AIMC Topic: Magnetic Resonance Imaging

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Artificial Intelligence in Prostate Cancer Diagnosis on Magnetic Resonance Imaging: Time for a New PARADIGM.

European urology
Artificial intelligence (AI) may provide a solution for improving access to expert, timely, and accurate magnetic resonance imaging (MRI) interpretation. The PARADIGM trial will provide level 1 evidence on the role of AI in the diagnosis of prostate ...

Self-supervised learning for label-free segmentation in cardiac ultrasound.

Nature communications
Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same problematic manual annotations. We build a pipeline for self-s...

Predicting the onset of mental health problems in adolescents.

Psychological medicine
OBJECTIVE: Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of mental health problems, but no tools are available to identify individuals at risk before they requ...

Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.

The international journal of cardiovascular imaging
Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neura...

Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.

Scientific reports
Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this ...

A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.

Scientific reports
Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...

Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.

European radiology experimental
BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.

Neurosurgical review
Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using...

Enhanced Brain Functional Interaction Following BCI-Guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, ...

Global-Local Feature Fusion Network Based on Nonlinear Spiking Neural Convolutional Model for MRI Brain Tumor Segmentation.

International journal of neural systems
Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doct...