5,876 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
parameters -- such as the apparent transverse relaxation rate R2*, the
longitudinal relaxation rate R1 and the magnetisation transfer saturation --
that can be compared across sites and scanners and carry important information
about the underlying microstructure. The multi-parameter mapping (MPM) protocol
takes advantage of multi-echo acquisitions with variable flip angles to extract
these parameters in a clinically acceptable scan time. In this context,
ESTATICS performs a joint loglinear fit of multiple echo series to extract R2*
and multiple extrapolated intercepts, thereby improving robustness to motion
and decreasing the variance of the estimators. In this paper, we extend this
model in two ways: (1) by introducing a joint total variation (JTV) prior on
the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a
posteriori} estimate. We evaluated the proposed algorithm by predicting
left-out echoes in a rich single-subject dataset. In this validation, we
outperformed other state-of-the-art methods and additionally showed that the
proposed approach greatly reduces the variance of the estimated maps, without
introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI
202
Detection of Metabolites by Proton Ex Vivo NMR, in Vivo MR Spectroscopy Peaks and Tissue Content Analysis: Biochemical-Magnetic Resonance Correlation: Preliminary Results
*Aim*: Metabolite concentrations by in vivo magnetic resonance spectroscopy and ex vivo NMR spectroscopy were compared with excised normal human tissue relaxation times and tissue homogenate contents.

*Hypothesis*: Biochemical analysis combined with NMR and MR spectroscopy defines better tissue analysis.

*Materials and Methods*: Metabolites were measured using peak area, amplitude and molecular weights of metabolites in the reference solutions. In normal brain and heart autopsy, muscle and liver biopsy tissue ex vivo NMR peaks and spin-lattice (T1) and spin-spin (T2) relaxation times, were compared with diseased tissue NMR data in meningioma brain, myocardial infarct heart, duchene-muscular-dystrophy muscle and diffused-liver-injury liver after respective in vivo proton MR spectroscopy was done. NMR data was compared with tissue homogenate contents and serum levels of biochemical parameters.

*Results*: The quantitation of smaller NMR visible metabolites was feasible for both ex vivo NMR and in vivo MR spectroscopy. Ex vivo H-1 NMR and in vivo MRS metabolite characteristic peaks (disease/normal data represented as fold change), T1 and T2, and metabolites in tissue homogenate and serum indicated muscle fibrosis in DMD, cardiac energy depletion in MI heart, neuronal dysfunction in meningioma brain and carbohydrate-lipid metabolic crisis in DLI liver tissues.

*Conclusion*: This preliminary report highlights the biochemical-magnetic resonance correlation as basis of magnetic resonance spectroscopic imaging data interpretation of disease
Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided
High-Performance Bioinstrumentation for Real-Time Neuroelectrochemical Traumatic Brain Injury Monitoring
Traumatic brain injury (TBI) has been identified as an important cause of death and severe disability in all age groups and particularly in children and young adults. Central to TBIs devastation is a delayed secondary injury that occurs in 30–40% of TBI patients each year, while they are in the hospital Intensive Care Unit (ICU). Secondary injuries reduce survival rate after TBI and usually occur within 7 days post-injury. State-of-art monitoring of secondary brain injuries benefits from the acquisition of high-quality and time-aligned electrical data i.e., ElectroCorticoGraphy (ECoG) recorded by means of strip electrodes placed on the brains surface, and neurochemical data obtained via rapid sampling microdialysis and microfluidics-based biosensors measuring brain tissue levels of glucose, lactate and potassium. This article progresses the field of multi-modal monitoring of the injured human brain by presenting the design and realization of a new, compact, medical-grade amperometry, potentiometry and ECoG recording bioinstrumentation. Our combined TBI instrument enables the high-precision, real-time neuroelectrochemical monitoring of TBI patients, who have undergone craniotomy neurosurgery and are treated sedated in the ICU. Electrical and neurochemical test measurements are presented, confirming the high-performance of the reported TBI bioinstrumentation
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