AIMC Topic: Magnetic Resonance Imaging

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Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study.

JMIR medical informatics
BACKGROUND: Liver fibrosis is a pathological outcome of chronic liver injury and a hallmark of multiple chronic liver diseases. Magnetic resonance elastography (MRE) provides a non-invasive modality for evaluating the severity of liver fibrosis.

Automated Multimodal Image Registration for Prostate Cancer Using Squeeze-and-Excitation ResNet with Thin Plate Spline Transformation: A Deep Learning Approach.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Accurate spatial correlation between preoperative prostate MRI and post-prostatectomy histopathology is critical for improving prostate cancer diagnosis, treatment planning, and MRI interpretation. Current manual registration methods are t...

Res-MoCoDiff: residual-guided diffusion models for motion artifact correction in brain MRI.

Physics in medicine and biology
Motion artifacts (ARTs) in brain magnetic resonance imaging (MRI), mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these ARTs, including repeated acquisitions or motion trackin...

Multiple Sclerosis Relapse Treatment During Pregnancy and Offspring Functional and Structural Neurodevelopment: A Cross-Sectional Study.

Neurology
BACKGROUND AND OBJECTIVES: High-dose methylprednisolone (MP) is the global standard for treating pregnancy-associated relapses in multiple sclerosis (MS). Given that glucocorticoids cross the placenta and may interfere with fetal brain development, c...

A modified deep learning approach for seminal vesicle region localization in prostate MRI.

Scientific reports
The seminal vesicle region plays a crucial role in male reproductive health, and its accurate evaluation is essential for diagnosing infertility and carcinoma. Magnetic resonance imaging (MRI) is the primary modality for assessment; however, manual e...

Morphometric similarity network-based graph convolutional networks for schizophrenia classification.

Scientific reports
Schizophrenia is a complex neuropsychiatric disorder characterized by significant heterogeneity, posing a challenge for accurate classification using neuroimaging data. Graph convolutional networks (GCNs) have emerged as a promising approach for leve...

Using magnetic resonance imaging-based subregional texture analysis models to classify knee osteoarthritis severity by compartment.

Scientific reports
We evaluated the effectiveness of magnetic resonance imaging (MRI)-based subregional texture analysis (TA) models for classifying knee osteoarthritis (OA) severity grades by compartment. We identified 122 MR images of 121 patients with knee OA (mild-...

Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.

Scientific reports
This study presents an automated system using Convolutional Neural Networks (CNNs) for segmenting FLAIR Magnetic Resonance Imaging (MRI) images to aid in the diagnosis of Multiple Sclerosis (MS). The dataset included 103 patients from Imam Khomeini H...

Mapping global research landscape and trends in magnetic resonance imaging applications for primary trigeminal neuralgia: a bibliometric analysis.

Neurosurgical review
While magnetic resonance imaging (MRI) has become indispensable in the clinical management of primary trigeminal neuralgia (PTN), a systematic analysis delineating the intellectual structure and evolving trends in this domain remains lacking. This st...

The ethics of simplification: balancing patient autonomy, comprehension, and accuracy in AI-generated radiology reports.

BMC medical ethics
BACKGROUND: Large language models (LLMs) such as GPT-4 are increasingly used to simplify radiology reports and improve patient comprehension. However, excessive simplification may undermine informed consent and autonomy by compromising clinical accur...