AIMC Topic: Magnetic Resonance Imaging

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Automatic brain tissue segmentation in fetal MRI using convolutional neural networks.

Magnetic resonance imaging
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segment...

Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...

Dual-domain convolutional neural networks for improving structural information in 3 T MRI.

Magnetic resonance imaging
We propose a novel dual-domain convolutional neural network framework to improve structural information of routine 3 T images. We introduce a parameter-efficient butterfly network that involves two complementary domains: a spatial domain and a freque...

Machine learning in resting-state fMRI analysis.

Magnetic resonance imaging
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. W...

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.

Magnetic resonance in medicine
PURPOSE: To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern ...

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned.

Magnetic resonance imaging
The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a ...

Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.

Human brain mapping
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, b...

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends.

Seminars in musculoskeletal radiology
Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After...

Atrophy of cerebellar peduncles in essential tremor: a machine learning-based volumetric analysis.

European radiology
BACKGROUND: Subtle cerebellar signs are frequently observed in essential tremor (ET) and may be associated with cerebellar dysfunction. This study aims to evaluate the macrostructural integrity of the superior, middle, and inferior cerebellar peduncl...

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Human brain mapping
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be...