Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

689 results found

Oksuz I, Ruijsink B, Puyol-Anton E, Clough JR, Cruz G, Bustin A, Prieto C, Botnar R, Rueckert D, Schnabel JA, King APet al., 2019, Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning, MEDICAL IMAGE ANALYSIS, Vol: 55, Pages: 136-147, ISSN: 1361-8415

Journal article

Cerrolaza JJ, Picazo ML, Humbert L, Sato Y, Rueckert D, Ballester MÁG, Linguraru MGet al., 2019, Computational anatomy for multi-organ analysis in medical imaging: A review., Med Image Anal, Vol: 56, Pages: 44-67

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of medical imaging applications on the future of healthcare.

Journal article

Lavdas I, Glocker B, Rueckert D, Taylor SA, Aboagye EO, Rockall AGet al., 2019, Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data, CLINICAL RADIOLOGY, Vol: 74, Pages: 346-356, ISSN: 0009-9260

Journal article

Howard J, Fisher L, Shun-Shin M, Keene D, Arnold A, Ahmad Y, Cook C, Moon J, Manisty C, Whinnett Z, Cole G, Rueckert D, Francis Det al., 2019, Cardiac rhythm device identification using neural networks, JACC: Clinical Electrophysiology, Vol: 5, Pages: 576-586, ISSN: 2405-5018

BackgroundMedical staff often need to determine the model of a pacemaker or defibrillator (cardiac rhythm devices) quickly and accurately. Current approaches involve comparing a device’s X-ray appearance with a manual flow chart. We aimed to see whether a neural network could be trained to perform this task more accurately.Methods and ResultsWe extracted X-ray images of 1676 devices, comprising 45 models from 5 manufacturers. We developed a convolutional neural network to classify the images, using a training set of 1451 images. The testing set was a further 225 images, consisting of 5 examples of each model. We compared the network’s ability to identify the manufacturer of a device with those of cardiologists using a published flow-chart.The neural network was 99.6% (95% CI 97.5 to 100) accurate in identifying the manufacturer of a device from an X-ray, and 96.4% (95% CI 93.1 to 98.5) accurate in identifying the model group. Amongst 5 cardiologists using the flow-chart, median manufacturer accuracy was 72.0% (range 62.2% to 88.9%), and model group identification was not possible. The network was significantly superior to all of the cardiologists in identifying the manufacturer (p < 0.0001 against the median human; p < 0.0001 against the best human).ConclusionsA neural network can accurately identify the manufacturer and even model group of a cardiac rhythm device from an X-ray, and exceeds human performance. This system may speed up the diagnosis and treatment of patients with cardiac rhythm devices and it is publicly accessible online.

Journal article

Cox DJ, Bai W, Price AN, Edwards AD, Rueckert D, Groves AMet al., 2019, Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle, PEDIATRIC RESEARCH, Vol: 85, Pages: 807-815, ISSN: 0031-3998

Journal article

Meng Q, Sinclair M, Zimmer V, Hou B, Rajchl M, Toussaint N, Oktay O, Schlemper J, Gomez A, Housden J, Matthew J, Rueckert D, Schnabel JA, Kainz Bet al., 2019, Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.

Working paper

Meng Q, Zimmer V, Hou B, Rajchl M, Toussaint N, Oktay O, Schlemper J, Gomez A, Housden J, Matthew J, Rueckert D, Schnabel JA, Kainz Bet al., 2019, Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.

Journal article

Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert Det al., 2019, Attention gated networks: Learning to leverage salient regions in medical images, MEDICAL IMAGE ANALYSIS, Vol: 53, Pages: 197-207, ISSN: 1361-8415

Journal article

Alansary A, Oktay O, Li Y, Folgoc LL, Hou B, Vaillant G, Kamnitsas K, Vlontzos A, Glocker B, Kainz B, Rueckert Det al., 2019, Evaluating reinforcement learning agents for anatomical landmark detection, Medical Image Analysis, Vol: 53, Pages: 156-164, ISSN: 1361-8415

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.

