AIMC Topic: Magnetic Resonance Imaging

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A simultaneous multi-slice T mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction.

Magnetic resonance in medicine
PURPOSE: Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition...

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.

Sensors (Basel, Switzerland)
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone ...

Attention modulates neural representation to render reconstructions according to subjective appearance.

Communications biology
Stimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception...

Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus.

Investigative radiology
OBJECTIVE: This study aimed to evaluate the effects of deep learning (DL) reconstruction and a postprocessing sharpening filter on the image quality of single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) of the uterus.

Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system.

Scientific reports
Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC beca...

Deep robust residual network for super-resolution of 2D fetal brain MRI.

Scientific reports
Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR...

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

Journal of healthcare engineering
Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, b...

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

Generalizing deep learning brain segmentation for skull removal and intracranial measurements.

Magnetic resonance imaging
Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonance imaging (MRI). Detailed whole brain segmentation provides a non-invasive way to measure brain ...