AIMC Topic: Echo-Planar Imaging

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Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.

AJNR. American journal of neuroradiology Apr 2, 2025
BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high ac...
Echo-Planar Imaging Brain Neoplasms Male Middle Aged Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Artifacts Humans Brain Female
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Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.

European journal of radiology Feb 12, 2025
PURPOSE: This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) compared with conventional single-shot echo-planar imaging (ss-EPI) in 3 T breast MRI.
Humans Breast Neoplasms Middle Aged Deep Learning Breast Prospective Studies Female Aged Adult Echo-Planar Imaging Image Interpretation, Computer-Assisted Artifacts Diffusion Magnetic Resonance Imaging
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Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.

Investigative radiology Feb 7, 2025
OBJECTIVES: Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resol...
Brain Female Aged Adult Humans Prospective Studies Image Interpretation, Computer-Assisted Artifacts Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Male Middle Aged Deep Learning Image Processing, Computer-Assisted
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Diagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.

European journal of radiology Dec 20, 2024
OBJECTIVES: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.
Reference Standards Elasticity Imaging Techniques Echo-Planar Imaging Area Under Curve Biopsy Image Interpretation, Computer-Assisted Severity of Illness Index ROC Curve Liver Cirrhosis Support Vector Machine Magnetic Resonance Imaging Liver Principal Component Analysis Aged Adult Male Middle Aged Humans Retrospective Studies Female
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Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction.

Magnetic resonance imaging Nov 19, 2024
PURPOSE: BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of mo...
Humans Neural Networks, Computer Algorithms Brain Deep Learning Image Processing, Computer-Assisted Diffusion Magnetic Resonance Imaging Echo-Planar Imaging
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Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.

Journal of magnetic resonance imaging : JMRI Feb 16, 2024
BACKGROUND: For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.
Reproducibility of Results Aged Male Middle Aged Risk Assessment ROC Curve Magnetic Resonance Imaging Humans Biopsy Prostatic Neoplasms Machine Learning Image Interpretation, Computer-Assisted Prostate-Specific Antigen Retrospective Studies Diffusion Magnetic Resonance Imaging Prostate Echo-Planar Imaging
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Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding.

Magnetic resonance in medicine Dec 10, 2023
PURPOSE: To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications.
Echo-Planar Imaging Image Processing, Computer-Assisted Artifacts Phantoms, Imaging Algorithms Brain Deep Learning
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Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

Magnetic resonance imaging Nov 7, 2023
PURPOSE: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI).
Female Reproducibility of Results Middle Aged Deep Learning Echo-Planar Imaging Image Processing, Computer-Assisted Humans Retrospective Studies Spine Diffusion Magnetic Resonance Imaging
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Ultrafast Brain MRI Protocol at 1.5 T Using Deep Learning and Multi-shot EPI.

Academic radiology May 19, 2023
RATIONALE AND OBJECTIVES: To evaluate clinical feasibility and image quality of a comprehensive ultrafast brain MRI protocol with multi-shot echo planar imaging and deep learning-enhanced reconstruction at 1.5T.
Echo-Planar Imaging Humans Brain Deep Learning Magnetic Resonance Imaging Neuroimaging Diffusion Magnetic Resonance Imaging
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Ultrafast MRI using deep learning echoplanar imaging for a comprehensive assessment of acute ischemic stroke.

European radiology Mar 16, 2023
OBJECTIVES: Acute ischemic stroke (AIS) is an emergency requiring both fast and informative MR sequences. We aimed to assess the performance of an artificial intelligence-enhanced ultrafast (UF) protocol, compared to the reference protocol, in the AI...
Echo-Planar Imaging Magnetic Resonance Imaging Ischemic Stroke Humans Stroke Artificial Intelligence Diffusion Magnetic Resonance Imaging Deep Learning
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