AIMC Topic: Magnetic Resonance Imaging

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A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.

Magnetic resonance imaging
BACKGROUND: The classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for b...

Highly realistic simulation for robot-assisted hypothalamic hamartoma real-time MRI-guided laser interstitial thermal therapy (LITT).

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Real-time MRI-guided laser interstitial thermal therapy (LITT) is a challenging procedure due to its technical complexity, as well as the need for efficient multidisciplinary teamwork and transfer of an anesthetized patient between operating...

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BMC musculoskeletal disorders
BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiolog...

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis.

Annals of neurology
OBJECTIVE: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-ass...

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

BMC bioinformatics
BACKGROUND: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or...

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry.

Physics in medicine and biology
Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially...

Editorial for "Deep-Learning Detection of Cancer Metastasis to the Brain on MRI".

Journal of magnetic resonance imaging : JMRI
5 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:1237-1238.

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

NeuroImage. Clinical
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of ...

Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.

Neurobiology of aging
Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on...