3. doi: 10.1007/s12539-025-00698-3. Online ahead of print.

ABSTRACT

Identification of drug-target interactions (DTIs) is critical for drug discovery and drug repositioning. However, most DTI methods that extract features from drug molecules and protein entities neglect specific substructure information of pharmacological responses, which leads to poor predictive performance. Moreover, most existing methods are based on molecular graphs or molecular descriptors to obtain abstract representations of molecules, but combining the two feature learning methods for DTI prediction remains unexplored. Therefore, a new ASCS-DTI framework for DTI prediction is proposed, which utilizes a substructure attention mechanism to flexibly capture substructures of compounds at different grain sizes, allowing the important substructure information of each molecule to be learned. Additionally, the framework combines three different molecular fingerprinting information to comprehensively characterize molecular representations. A stacked convolutional coding module processes the sequence information of target proteins in a multi-scale and multi-level view. Finally, multi-modal fusion of molecular graph features and molecular fingerprint features, along with multi-modal information encoding of DTIs, is performed by the feature fusion module. The method outperforms six advanced baseline models on different benchmark datasets: Biosnap, BindingDB, and Human, with a significant improvement in performance, particularly in maintaining strong results across different experimental settings.

PMID:40178777 | DOI:10.1007/s12539-025-00698-3

Neurogenetics. 2025 Apr 3;26(1):41. doi: 10.1007/s10048-025-00822-x.

ABSTRACT

Neurodevelopmental, jaw, eye, and digital syndrome (NEDJED) is a rare autosomal dominant condition that has demonstrated diverse phenotypes. This is the second case report published on this condition, covering the disease history of an 8 year old patient with a severe manifestation of the disease. The patient was born with hydrocephalus, and demonstrated major developmental delay as he aged. Whole-genome sequencing of the patient and his parents was conducted, detecting a de novo variant, NM_001378974.1:c.1220 A > T [p.Lys407Ile], located in the conserved WD4 region of the WD40 domain of FBXW11, which is consistent with all previously reported patients. The phenotype of the patient is presented with a focus on MRI and EEG features, including images and detailed description for both. While the patient's phenotype is overall consistent with previous findings, there are a number of major factors we believe are caused by the FBXW11 variant that have not been previously described, such as the patient's complete inability to walk.

PMID:40178747 | DOI:10.1007/s10048-025-00822-x

J Insect Sci. 2025 Mar 14;25(2):8. doi: 10.1093/jisesa/ieaf019.

ABSTRACT

Taxonomy provides a general foundation for research on insects. Using stored product pest (SPP) arthropods as a model group, this article overviews the historical impacts of taxonomy on applied entomology. The article surveys the dynamics of historical descriptions of new species in various SPP taxa; the majority of all species (90%) were described prior to 1925, while the key pests were described prior to 1866. The review shows that process of describing new SPP species is not random but is influenced by following factors: (i) larger species tend to be described earlier than smaller and SPP moths and beetles are described earlier than psocids and mites; (ii) key economic pests are on average described earlier than less significant ones. Considering a species name as a "password" to unique information resources, this review also assesses the historical number of synonymous or duplicate names of SPP species. Pests belonging to some higher taxa Lepidoptera and Coleoptera has accumulated more scientific synonyms than those others belonging to Psocoptera and Acari. Number of synonyms positively correlated with the economic importance of SPP species. The review summarized semantic origin of SPP names showing minor proportion of names (17.6%) are toponyms (geography) or eponyms (people), while the majority (82.4%) fall into other categories (descriptive, etc.). It is concluded that awareness of taxonomic advances, including changes to species and higher taxa names, should be effectively communicated to pest control practitioners and applied entomology students, and specifically addressed in relevant textbooks, web media, and databases.

PMID:40178352 | DOI:10.1093/jisesa/ieaf019

Angew Chem Int Ed Engl. 2025 Apr 3:e202505714. doi: 10.1002/anie.202505714. Online ahead of print.

ABSTRACT

Antibiotic-resistant bacteria of the genus Pandoraea, frequently acquired from the environment, are an emerging cause of opportunistic respiratory infections, especially in cystic fibrosis (CF) patients. However, their specialized metabolites, including niche and virulence factors, remained unknown. Through genome mining of environmental and clinical isolates of diverse Pandoraea species, we identified a highly conserved biosynthesis gene cluster (pan) that codes for a non-ribosomal peptide synthetase (NRPS) assembling a new siderophore. Using bioinformatics-guided metabolic profiling of wild type and a targeted null mutant, we discovered the corresponding metabolites, pandorabactin A and B. Their structures and chelate (gallium) complexes were elucidated by a combination of chemical degradation, derivatization, NMR, and MS analysis. Metagenomics and bioinformatics of sputum samples of CF patients indicated that the presence of the pan gene locus correlates with the prevalence of specific bacteria in the lung microbiome. Bioassays and mass spectrometry imaging showed that pandorabactins have antibacterial activities against various lung pathogens (Pseudomonas, Mycobacterium, and Stenotrophomonas) through depleting iron in the competitors. Taken together, these findings offer first insight into niche factors of Pandoraea and indicate that pandorabactins shape the diseased lung microbiota through the competition for iron.

PMID:40178319 | DOI:10.1002/anie.202505714

Abdom Radiol (NY). 2025 Apr 3. doi: 10.1007/s00261-025-04921-z. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to preoperatively predict Ki-67 proliferation levels in patients with pancreatic solid pseudopapillary neoplasm (pSPN) using radiomics features extracted from arterial phase helical CT images.

METHODS: We retrospectively analyzed 92 patients (Ningbo Medical Center Lihuili Hospital: n = 64, Taizhou Central Hospital: n = 28) with pathologically confirmed pSPN from June 2015 to June 2023. Ki-67 positivity > 3% was considered high. Radiomics features were extracted using PyRadiomics, with patients from training cohort (n = 64) and validation cohort (n = 28). A radiomics signature was constructed, and a CT radiomics score (CTscore) was calculated. Deep learning models were employed for prediction, with early stopping to prevent overfitting.

RESULTS: Seven key radiomics features were selected via LASSO regression with cross-validation. The deep learning model demonstrated improved accuracy with demographics and CTscore, with key features such as Morphology and CTscore contributing significantly to predictive accuracy. The best-performing models, including GBM and deep learning algorithms, achieved high predictive performance with an AUC of up to 0.946 in the training cohort.

CONCLUSIONS: We developed a robust deep learning-based radiomics model using arterial phase CT images to predict Ki-67 levels in pSPN patients, identifying CTscore and Morphology as key predictors. This non-invasive approach has potential utility in guiding personalized preoperative treatment strategies.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40178588 | DOI:10.1007/s00261-025-04921-z

Radiol Cardiothorac Imaging. 2025 Apr;7(2):e240272. doi: 10.1148/ryct.240272.

ABSTRACT

Purpose To develop and evaluate a free-breathing, highly accelerated, multisection, single-beat cine sequence for cardiac MRI. Materials and Methods This prospective study, conducted from July 2022 to December 2023, included participants with various cardiac conditions as well as healthy participants who were imaged using a 3-T MRI system. A single-beat sequence was implemented, collecting data for each section in one heartbeat. Images were acquired with an in-plane spatiotemporal resolution of 1.9 × 1.9 mm2 and 37 msec and reconstructed using resolution enhancement generative adversarial inline neural network (REGAIN), a deep learning model. Multibreath-hold k-space-segmented (4.2-fold acceleration) and free-breathing single-beat (14.8-fold acceleration) cine images were collected, both reconstructed with REGAIN. Left ventricular (LV) and right ventricular (RV) parameters between the two methods were evaluated with linear regression, Bland-Altman analysis, and Pearson correlation. Three expert cardiologists independently scored diagnostic and image quality. Scan and rescan reproducibility was evaluated in a subset of participants 1 year apart using the intraclass correlation coefficient (ICC). Results This study included 136 participants (mean age [SD], 54 years ± 15; 69 female, 67 male), 40 healthy and 96 with cardiac conditions. k-Space-segmented and single-beat scan times were 2.6 minutes ± 0.8 and 0.5 minute ± 0.1, respectively. Strong correlations (P < .001) were observed between k-space-segmented and single-beat cine parameters in both LV (r = 0.97-0.99) and RV (r = 0.89-0.98). Scan and rescan reproducibility of single-beat cine was excellent (ICC, 0.97-1.0). Agreement among readers was high, with 125 of 136 (92%) images consistently assessed as diagnostic and 133 of 136 (98%) consistently rated as having good image quality by all readers. Conclusion Free-breathing 30-second single-beat cardiac cine MRI yielded accurate biventricular measurements, reduced scan time, and maintained high diagnostic and image quality compared with conventional multibreath-hold k-space-segmented cine images. Keywords: MR-Imaging, Cardiac, Heart, Imaging Sequences, Comparative Studies, Technology Assessment Supplemental material is available for this article. © RSNA, 2025.

PMID:40178397 | DOI:10.1148/ryct.240272