ArticleNPJ digital medicine2024
Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients.Quantitative imaging in medicine and surgery · 2026Article
- Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.Physical and engineering sciences in medicine · 2026Article
- Deep learning-based CT slice synthesis improves radiomic feature reproducibility and discriminative performance in lung nodule assessment.Insights into imaging · 2026Article
- TASC-SwinMT: Task-Adaptive Synergistic Cross-Task Swin Multi-Task Framework for CT and MRI Image Interpolation and Segmentation.Tomography (Ann Arbor, Mich.) · 2026Article
- 3DAD: Super-Resolution Image Synthesis from Anisotropic CT Images Using a Three-Dimensional Adversarial Diffusion Model.Bioengineering (Basel, Switzerland) · 2026Article
- Time-dependent Diffusion MRI for Predicting Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.Radiology. Imaging cancer · 2026Article
- CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.NPJ digital medicine · 2026Article
- Real-time reconstruction of 3D bone models via very-low-dose protocols.NPJ digital medicine · 2026Article
- CT image-based machine learning models for predicting blood eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease.BMC pulmonary medicine · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Development of a radiomics-vision transformer fusion model based on chest CT for predicting adverse respiratory events during recovery in elderly hip fracture patients under general anesthesia.Frontiers in medicine · 2026Article
- A self-training framework for semi-supervised pulmonary vessel segmentation and its application in COPD.Journal of X-ray science and technology · 2026Article
- Data-centric strategies enable cross-center generalization of convolutional neural networks for CT brain extraction.Frontiers in neurology · 2026Article
- GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation.NPJ digital medicine · 2025Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Gastrointestinal cancer: molecular pathogenesis and targeted therapy.Molecular biomedicine · 2025Review
- A Fully Automatic Pipeline of Identification, Segmentation, and Subtyping of Aortic Dissection from CT Angiography.Cardiovascular engineering and technology · 2025Article
- An interpretableFrontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists' accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.