A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal.

Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of clinical diagnosis and cause errors in automated image analysis. In this work, we present a method for generating realistic motion artefacts from artefact-free magnitude MRI data to be used in deep learning frameworks, increasing training appearance variability and ultimately making machine learning algorithms such as convolutional neural networks (CNNs) more robust to the presence of motion artefacts.

Basal forebrain atrophy in frontotemporal dementia.

Background: The basal forebrain is a subcortical structure that plays an important role in learning, attention, and memory. Despite the known subcortical involvement in frontotemporal dementia (FTD), there is little research into the role of the basal forebrain in this disease. We aimed to investigate differences in basal forebrain volumes between clinical, genetic, and pathological diagnoses of FTD.

Disrupted principal network organisation in multiple sclerosis relates to disability.

Structural network-based approaches can assess white matter connections revealing topological alterations in multiple sclerosis (MS). However, principal network (PN) organisation and its clinical relevance in MS has not been explored yet. Here, structural networks were reconstructed from diffusion data in 58 relapsing-remitting MS (RRMS), 28 primary progressive MS (PPMS), 36 secondary progressive (SPMS) and 51 healthy controls (HCs).

Computer-assisted planning for minimally invasive anterior two-thirds laser corpus callosotomy: A feasibility study with probabilistic tractography validation.

Background: Anterior two-thirds corpus callosotomy is an effective palliative neurosurgical procedure for drug-refractory epilepsy that is most commonly used to treat drop-attacks. Laser interstitial thermal therapy is a novel stereotactic ablative technique that has been utilised as a minimally invasive alternative to resective and disconnective open neurosurgery. Case series have reported success in performing laser anterior two-thirds corpus callosotomy.

Efficacy of three neuroprotective drugs in secondary progressive multiple sclerosis (MS-SMART): a phase 2b, multiarm, double-blind, randomised placebo-controlled trial.

Background: Neurodegeneration is the pathological substrate that causes major disability in secondary progressive multiple sclerosis. A synthesis of preclinical and clinical research identified three neuroprotective drugs acting on different axonal pathobiologies. We aimed to test the efficacy of these drugs in an efficient manner with respect to time, cost, and patient resource.

Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy.

Spinal cord atrophy measurements obtained from structural magnetic resonance imaging (MRI) are associated with disability in many neurological diseases and serve as in vivo biomarkers of neurodegeneration. Longitudinal spinal cord atrophy rate is commonly determined from the numerical difference between two volumes (based on 3D surface fitting) or two cross-sectional areas (CSA, based on 2D edge detection) obtained at different time-points. Being an indirect measure, atrophy rates are susceptible to variable segmentation errors at the edge of the spinal cord.

Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis.

Objective: Hippocampal sclerosis (HS) is the most common cause of drug-resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross-sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities.

Minimally invasive photoacoustic imaging: Current status and future perspectives.

Photoacoustic imaging (PAI) is an emerging biomedical imaging modality that is based on optical absorption contrast, capable of revealing distinct spectroscopic signatures of tissue at high spatial resolution and large imaging depths. However, clinical applications of conventional non-invasive PAI systems have been restricted to examinations of tissues at depths less than a few cm due to strong light attenuation. Minimally invasive photoacoustic imaging (miPAI) has greatly extended the landscape of PAI by delivering excitation light within tissue through miniature fibre-optic probes.

White matter hyperintensities in progranulin-associated frontotemporal dementia: A longitudinal GENFI study.

Frontotemporal dementia (FTD) is a heterogeneous group of neurodegenerative disorders with both sporadic and genetic forms. Mutations in the progranulin gene (GRN) are a common cause of genetic FTD, causing either a behavioural presentation or, less commonly, language impairment. Presence on T2-weighted images of white matter hyperintensities (WMH) has been previously shown to be more commonly associated with GRN mutations rather than other forms of FTD.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques.

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person.

Improved fetal blood oxygenation and placental estimated measurements of diffusion-weighted MRI using data-driven Bayesian modeling.

Purpose: Motion correction in placental DW-MRI is challenging due to maternal breathing motion, maternal movements, and rapid intensity changes. Parameter estimates are usually obtained using least-squares methods for voxel-wise fitting; however, they typically give noisy estimates due to low signal-to-noise ratio. We introduce a model-driven registration (MDR) technique which incorporates a placenta-specific signal model into the registration process, and we present a Bayesian approach for Diffusion-rElaxation Combined Imaging for Detailed placental Evaluation model to obtain individual and population trends in estimated parameters.

