AIMC Topic: Magnetic Resonance Imaging

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Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification.

Sensors (Basel, Switzerland)
Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have tech...

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Frontiers in endocrinology
Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. (1) Develop...

Multi-dimensional predictions of psychotic symptoms via machine learning.

Human brain mapping
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have pr...

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Contrast media & molecular imaging
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications includin...

MR-Conditional Actuations: A Review.

Annals of biomedical engineering
Magnetic resonance imaging (MRI) is one of the most prevailing technologies to enable noninvasive and radiation-free soft tissue imaging. Operating a robotic device under MRI guidance is an active research area that has the potential to provide effic...

Spatial-channel relation learning for brain tumor segmentation.

Medical physics
PURPOSE: Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in magnetic resonance imaging data and linear fusion omitting semantic gaps between features in different branches remain challenging. We nee...

[Robot-controlled MRI-guided transrectal prostate biopsy, a promising technique].

Nederlands tijdschrift voor geneeskunde
At the so-called in-bore, MRI-guided prostate biopsy, the radiologist in the MRI suite manually directs a rectal biopsy needle guide at an abnormality confirmed by a previous prostate MRI. This manual technique of taking a biopsy is time-consuming an...

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Journal of magnetic resonance imaging : JMRI
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer appl...

A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Laboratory investigation; a journal of technical methods and pathology
Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratific...

MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: MRI of endolymphatic hydrops (EH) 4 h after intravenous administration of a single dose of gadolinium-based contrast agent is used for clinical examination in some institutions; however, further improvement in image quality would be valuable...