AI Medical Compendium Topic:
Magnetic Resonance Imaging

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MicroRNA signature for early prediction of knee osteoarthritis structural progression using integrated machine and deep learning approaches.

Osteoarthritis and cartilage
OBJECTIVE: Conventional methodologies are ineffective in predicting the rapid progression of knee osteoarthritis (OA). MicroRNAs (miRNAs) show promise as biomarkers for patient stratification. We aimed to develop a miRNA prognosis model for identifyi...

Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Journal of applied clinical medical physics
PURPOSE: Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and ...

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.

Journal of magnetic resonance (San Diego, Calif. : 1997)
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...

A novel deep learning based method for myocardial strain quantification.

Biomedical physics & engineering express
This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination.We measure the global and regional myocardial stra...

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

Tomography (Ann Arbor, Mich.)
: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging ...

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review.

Physics in medicine and biology
Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufa...

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.

Journal of X-ray science and technology
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping.

Computers in biology and medicine
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Sensors (Basel, Switzerland)
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnost...