AIMC Topic: Brain Diseases

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Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Physics in medicine and biology
Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy ...

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

European radiology
OBJECTIVE: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

European radiology
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a m...

A computer vision-based system for monitoring Vojta therapy.

International journal of medical informatics
A neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body...

Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

Medical image analysis
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensiona...

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

Academic radiology
RATIONALE AND OBJECTIVES: Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural la...

A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy.

Journal of medical systems
This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 ...

Experimental new automatic tools for robotic stereotactic neurosurgery: towards "no hands" procedure of leads implantation into a brain target.

Journal of neural transmission (Vienna, Austria : 1996)
The use of robotics in neurosurgery and, particularly, in stereotactic neurosurgery, is becoming more and more adopted because of the great advantages that it offers. Robotic manipulators easily allow to achieve great precision, reliability, and rapi...

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

NeuroImage
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hund...

POETenceph - Automatic identification of clinical notes indicating encephalopathy using a realist ontology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Identifying inpatients with encephalopathy is important. The disorder is prevalent, often missed, and puts patients at risk. We describe POETenceph, natural language processing pipeline, which ranks clinical notes on the extent to which they indicate...