Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomo...
This paper presents a hardware and software system to implement the task space control of an MR-conditional robot by integrating inductively coupled wireless coil based tracking feedback into the control loop. The main motivation of this work is to i...
Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance ...
International journal for numerical methods in biomedical engineering
Jun 19, 2019
Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments ...
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of bra...
Journal of neuroradiology = Journal de neuroradiologie
Jun 18, 2019
PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM).
IEEE journal of biomedical and health informatics
Jun 17, 2019
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the a...
Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep-learning methods are limited by the...
PURPOSE: To develop and evaluate a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high-quality accelerated real-time MR imaging.
BACKGROUND: Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. However, it is difficult to target specific brain regions accurately because the complex anatomy of the b...
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