Radiology

Clinically Explainable Disease Diagnosis Based on Biomarker Activation Map.

OBJECTIVE: Artificial intelligence (AI)-based disease classifiers have achieved specialist-level performances in several diagnostic tasks. However, real-world adoption of these classifiers remains challenging due to the black box issue. Here, we report a novel biomarker activation map (BAM) generation framework that can provide clinically meaningful explainability to current AI-based disease class...

Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders.

OBJECTIVE: Graph-based methods using resting-state functional magnetic resonance imaging demonstrate strong capabilities in modeling brain networks. However, existing graph-based methods often overlook inter-graph relationships, limiting their ability to capture the intrinsic features shared across individuals. Additionally, their simplistic integration strategies may fail to take full advantage o...

A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications.

MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Protocols ca...

60 kVp Coronary CT Angiography as a Screening Tool on Asymptomatic Patients: An Initial Experience.

PURPOSE: To investigate the feasibility of using 60 kVp coronary CT angiography (CCTA) combined with deep learning-based CT reconstruction as a screen...

Deep Learning Reconstruction Enables Diagnostic-Quality 0.4T Knee and Spine MRI in One-Third of the Time.

There has been a growing interest in low-field MRI due to its lower costs, enabling an increase in accessibility of MRI worldwide. Long scan times are...

Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.

This study introduces a domain-conditioned and temporally guided diffusion framework for accelerated dynamic MRI reconstruction, in which the reverse ...

Multiparametric MRI-based Deep Learning and Radiomics for Evaluating Lymph Node Metastasis in Early-Stage Cervical Cancer.

Purpose To develop a multiparametric MRI-based radiomics model and deep learning-radiomics (DLR) fusion model for preoperative prediction of lymph nod...

DEEP-DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration.

3D MR image acquisition is inherently time intensive, rendering it susceptible to patient motion during scanning. This may introduce significant blurr...

Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.

Purpose To simulate an artificial intelligence (AI)-driven triaging workflow in which an AI system, using high-confidence thresholds, assesses a subse...

Investigation of the Accuracy and Contributing Factors of AI-Based Diagnosis of Urothelial Carcinoma in Canine Abdominal Radiography.

Urothelial carcinoma (UC) is a highly malignant urinary cancer of the transitional epithelium in dogs. Recent advances in artificial intelligence (AI)...

Deep learning "super resolution" versus iterative reconstruction: Phantom-based image quality assessment.

BACKGROUND: Deep learning reconstruction (DLR) algorithms have begun replacing iterative reconstruction (IR) in CT. Besides the potential to reduce no...

A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.

BACKGROUND: Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial application...

Segmentation of uterus and myoma from MRI image based on deep learning.

OBJECTIVE: Myoma is a common gynecologic condition with abnormal muscular and fibrotic tissue growth in the uterus. Compared with sonography, magnetic...

Explainable Artificial Intelligence to Predict Neurocognitive Disorder Progression in Multiple Sclerosis Using MRI and Clinical Data.

BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS), yet the application of diagnostic frameworks of Neurocognitive Disorders (NCDs)...

Deep Learning-Based Prediction System for Surgical Difficulty in Rectal Cancer Patients Using MRI Pelvimetry.

PURPOSE: This study presents a system that automatically predicts the difficulty of laparoscopic total mesorectal excision (TME) using magnetic resona...

From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.

Three-dimensional reconstruction of cortical surfaces from MRI for subsequent morphometric analysis is fundamental for understanding brain structure. ...

Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.

BACKGROUND: The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy....

From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging.

Deep Learning-Based Augmented Contrast-Enhancement and Denoising for Reduced-Iodine and Low-Radiation 70-kVp Cerebral CT Angiography: A Prospective Study.

OBJECTIVE: To evaluate the feasibility of cerebral computed tomography angiography (CTA) obtained with reduced iodine and low radiation at 70 kVp and ...

Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.

BACKGROUND: Diffusion-weighted magnetic resonance imaging provides a non-invasive way to probe brain tissue microstructure and is widely used in neuro...