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Magnetic Resonance Imaging

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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.

PloS one
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...

Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.

Magnetic resonance imaging
OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image qua...

Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.

Academic radiology
RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging ...

Differentiating Functional Connectivity Patterns in ADHD and Autism Among the Young People: A Machine Learning Solution.

Journal of attention disorders
OBJECTIVE: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of i...

Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.

Scientific reports
Peripheral artery disease (PAD) remains underdiagnosed and undertreated and is associated with an increased risk for adverse cardiovascular outcomes. Imaging provides an approach to identifying patients with PAD. However, the role of integrating imag...

Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.

Scientific reports
Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for pr...

Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP.

Scientific reports
To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed...

Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.

Thessaly Graft Index: An Artificial Intelligence-Based Index for the Assessment of Graft Integrity in ACL-Reconstructed Knees.

The Journal of bone and joint surgery. American volume
BACKGROUND: Magnetic resonance imaging (MRI) has proven to be a valuable noninvasive tool to evaluate graft integrity after anterior cruciate ligament (ACL) reconstruction. However, MRI protocols and interpretation methodologies are quite diverse, pr...

Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction.

Medical image analysis
Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed im...