Radiology

Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learn...

Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer.

OBJECTIVE: This study aimed to evaluate the value of habitat analysis based bi-modal ultrasound radiomics in predicting axillary lymph node (ALN) status in patients with early-stage breast cancer, and find a non-invasive and accurate method to predict ALN status.

Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study.

BACKGROUND: Vascular cognitive impairment (VCI) is prevalent but underdiagnosed due to its heterogeneous nature and the lack of reliable diagnostic to...

TFKT V2: task-focused knowledge transfer from natural images for computed tomography perceptual image quality assessment.

PURPOSE: The accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic reliability while minimizing radiation d...

Contrast-Enhanced Ultrasound for Hepatocellular Carcinoma Diagnosis- Expert Panel Narrative Review.

Despite growing clinical use of contrast-enhanced ultrasound (CEUS), inconsistency remains in the modality's role in clinical pathways for hepatocellu...

Image analysis research in neuroradiology: bridging clinical and technical domains.

PURPOSE: Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ...

Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer.

Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imagin...

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.

OBJECTIVE: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be ...

RadCLIP: Enhancing Radiologic Image Analysis Through Contrastive Language-Image Pretraining.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted ...

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly inc...

Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH.

OBJECTIVES: This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiogra...

Efficient feature extraction using light-weight CNN attention-based deep learning architectures for ultrasound fetal plane classification.

Ultrasound fetal imaging is beneficial to support prenatal development because it is affordable and non-intrusive. Nevertheless, fetal plane classific...

Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling.

Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling...

Incorporating organ deformation in biological modeling and patient outcome study for permanent prostate brachytherapy.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the ma...

Artificial Intelligence Augmented Cerebral Nuclear Imaging.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nucl...

Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disease characterized by demyelinating lesions in the central nervous system. Cross-sectional measur...

The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer.

Differentiation of adrenal incidentalomas (AIs) remains a challenge in the oncological setting. The aim of the study was to explore the diagnostic ef...

Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification.

: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagno...

Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes.

Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic c...

Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning.

BACKGROUND AND PURPOSE: Deep learning-based tumor tracking is promising for real-time magnetic-resonance-imaging (MRI)-guided radiotherapy. We investi...