Radiology

Mapping intratumoral heterogeneity through PET-derived washout and deep learning after proton therapy.

The distribution of produced isotopes during proton therapy can be imaged with Positron Emission Tomography (PET) to verify dose delivery. However, biological washout, driven by tissue-dependent processes such as perfusion and cellular metabolism, reduces PET signal-to-noise ratio (SNR) and limits quantitative analysis. In this work, we propose an uncertainty-aware deep learning framework to impro...

LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.

Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of all the types of dementia. It is a gradually progressing condition which can begin with mild memory impairment and may lead to the inability to engage in meaningful conversations in the future. In addition to that, it is also capable of reacting app...

Beyond Conventional Imaging: From Time-Based to Data-Driven Decision-Making in Acute Ischemic Stroke.

Reperfusion therapy has profoundly transformed acute ischemic stroke (AIS) care. Initially, treatment decisions relied primarily on time from symptom ...

FDG PET in pulmonary sarcoidosis: An update and future aspects.

Pulmonary sarcoidosis is a heterogeneous granulomatous disease with an unpredictable clinical course, in which accurate assessment of disease activity...

Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer.

OBJECTIVE: Accurate preoperative assessment of muscle invasion in bladder cancer (BCa) guides therapy selection. However, MRI interpretation varies ac...

Ultrasound synthetic aperture imaging with SpecularNet for enhanced needle visualization.

PURPOSE: Ultrasound imaging has been routinely used for needle guidance due to its real-time capability and cost-efficiency. However, conventional ult...

Decipher-MR: a vision-language foundation model for 3D MRI representations.

Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, ge...

A hierarchical prompt and prototype learning framework for brain disorder classification.

Accurate diagnosis of brain disorders (BDs) is challenging in clinical practice. Most existing deep learning-based methods perform diagnosis only in a...

Lung cancer segmentation Using the Att-U-Net Model on PET-CT Images.

INTRODUCTION: Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the need for early and accurate diagnosis. Precise segm...

Preoperative functional connectivity patterns predict tremor relief following MRgFUS thalamotomy in essential tremor: A machine learning investigation.

BACKGROUND: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy made a breakthrough in treating essential tremor (ET), but with variable...

Artificial Intelligence-assisted Mining of Polyethylene Terephthalate Hydrolases.

Polyethylene terephthalate (PET) hydrolases efficiently hydrolyze the ester bonds in PET, converting it into valuable monomers or oligomers, offering ...

Deep learning for dentomaxillofacial cone-beam computed tomography enhancement: A systematic review and meta-analysis.

BACKGROUND: Cone-beam computed tomography (CBCT) is widely used in dentomaxillofacial imaging and radiotherapy but is limited by noise and low contras...

Acute spinal cord injury: contemporary management and novel therapies.

Acute spinal cord injury (SCI) remains a life-threatening condition associated with substantial morbidity, mortality, and long-term disability. Despit...

Computational models for predicting bone fracture healing: a review of modeling approaches, predictions, and emerging strategies.

BACKGROUND AND OBJECTIVE: The development of computational models for predicting bone fracture healing process holds strong potential to optimize ther...

Temporal AI-assisted compressed sensing for high-resolution, motion-robust small-bowel MR enterography without antiperistaltic agents: a feasibility study.

OBJECTIVES: To evaluate whether temporal AI-assisted compressed sensing (tACS) enables high-resolution, motion-robust magnetic resonance enterography ...

Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images.

INTRODUCTION: Accurate segmentation of the lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is essential for understanding cerebros...

Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma.

RATIONALE AND OBJECTIVE: The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan...

Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia.

BACKGROUND AND OBJECTIVE: Differentiating T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) is challenging. This study aims to cons...

A longitudinal and explainable 2.5D deep learning framework for Alzheimer's disease progression using ADNI MRI.

Early identification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), is important for timely clinical assessment...

Focused Ultrasound-activated Extracellular Vesicles-nanoparticle Hybrids for Targeted Brain Drug Delivery: Engineering Strategies and Translational Outlook.

A revolutionary method for targeted neurotherapeutics is focused ultrasound (FUS)-mediated blood-brain barrier (BBB) regulation. In order to enable co...