Radiology

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Objective.While photon-counting computed tomography (PCCT) improves image quality and reduces radiation dose, artifacts induced by cardiac and respiratory motion is still a challenge. The purpose of this work is to evaluate the potential of an image-domain motion-artifact-correction method based on a deep-learning model that incorporates spectral information (material basis images).Approach.We sim...

Tomographic Foundation Model-FORCE: Flow-Oriented Reconstruction Conditioning Engine.

Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe noise and artifacts in reconstructed images, requiring improved reconstruction techniques. The introduction of deep learning has significantly advanced CT image reconstruction. However, obtaining paired training data remains...

Enhanced Magnetic Resonance Imaging-Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study.

BACKGROUND: Accurate segmentation of cartilage from magnetic resonance imaging (MRI) is crucial for the diagnosis and surgical planning of knee osteoa...

Artificial Intelligence in Point-of-Care Ultrasound.

Artificial intelligence (AI) is increasingly integrated into point-of-care ultrasound (POCUS) to enhance its utility in critical care settings. This m...

MTCL2 is essential for the bipolar-to-multipolar transition in the dendrite extension of cerebellar granule neurons.

The dynamic regulation of neuronal polarity is essential for the formation of neural networks during brain development. Primary cultures of rodent neu...

3DViT-GAT: a unified atlas-based 3D vision transformer and graph learning framework for major depressive disorder detection using structural MRI data.

Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Au...

Single capture quantitative oblique back-illumination microscopy.

Quantitative oblique back-illumination microscopy (qOBM) has emerged as a powerful technique for label-free, 3D quantitative phase imaging of arbitrar...

A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer.

The use of deep-learning (DL) models to support and automate medical imaging diagnostic procedures has become an ongoing focus of research and develop...

Multi-reader evaluation of deep learning-based auto-segmentation of eloquent brain arteriovenous malformation on MRA and white matter tractography in stereotactic radiosurgery.

OBJECTIVE: To minimize the radiation injury for white matter (WM) pathways during brain arteriovenous malformation (bAVM) stereotactic radiosurgery (S...

From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.

Bone tumors such as osteosarcoma and Ewing sarcoma remain among the most challenging cancers to diagnose and monitor because of their biological heter...

Learning 3-D Ultrasound Segmentation under Extreme Label Deficiency.

OBJECTIVE: 3-D ultrasound imaging has shown great promise in clinical diagnosis by offering comprehensive volumetric assessment of organs and anatomic...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Clinical Neuroimaging Over the Last Decade: Achievements and What Lies Ahead.

The past decade has witnessed notable advancements in clinical neuroimaging facilitated by technological innovations and significant scientific discov...

A Decade of Advancements in Musculoskeletal Imaging.

The past decade has witnessed remarkable advancements in musculoskeletal radiology, driven by increasing demand for medical imaging and rapid technolo...

Diagnostic and Technological Advances in Magnetic Resonance (Focusing on Imaging Technique and the Gadolinium-Based Contrast Media), Computed Tomography (Focusing on Photon Counting CT), and Ultrasound-State of the Art.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances...

Back to the Future-Cardiovascular Imaging From 1966 to Today and Tomorrow.

This article, on the 60th anniversary of the journal Investigative Radiology , a journal dedicated to cutting-edge imaging technology, discusses key h...

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Impression Generation on Multi-Institution and Multi-System Data.

Achieving clinical level performance and widespread deployment for generating radiology impressions encounters a giant challenge for conventional arti...

DINOMotion: Advanced Robust Tissue Motion Tracking With DINOv2 in 2D-Cine MRI-Guided Radiotherapy.

Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by r...

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.

PURPOSE: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with ...

DIAGNOSTIC PERFORMANCE OF MACHINE LEARNING TECHNOLOGY USING OPTICAL COHERENCE TOMOGRAPHIC IMAGE IN RETINAL DISEASES PRESENTED WITH SUBRETINAL FLUID.

PURPOSE: To study the diagnostic performance of machine learning in the diagnosis of three retinal diseases presented with subretinal fluid: central s...