Radiology

Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study.

PURPOSE: While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F.

Machine learning to identify suitable boundaries for band-pass spectral analysis of dynamic [ C]Ro15-4513 PET scan and voxel-wise parametric map generation.

BACKGROUND: Spectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injection. Band-pass spectral analysis, considering specific frequency ranges, enables calculation of separate parametric maps of receptor subtype tracer binding for suitable radiopharmaceuticals such as [ C]Ro15-4513 binding to GABA 1/5 subunits. Frequen...

Automated MRI protocoling in neuroradiology in the era of large language models.

PURPOSE: This study investigates the automation of MRI protocoling, a routine task in radiology, using large language models (LLMs), comparing an open...

Artificial Intelligence for Low-Dose CT Lung Cancer Screening: Comparison of Utilization Scenarios.

. Artificial intelligence (AI) tools for evaluating low-dose CT (LDCT) lung cancer screening examinations are used predominantly for assisting radiolo...

Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network.

Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration o...

Multiparametric ultrasound techniques are superior to AI-assisted ultrasound for assessment of solid thyroid nodules: a prospective study.

OBJECTIVES: To evaluate the diagnostic performance of multiparametric ultrasound (mpUS) and AI-assisted B-mode ultrasound (AI-US), and their potential...

Flow-Rate-Constrained Physics-Informed Neural Networks for Flow Field Error Correction in Four-Dimensional Flow Magnetic Resonance Imaging.

In this study, we present enhanced physics-informed neural networks (PINNs), which were designed to address flow field errors in four-dimensional flow...

Acute Management of Nasal Bone Fractures: A Systematic Review and Practice Management Guideline.

Nasal bone fractures represent the most common facial skeletal injury, challenging both function and aesthetics. This Preferred Reporting Items for Sy...

Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence.

There is a pressing need for improved standardization of terminology and data in nuclear medicine. The field is experiencing unprecedented growth, dri...

PediMS: A Pediatric Multiple Sclerosis Lesion Segmentation Dataset.

Multiple Sclerosis (MS) is a chronic autoimmune disease that primarily affects the central nervous system and is predominantly diagnosed in adults, ma...

Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations.

Super-resolution imaging has emerged as a rapidly advancing field in diagnostic ultrasound. Ultrasound Localization Microscopy (ULM) achieves sub-wave...

Intratumoral and peritumoral radiomics based on 2D ultrasound imaging in breast cancer was used to determine the optimal peritumoral range for predicting KI-67 expression.

OBJECTIVES: Currently, radiomics focuses on intratumoral regions and fixed peritumoral regions, and lacks an optimal peritumoral region taken to predi...

Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer.

With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary stag...

FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial ...

MRI sequence focused on pancreatic morphology evaluation: three-shot turbo spin-echo with deep learning-based reconstruction.

BackgroundHigher-resolution magnetic resonance imaging sequences are needed for the early detection of pancreatic cancer.PurposeTo compare the quality...

Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models.

OBJECTIVE: As increased nuchal translucency (NT) thickness is notably associated with fetal chromosomal abnormalities, structural defects, and genetic...

BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability.

Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians ass...

From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression.

BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patt...

Predicting the sonication energy for focused ultrasound surgery treatment of breast fibroadenomas using machine learning algorithms.

PURPOSE: To establish a predictive model for the sonication energy required for focused ultrasound surgery (FUS) of breast fibroadenomas.

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is tra...