Radiology

High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.

To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung MRI.Free-breathing lung MRI was conducted on eight healthy volunteers and ten patients with lung tumors on a 3 T MRI scanner using a 3D radial kooshba...

Quasi-supervised MR-CT image conversion based on unpaired data.

. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and treatment. However, simultaneous acquisition of MR and CT images is time-consuming, economically expensive, and involves ionizing radiation, which poses health risks to patients. The objective of this study is to generate CT images from radiation-free M...

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquis...

AI based automatic measurement of split renal function in [F]PSMA-1007 PET/CT.

BACKGROUND: Prostate-specific membrane antigen (PSMA) is an important target for positron emission tomography (PET) with computed tomography (CT) in p...

Can automation and artificial intelligence reduce echocardiography scan time and ultrasound system interaction?

BACKGROUND: The number of patients referred for and requiring a transthoracic echocardiogram (TTE) has increased over the years resulting in more card...

Canadian Radiology Update.

Radiology research at Canadian institutions is advancing patient care through multidisciplinary collaboration, technological innovation, and quality i...

Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.

Computed tomography (CT) is a cornerstone of abdominal imaging, playing a vital role in accurate diagnosis, appropriate treatment planning, and diseas...

Predicting mucosal healing in Crohn's disease: development of a deep-learning model based on intestinal ultrasound images.

OBJECTIVE: Predicting treatment response in Crohn's disease (CD) is essential for making an optimal therapeutic regimen, but relevant models are lacki...

A Semi-supervised Ultrasound Image Segmentation Network Integrating Enhanced Mask Learning and Dynamic Temperature-controlled Self-distillation.

Ultrasound imaging is widely used in clinical practice due to its advantages of no radiation and real-time capability. However, its image quality is o...

Identifying Clinical Research Priorities in Interventional Pulmonary: An Interventional Pulmonology Outcomes Group (IPOG) Working Group Report.

The field of Interventional Pulmonology suffers from a paucity of methodologically robust studies to inform patient care, often relying on retrospecti...

Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world w...

Multimodal personalization of transcranial direct current stimulation for modulation of sensorimotor integration.

Transcranial direct current stimulation (tDCS) for the modulation of smooth pursuit eye movements provides an ideal model for investigating sensorimot...

AN INNOVATIVE MACHINE LEARNING-BASED ALGORITHM FOR DIAGNOSING PEDIATRIC OVARIAN TORSION.

AIM: We aimed to develop a machine-learning(ML) algorithm consisting of physical examination, sonographic findings, and laboratory markers.

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result...

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PTSD agents, include psychedelics e.g. psilocybin, L...

ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation.

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges ...

A review: Lightweight architecture model in deep learning approach for lung disease identification.

As one of the leading causes of death worldwide, early detection of lung disease is a very important step to improve the effectiveness of treatment. B...

Predicting pulmonary hemodynamics in pediatric pulmonary arterial hypertension using cardiac magnetic resonance imaging and machine learning: an exploratory pilot study.

PURPOSE: Pulmonary arterial hypertension (PAH) significantly affects the pulmonary vasculature, requiring accurate estimation of mean pulmonary arteri...

Comparative analysis of pre-transcatheter aortic valve implantation CTA protocols: Optimizing radiation dose and contrast volume.

BACKGROUND: To establish the most effective and safe pre-transcatheter aortic valve implantation (TAVI) CT angiography (CTA) protocol by comparing two...

Application of Machine Learning to Breast MR Imaging.

The demand for breast imaging services continues to grow, driven by expanding indications in breast cancer diagnosis and treatment. This increasing de...