AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 3551 to 3560 of 6780 articles

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after r...

Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.

Journal of healthcare engineering
Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary m...

Deep learning for automatic segmentation of thigh and leg muscles.

Magma (New York, N.Y.)
OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach.

Support Vector Machine-based Spontaneous Intracranial Hypotension Detection on Brain MRI.

Clinical neuroradiology
BACKGROUND AND PURPOSE: To develop a fully automatic algorithm for the magnetic resonance imaging (MRI) identification of patients with spontaneous intracranial hypotension (SIH).

Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging.

NMR in biomedicine
Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often...

Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?

NeuroImage
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological proces...

A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.

Arthritis research & therapy
BACKGROUND: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics.

Human brain mapping
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional ...

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge.

Artificial intelligence in medicine
Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, thi...

Preliminary study of generalized semiautomatic segmentation for 3D voxel labeling of lesions based on deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: The three-dimensional (3D) voxel labeling of lesions requires significant radiologists' effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized ...