3,758 research outputs found
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Registration involving one or more images containing pathologies is
challenging, as standard image similarity measures and spatial transforms
cannot account for common changes due to pathologies. Low-rank/Sparse (LRS)
decomposition removes pathologies prior to registration; however, LRS is
memory-demanding and slow, which limits its use on larger data sets.
Additionally, LRS blurs normal tissue regions, which may degrade registration
performance. This paper proposes an efficient alternative to LRS: (1) normal
tissue appearance is captured by principal component analysis (PCA) and (2)
blurring is avoided by an integrated model for pathology removal and image
reconstruction. Results on synthetic and BRATS 2015 data demonstrate its
utility.Comment: Accepted as a conference paper for ISBI 201
Magnetic resonance imaging-guided radiation therapy using animal models of glioblastoma
Glioblastoma is the most aggressive and most common malignant primary brain tumour in adults and has a high mortality and morbidity. Because local tumour control in glioblastoma patients is still elusive in the majority of patients, there is an urgent need for alternative treatment strategies. However, to implement changes to the existing clinical standard of care, research must be conducted to develop alternative treatment strategies. A novel approach in radiotherapy is the introduction of pre-clinical precision image-guided radiation research platforms. The aim of this review is to give a brief overview of the efforts that have been made in the field of radiation research using animal models of glioblastoma. Because MRI has become the reference imaging technique for treatment planning and assessment of therapeutic responses in glioblastoma patients, we will focus in this review on small animal radiotherapy combined with MRI
Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI
Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patientβs treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa
Quantification of tumour heterogenity in MRI
Cancer is the leading cause of death that touches us all, either directly or indirectly.
It is estimated that the number of newly diagnosed cases in the Netherlands will increase
to 123,000 by the year 2020. General Dutch statistics are similar to those in
the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised
at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence
per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
The Integration of Positron Emission Tomography With Magnetic Resonance Imaging
A number of laboratories and companies are currently exploring the development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET). Scanners for both preclinical and human research applications are being pursued. In contrast to the widely distributed and now quite mature PET/computed tomography technology, most PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data. While this offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. This paper discusses the motivation for developing combined PET/MRI technology, outlines the obstacles in realizing such an integrated instrument, and presents recent progress in the development of both the instrumentation and of novel imaging agents for combined PET/MRI studies. The performance of the first-generation PET/MRI systems is described. Finally, a range of possible biomedical applications for PET/MRI are outlined
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR
We present USLR, a computational framework for longitudinal registration of
brain MRI scans to estimate nonlinear image trajectories that are smooth across
time, unbiased to any timepoint, and robust to imaging artefacts. It operates
on the Lie algebra parameterisation of spatial transforms (which is compatible
with rigid transforms and stationary velocity fields for nonlinear deformation)
and takes advantage of log-domain properties to solve the problem using
Bayesian inference. USRL estimates rigid and nonlinear registrations that: (i)
bring all timepoints to an unbiased subject-specific space; and (i) compute a
smooth trajectory across the imaging time-series. We capitalise on
learning-based registration algorithms and closed-form expressions for fast
inference. A use-case Alzheimer's disease study is used to showcase the
benefits of the pipeline in multiple fronts, such as time-consistent image
segmentation to reduce intra-subject variability, subject-specific prediction
or population analysis using tensor-based morphometry. We demonstrate that such
approach improves upon cross-sectional methods in identifying group
differences, which can be helpful in detecting more subtle atrophy levels or in
reducing sample sizes in clinical trials. The code is publicly available in
https://github.com/acasamitjana/uslrComment: Submitted to Medical Image Analysi
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