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Conference paperDuan J, Schlemper J, Bai W, et al., 2018,
Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images
, MICCAI ShapeMI Workshop, Publisher: Springer Verlag, Pages: 258-267, ISSN: 0302-9743In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes.
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Journal articleHaijen ECHM, Kaelen M, Roseman L, et al., 2018,
Predicting responses to psychedelics: a prospective study
, Frontiers in Pharmacology, Vol: 9, ISSN: 1663-9812Responses to psychedelics are notoriously difficult to predict, yet significant work is currently underway to assess their therapeutic potential and the level of interest in psychedelics among the general public appears to be increasing. We aimed to collect prospective data in order to improve our ability to predict acute- and longer-term responses to psychedelics. Individuals who planned to take a psychedelic through their own initiative participated in an online survey (www.psychedelicsurvey.com). Traits and variables relating to set, setting and the acute psychedelic experience were measured at five different time points before and after the experience. Principle component and regression methods were used to analyse the data. Sample sizes for the five time points included N= 654, N= 535, N= 379, N= 315, and N= 212 respectively. Psychological well-being was increased two weeks after a psychedelic experience and remained at this level after four weeks. This increase was larger for individuals who scored higher for a ‘mystical-type experience’, and smaller for those who scored higher for ‘challenging experience’. Having ‘clear intentions’ for the experience was conducive to mystical-type experiences. Having a positive ‘set’, as well as having the experience with intentions related to ‘recreation’, were both found to decrease the likelihood of having a challenging experience. The trait ‘absorption’ and higher drug doses promoted both mystical-type and challenging experiences. When comparing different types of variables, traits variables seemed to explain most variance in the change in well-being after a psychedelic experience. These results confirm the importance of extra-pharmacological factors in determining responses to a psychedelic. We view this study as an early step towards the development of empirical guidelines that can evolve and improve iteratively with the ultimate purpose of guiding
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Journal articleRoberts R, Ahmad H, Patel M, et al., 2018,
An fMRI study of visuo-vestibular interaction in Vestibular Neuritis
, NeuroImage: Clinical, Vol: 20, Pages: 1010-1017, ISSN: 2213-1582Vestibular neuritis (VN) is characterised by acute vertigo due to a sudden loss of unilateral vestibular function. A considerable proportion of VN patients proceed to develop chronic symptoms of dizziness, including visually induced dizziness, specifically during head turns. Here we investigated whether the development of such poor clinical outcomes following VN, is associated with abnormal visuo-vestibular cortical processing. Accordingly, we applied functional magnetic resonance imaging to assess brain responses of chronic VN patients and compared these to controls during both congruent (co-directional) and incongruent (opposite directions) visuo-vestibular stimulation (i.e. emulating situations that provoke symptoms in patients). We observed a focal significant difference in BOLD signal in the primary visual cortex V1 between patients and controls in the congruent condition (small volume corrected level of p < .05 FWE). Importantly, this reduced BOLD signal in V1 was negatively correlated with functional status measured with validated clinical questionnaires. Our findings suggest that central compensation and in turn clinical outcomes in VN are partly mediated by adaptive mechanisms associated with the early visual cortex.
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Journal articleOliveira V, Martins R, Liow N, et al., 2018,
Prognostic accuracy of heart rate variability analysis in neonatal encephalopathy: a systematic review
, Neonatology, Vol: 115, Pages: 59-67, ISSN: 1661-7800BACKGROUND: Heart rate variability analysis offers real-time quantification of autonomic disturbance after perinatal asphyxia, and may therefore aid in disease stratification and prognostication after neonatal encephalopathy (NE). OBJECTIVE: To systematically review the existing literature on the accuracy of early heart rate variability (HRV) to predict brain injury and adverse neurodevelopmental outcomes after NE. DESIGN/METHODS: We systematically searched the literature published between May 1947 and May 2018. We included all prospective and retrospective studies reporting HRV metrics, within the first 7 days of life in babies with NE, and its association with adverse outcomes (defined as evidence of brain injury on magnetic resonance imaging and/or abnormal neurodevelopment at ≥1 year of age). We extracted raw data wherever possible to calculate the prognostic indices with confidence intervals. RESULTS: We retrieved 379 citations, 5 of which met the criteria. One further study was excluded as it analysed an already-included cohort. The 4 studies provided data on 205 babies, 80 (39%) of whom had adverse outcomes. Prognostic accuracy was reported for 12 different HRV metrics and the area under the curve (AUC) varied between 0.79 and 0.94. The best performing metric reported in the included studies was the relative power of high-frequency band, with an AUC of 0.94. CONCLUSIONS: HRV metrics are a promising bedside tool for early prediction of brain injury and neurodevelopmental outcome in babies with NE. Due to the small number of studies available, their heterogeneity and methodological limitations, further research is needed to refine this tool so that it can be used in clinical practice.
