Radiology

Use of artificial intelligence in analysis of endoscopic images to detect residual disease or regrowth in rectal patients with complete clinical response to neoadjuvant chemoradiotherapy.

BACKGROUND: Patients with rectal cancer who have a complete clinical response (cCR) to neoadjuvant chemo/radiotherapy (nCRT) may opt for organ preservation and watch and wait (W&W). This consists of an intense surveillance program including serial endoscopies, pelvic magnetic resonance imaging (MRI), carcinoembryonic antigen (CEA), and computed tomography (CT) scans to detect regrowth at an early ...

superLPNet: a super lightweight parameter deep learning model for brain age estimation from structural MRI.

OBJECTIVES: This study aimed to develop a super lightweight deep learning model for brain age estimation using structural MRI, enabling accurate age estimation with minimal computational cost for deployment in resource-constrained clinical settings. METHODS: A super lightweight brain age estimation network, termed superLPNet, was proposed. Lightweight convolutional structures inspired by MobileNet...

Review of Current Advances in Ultrasound Computed Tomography for Medical Imaging.

Ultrasound Computed Tomography (USCT) represents a paradigm shift in medical imaging, offering quantitative, high-resolution tissue characterization f...

Application of a conversational AI system for voice-interactive history taking in neurosurgical patients.

BACKGROUND: ccurate and structured medical history taking is essential in neurosurgical practice, but repetitive inpatient interviews can be time-cons...

Potential and challenges of generative adversarial networks for super-resolution in 4D flow MRI.

Time-resolved three-dimensional phase-contrast MRI (4D Flow MRI) enables non-invasive quantification of blood flow and derivation of hemodynamic param...

CT-less TOF PET: challenges and innovations for quantitative imaging.

A decade has passed since the groundbreaking work by Defrise et al. (2012), which demonstrated that TOF PET imaging is self-correcting for a variety o...

Achieving Ultra-High Acceleration Rates in 7T MRI Using Combined Controlled Aliasing in Parallel Imaging and Compressed Sensing with Deep-Learning-Based Image Reconstruction.

BACKGROUND AND PURPOSE: Clinical adoption of 7T MRI has been limited by lengthy acquisitions. Acceleration techniques, such as controlled aliasing in ...

Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation.

The rapid advancements in PET technology, coupled with the need for accurate and efficient imaging, necessitate the development of robust and generali...

Dual-Phase Computed Tomography-Based Deep Learning Architecture for Three-Year Survival Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a major global health burden, ranking as the sixth most common cancer and the third leading cause of cancer-related ...

Development and validation of a novel deep learning coronary artery plaque quantification model.

BACKGROUND: Coronary Computed Tomography Angiography (CCTA) is an established tool for assessing coronary artery disease. CCTA determined total corona...

Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3/T4 stage gastric cancer.

Gastric cancer, prevalent in East Asia, often presents with peritoneal metastasis at diagnosis, limiting surgical options and reducing survival rates....

Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.

OBJECTIVES: To develop and validate a deep learning-based model capable of generating dopamine transporter (DAT) images from early-phase [18F]-FP-CIT ...

Continuous-time causal distribution learning with identifiability for brain dynamic effective connectivity inference.

Dynamic effective connectivity (dEC) analysis provides an approach for revealing the causal mechanism of information transmission in human brain. Howe...

Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction.

AIMS: Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic pe...

Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.

Artificial intelligence (AI) holds significant promise for transforming cerebral infarction care, yet its real-world performance across the entire dis...

Real-Time Artificial Intelligence Assistance in Neuroendovascular Therapy: Comparative Analysis of Elective and Emergency Procedures.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has emerged as an adjunct in neuroendovascular interventions; however, its clinical utility re...

Anatomy Guided Truncated Conditional Diffusion Model for Super-Resolution Arterial Spin Labeling Imaging.

Arterial Spin Labeling (ASL) is a non-invasive magnetic resonance imaging technique used to measure cerebral blood flow. However, ASL images typically...

A proof-of-principle study of tractography-based machine learning for predicting transcranial magnetic stimulation motor responsiveness.

BACKGROUND: Identifying the brain stimulation target is fundamental for transcranial magnetic stimulation (TMS). Currently, this process is time-consu...

Deep learning for detection and automatic visualization of radiation-induced temporal lobe injury in nasopharyngeal carcinoma across endemic and non-endemic areas in China.

PURPOSE: Detection of radiation-induced temporal lobe injury (RTLI) at the earliest radiologically detectable stage is important for timely interventi...

MRI-based radiomics explainable model for predicting recurrence of limb chronic osteomyelitis in limb bones treated by Masquelet technique.

AIMS: This study aimed to develop a machine learning model for predicting chronic osteomyelitis recurrence (COR) following the Masquelet technique (MT...