Radiology

Optimizing acquisition time and injected dose in 18F-FDG PET/CT imaging using deep learning: enhancing image protocol efficiency and safety.

BACKGROUND: This study aims to evaluate the effectiveness of deep learning algorithms in simulating standard acquisition time images from shortened acquisition times in 18F-FDG PET/CT imaging, thereby optimizing both image quality and radiopharmaceutical use. METHODS: We evaluated 322 patients who underwent 18F-FDG PET/CT, simulating half-dose conditions and reconstructing images at acquisition ti...

Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.

OBJECTIVE: The objective was to develop prognostic models that included convolutional neural networks (CNN) derived from 18F-DCFPyL (PSMA) PET imaging of the primary tumor uptake patterns to prognose early metastatic progression after curative intent treatment for localized prostate cancer. METHODS: Due to the lack of sufficient cases with adequate follow-up and metastatic events to derive this mo...

Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study.

OBJECTIVE: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using...

Mamba2SVN: a Mamba2 and reconstruction-cooperative sensitivity refinement-based variational network for parallel MRI reconstruction.

OBJECTIVE: Magnetic resonance imaging (MRI) is widely used for its excellent soft-tissue contrast and non-ionizing nature, but its long acquisition ti...

Navigating PI-RADS v2.1 in clinical practice: pitfalls, variability, and the supportive role of AI.

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 has substantially advanced the standardization of prostate MRI acquisition, interpret...

Machine learning of multiparametric MRI radiomics preoperatively distinguishes testicular seminoma from non-seminoma: a multicenter study.

RATIONALE AND OBJECTIVES: This study aims to develop an optimal model for distinguishing seminoma from non-seminoma testicular tumors using machine le...

Deep learning for clinically significant prostate cancer detection on MRI: a systematic review, HSROC meta-analysis, and direct comparison with PI-RADS-based interpretation.

OBJECTIVES: To estimate patient-level diagnostic accuracy of deep learning (DL) for MRI-based detection of clinically significant prostate cancer (csP...

Differentiating advanced from non-advanced hepatic fibrosis: a hybrid deep learning-radiomics model leveraging synthetic contrast-enhanced CT from CycleGAN.

OBJECTIVES: This study aimed to develop and validate a hybrid deep learning-radiomics model that leveraged Cycle-consistent generative adversarial net...

Beyond scarring: a next-generation vision for pulmonary fibrosis management.

BACKGROUND: Interstitial pulmonary fibrosis remains a progressive, fatal lung disease with a median survival of approximately 4-5 years in contemporar...

Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.

CONTEXT: This paper examines how artificial intelligence (AI) is reshaping diagnostic decision-making in academic and clinical head and neck pathology...

Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.

Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. H...

Making Machine Learning Clinically Useful in Thrombosis and Hemostasis: A Roadmap for Diagnostic Translation.

Artificial intelligence (AI), most often in the form of machine learning (ML), attracts high expectations across medicine and is often discussed as a ...

Diagnostic yield and cost of three-level H&E sectioning in prostate biopsies.

AIM: The traditional three-level H&E sectioning protocol for prostate biopsies was developed for ultrasound-guided systematic sampling and predates le...

A Synthetic Data-Augmented Deep Learning Framework for Robust Segmentation and Quantification of the Carotid Artery in Ultrasound Images.

OBJECTIVE: To address the severe limitation imposed by the scarcity of annotated data on deep learning-based automated segmentation of the carotid art...

Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.

Lymph nodes (LN) constitute a vital component of the lymphatic system, serving a pivotal role in immune functioning and maintaining fluid balance in t...

An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population.

OBJECTIVES: Current deep learning models for early breast cancer lack interpretability and multimodal integration, limiting their clinical acceptance....

Imaging foundation model for universal enhancement of non-ideal measurement CT.

Non-ideal measurement computed tomography (CT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degra...

Physics-informed deep learning enables reliable and scalable organoid quantification for drug screening via OCT.

Patient-derived organoids (PDOs) hold transformative potential for personalized medicine by recapitulating patient-specific drug responses. While Opti...

Machine learning-based integration and comparison of ADC map radiomics with conventional imaging markers for cholesteatoma diagnosis.

PURPOSE: To compare the diagnostic performance of apparent diffusion coefficient (ADC) map-based radiomics with conventional CT and DWI for differenti...

Validation of a deep-learning based thrombus classifier on digital subtraction angiography using a large-scale dataset.

PURPOSE: Digital subtraction angiography (DSA) interpretation is observer dependent. This study evaluated the diagnostic performance of an existing de...