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Journal articleGrant JE, Daws R, Hampshire A, et al., 2018,
An fMRI Pilot Study of Cognitive Flexibility in Trichotillomania.
, J Neuropsychiatry Clin Neurosci, Vol: 30, Pages: 318-324Trichotillomania is a relatively common psychiatric condition, although its neurobiological basis is unknown. Abnormalities of flexible responding have been implicated in the pathophysiology of obsessive-compulsive disorder and thus may be relevant in trichotillomania. The purpose of this study was to probe reversal learning and attentional set-shifting in trichotillomania. Twelve adults with trichotillomania and 13 matched healthy control subjects undertook a functional MRI task of cognitive flexibility. Group-level activation maps for extradimensional and reversal switches were independently parcellated into discrete regions of interest using a custom watershed algorithm. Activation magnitudes were extracted from each region of interest and study subject and compared at the group level. Reversal events evoked the expected patterns of insula and parietal regions and activity in the frontal dorsal cortex extending anterior to the frontal poles, whereas extradimensional shifts evoked the expected frontal dorsolateral and parietal pattern of activity. Trichotillomania was associated with significantly increased right middle frontal and reduced right occipital cortex activation during reversal and set-shifting. Elevated frontal activation coupled with reduced activation in more posterior brain regions was identified. These pilot data suggest potentially important neural dysfunction associated with trichotillomania.
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Conference paperLi W, Lao-Kaim N, Roussakis A, et al., 2018,
Functional connectivity changes in relation to dopaminergic decline in Parkinson's over time: A resting-state fMRI and 11C-PE2I PET imaging study
, International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, Pages: S682-S683, ISSN: 0885-3185 -
Conference paperSridharan S, Raffel J, Nandoskar A, et al., 2018,
Confirmation of specific binding of the 18 kDa translocator protein (TSPO) radioligand [<SUP>18</SUP>F]GE-180: a blocking study using XDB173 in multiple sclerosis
, 34th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), Publisher: SAGE PUBLICATIONS LTD, Pages: 421-422, ISSN: 1352-4585 -
Conference paperAlansary A, Le Folgoc L, Vaillant G, et al., 2018,
Automatic view planning with multi-scale deep reinforcement learning agents
, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 277-285, ISSN: 0302-9743We propose a fully automatic method to find standardizedview planes in 3D image acquisitions. Standard view images are impor-tant in clinical practice as they provide a means to perform biometricmeasurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several DeepQ-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.
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Conference paperTarroni G, Oktay O, Sinclair M, et al., 2018,
A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks
, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 268-276, ISSN: 0302-9743In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by inter-slice respiratory motion and will not correctly represent the cardiac volume, introducing potential errors in the following analyses and visualisations. We propose an approach to automatically correct inter-slice respiratory motion in SA CMR image stacks. Our approach makes use of probabilistic segmentation maps (PSMs) of the left ventricular (LV) cavity generated with decision forests. PSMs are generated for each slice of the SA stack and rigidly registered in-plane to a target PSM. If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks. The proposed approach was tested on a dataset of SA stacks acquired from 24 healthy subjects (for which anatomical 3D cardiac images were also available as reference) and compared to two techniques which use LA intensity images and LA segmentations as targets, respectively. The results show the accuracy and robustness of the proposed approach in motion compensation.
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Conference paperSchlemper J, Castro DC, Bai W, et al., 2018,
Bayesian deep learning for accelerated MR image reconstruction
, International Workshop on Machine Learning for Medical Image Reconstruction, Publisher: Springer, Cham, Pages: 64-71, ISSN: 0302-9743Recently, many deep learning (DL) based MR image reconstruction methods have been proposed with promising results. However, only a handful of work has been focussing on characterising the behaviour of deep networks, such as investigating when the networks may fail to reconstruct. In this work, we explore the applicability of Bayesian DL techniques to model the uncertainty associated with DL-based reconstructions. In particular, we apply MC-dropout and heteroscedastic loss to the reconstruction networks to model epistemic and aleatoric uncertainty. We show that the proposed Bayesian methods achieve competitive performance when the test images are relatively far from the training data distribution and outperforms when the baseline method is over-parametrised. In addition, we qualitatively show that there seems to be a correlation between the magnitude of the produced uncertainty maps and the error maps, demonstrating the potential utility of the Bayesian DL methods for assessing the reliability of the reconstructed images.
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