The impact of a novel deep learning reconstruction algorithm on image quality in ultralow-dose CT: a quantitative phantom study [0.03%]
一种新型深度学习重建算法对极低剂量CT图像质量影响的定量研究：基于体模的研究
Tong Su,Yongjun Jia,Yun Shen et al.
Tong Su et al.
Objective: The aim of this study is to evaluate the performance of a novel deep learning image reconstruction (DLIR) algorithm in noise reduction, contrast-to-noise ratio (CNR), and low iodine concentration detection for ...
Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence [0.03%]
基于人工智能的高分辨率磁共振图像视路整体分割
Andrea Diociasi,Emanuele Pravatà,Luca Carmisciano et al.
Andrea Diociasi et al.
Objective: Manual segmentation of the whole anterior visual pathway (aVP) from high-resolution magnetic resonance imaging (MRI) is time-consuming and prone to inter-rater variability. We developed and validated a fully au...
Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions [0.03%]
带有深度学习重建和化学偏移修正的零回波时间磁共振成像在检测溶骨性浆细胞瘤病灶中的应用
Darius Lepot,Caroline Chabot,Gaëtan Duchêne et al.
Darius Lepot et al.
Objective: We compared three magnetic resonance imaging (MRI) sequences-native zero echo time (ZTE), deep learning (DL)-chemical shift correction (CSC) reconstructed ZTE (ZTE-DLCSC), and gradient-echo black bone (BB)-in d...
Association between vertebral endplate lesions and sagittal spinal alignment: a retrospective imaging analysis in a selected adult patient population [0.03%]
椎体终板病变与脊柱矢状位对齐关系的回顾性研究
Tito Bassani,Riccardo Cecchinato,Sara Pasi et al.
Tito Bassani et al.
Objective: Most endplate lesions are asymptomatic and incidentally detected. Prevalence in adults ranges from 28% to 46%, but their clinical relevance remains unclear. A knowledge gap persists regarding how spinal alignme...
Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project [0.03%]
欧洲医学影像和放射肿瘤学教育中的人工智能课程的可访问、集中且可搜索数据库的发展：AIMIROE项目
Robin Decoster,Hendrik Erenstein,Jacob Menzinga et al.
Robin Decoster et al.
Objective: Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hinder adoption. This study, led by the European Society of Medical Imaging...
Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification [0.03%]
基于人工智能的自动化微CT器官分割工作流程的验证及其对荧光定量的影响
Elena Rama,Sihe Yu,Sarah Schraven et al.
Elena Rama et al.
Objective: Automated, artificial intelligence (AI)-based, organ segmentation has the potential to streamline preclinical imaging workflows, but its suitability must be evaluated not only by geometric accuracy, but also by...
Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction [0.03%]
基于深度学习重建的肌肉骨骼MRI分辨率和图像质量增强方法研究
Marco Porta,Giuseppe Agresti,Maria Marcella Laganà et al.
Marco Porta et al.
Objective: Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-to-noise ratio (SNR). We implemented deep learning reconstruction (DLR)...
Robert S Salkin,Arvind B Dev,Erica S Alexander et al.
Robert S Salkin et al.
Objective: We quantified volume and shape variability in lung microwave ablation (LMWA) zones, comparing them with expected ablation zones, exploring the correlation with tissue contraction. ...
Automated identification of MRI series using a hierarchical modular machine-learning pipeline [0.03%]
基于分层模块化机器学习流程的MRI序列自动识别方法
Mariusz J Kujawa,Matías Fernández-Patón,Leonor Cerdá Alberich et al.
Mariusz J Kujawa et al.
Objective: The volume and diversity of large MR imaging datasets require efficient automated labelling tools for cataloguing MR series, as manual annotation is impractical and costly. However, relying on DICOM header fiel...
Who labels best? Radiologists, rules, or large language models for CT reports on pulmonary embolism [0.03%]
谁是最佳标签提供者？放射科医生、规则还是大型语言模型在CT报告中检测肺栓塞？
Matthias A Fink,Arved Bischoff,Edem Atsiatorme et al.
Matthias A Fink et al.
Objective: To compare open-weight and proprietary large language models (LLMs), a rule-based extractor (RBE) and radiologists for labelling pulmonary embolism CT reports, and to test whether a hybrid RBE-LLM workflow impr...