Publisher Correction: Early neuropathological and neurobehavioral consequences of preterm birth in a rabbit model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Altered brain connectivity in sudden unexpected death in epilepsy (SUDEP) revealed using resting-state fMRI.

The circumstances surrounding SUDEP suggest autonomic or respiratory collapse, implying central failure of regulation or recovery. Characterisation of the communication among brain areas mediating such processes may shed light on mechanisms and noninvasively indicate risk. We used rs-fMRI to examine network properties among brain structures in people with epilepsy who suffered SUDEP (n = 8) over an 8-year follow-up period, compared with matched high- and low-risk subjects (n = 16/group) who did not suffer SUDEP during that period, and a group of healthy controls (n = 16).

An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI.

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains.

A 30-Year Clinical and Magnetic Resonance Imaging Observational Study of Multiple Sclerosis and Clinically Isolated Syndromes.

Objective: Clinical outcomes in multiple sclerosis (MS) are highly variable. We aim to determine the long-term clinical outcomes in MS, and to identify early prognostic features of these outcomes.

Methods: One hundred thirty-two people presenting with a clinically isolated syndrome were prospectively recruited between 1984 and 1987, and followed up clinically and radiologically 1, 5, 10, 14, 20, and now 30 years later.

Learning to see the invisible: A data-driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy.

Objective: To find the covert patterns of abnormality in patients with unilateral temporal lobe epilepsy (TLE) and visually normal brain magnetic resonance images (MRI-negative), comparing them to those with visible abnormalities (MRI-positive).

Methods: We used multimodal brain MRI from patients with unilateral TLE and employed contemporary machine learning methods to predict the known laterality of seizure onset in 104 subjects (82 MRI-positive, 22 MRI-negative). A visualization approach entitled "Importance Maps" was developed to highlight image features predictive of seizure laterality in both the MRI-positive and MRI-negative cases.

Age and hippocampal volume predict distinct parts of default mode network activity.

Group comparison studies have established that activity in the posterior part of the default-mode network (DMN) is down-regulated by both normal ageing and Alzheimer's disease (AD). In this study linear regression models were used to disentangle distinctive DMN activity patterns that are more profoundly associated with either normal ageing or a structural marker of neurodegeneration. 312 datasets inclusive of healthy adults and patients were analysed.

Micro-CT and histological investigation of the spatial pattern of feto-placental vascular density.

Introduction: There are considerable variations in villous morphology within a normal placenta. However, whether there is a reproducible spatial pattern of variation in villous vascular density is not known. Micro-CT provides three-dimensional volume imaging with spatial resolution down to the micrometre scale.

Photoacoustic imaging of the human placental vasculature.

Minimally invasive fetal interventions require accurate imaging from inside the uterine cavity. Twin-to-twin transfusion syndrome (TTTS), a condition considered in this study, occurs from abnormal vascular anastomoses in the placenta that allow blood to flow unevenly between the fetuses. Currently, TTTS is treated fetoscopically by identifying the anastomosing vessels, and then performing laser photocoagulation.

The Effect of Vascular Segmentation Methods on Stereotactic Trajectory Planning for Drug-Resistant Focal Epilepsy: A Retrospective Cohort Study.

Background: Stereotactic neurosurgical procedures carry a risk of intracranial hemorrhage, which may result in significant morbidity and mortality. Vascular imaging is crucial for planning stereotactic procedures to prevent conflicts with intracranial vasculature. There is a wide range of vascular imaging methods used for stereoelectroencephalography (SEEG) trajectory planning.

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks.

Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model () and image-based () uncertainties.

Handheld Active Add-On Control Unit for a Cable-Driven Flexible Endoscope.

The instruments currently used by surgeons for treatment of the twin-to-twin transfusion syndrome (TTTS) are rigid or semi-rigid. Their poor dexterity makes this surgical intervention risky and the surgeon's work very complex. This paper proposes the design, assembly and quantitative evaluation of an add-on system intended to be placed on a commercialized cable-driven flexible endoscope.