Journal article

Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, Urhemaa T, Tolonen A, van Gils M, Rueckert D, Dyremose N, Andersen BB, Lemstra AW, Hallikainen M, Kurl S, Herukka S-K, Remes AM, Waldemar G, Soininen H, Mecocci P, van der Flier WM, Lotjonen J, Hasselbalch SGet al., 2019, Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study, Alzheimers Research & Therapy, Vol: 11, ISSN: 1758-9193

BackgroundIn clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.MethodsIn this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy.ResultsAfter a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without too

Journal article

Robinson R, Valindria VV, Bai W, Oktay O, Kainz B, Suzuki H, Sanghvi MM, Aung N, Paiva JÉM, Zemrak F, Fung K, Lukaschuk E, Lee AM, Carapella V, Kim YJ, Piechnik SK, Neubauer S, Petersen SE, Page C, Matthews PM, Rueckert D, Glocker Bet al., 2019, Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study, Journal of Cardiovascular Magnetic Resonance, Vol: 21, ISSN: 1097-6647

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Journal article

van Essen TA, den Boogert HF, Cnossen MC, de Ruiter GCW, Haitsma I, Polinder S, Steyerberg EW, Menon D, Maas AIR, Lingsma HF, Peul WC, CENTER-TBI Investigators and Participantset al., 2019, Correction to: Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study., Acta Neurochir (Wien), Vol: 161, Pages: 451-455

The collaborator names are inverted.

Journal article

Meyer HV, Dawes TJW, Serrani M, Bai W, Tokarczuk P, Cai J, de Marvao A, Rueckert D, Matthews PM, Costantino ML, Birney E, Cook SA, O'Regan DPet al., 2019, Genomic analysis reveals a functional role for myocardial trabeculae in adults, Publisher: Cold Spring Harbor Laboratory

<jats:p>Since being first described by Leonardo da Vinci in 1513 it has remained an enigma why the endocardial surfaces of the adult heart retain a complex network of muscular trabeculae - with their persistence thought to be a vestige of embryonic development. For causative physiological inference we harness population genomics, image-based intermediate phenotyping and in silico modelling to determine the effect of this complex cardiovascular trait on function. Using deep learning-based image analysis we identified genetic associations with trabecular complexity in 18,097 UK Biobank participants which were replicated in an independently measured cohort of 1,129 healthy adults. Genes in these associated regions are enriched for expression in the fetal heart or vasculature and implicate loci associated with haemodynamic phenotypes and developmental pathways. A causal relationship between increasing trabecular complexity and both ventricular performance and electrical activity are supported by complementary biomechanical simulations and Mendelian randomisation studies. These findings show that myocardial trabeculae are a previously-unrecognised determinant of cardiovascular physiology in adult humans.</jats:p>

Working paper

Bello G, Dawes T, Duan J, Biffi C, Simoes Monteiro de Marvao A, Howard L, Gibbs S, Wilkins M, Cook S, Rueckert D, O'Regan Det al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol: 1, Pages: 95-104, ISSN: 2522-5839

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

Journal article

Gilbert K, Bai W, Mauger C, Medrano-Gracia P, Suinesiaputra A, Lee AM, Sanghvi MM, Aung N, Piechnik SK, Neubauer S, Petersen SE, Rueckert D, Young AAet al., 2019, Independent left ventricular morphometric atlases show consistent relationships with cardiovascular risk factors: A UK Biobank study, Scientific Reports, Vol: 9, ISSN: 2045-2322

Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.

Journal article

Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, Marvao AD, Doumou G, O'Regan DP, Rueckert Det al., Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.

Journal article

Chen C, Bai W, Rueckert D, 2019, Multi-task Learning for Left Atrial Segmentation on GE-MRI

© 2019, Springer Nature Switzerland AG. Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/.

Working paper

Qin C, Shi B, Liao R, Mansi T, Rueckert D, Kamen Aet al., 2019, Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations, Pages: 249-261, ISSN: 0302-9743

© 2019, Springer Nature Switzerland AG. We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learning-based image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.

Conference paper

Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, Urhemaa T, Tolonen A, van Gils M, Tong T, Guerrero R, Rueckert D, Dyremose N, Andersen BB, Simonsen AH, Lemstra A, Hallikainen M, Kurl S, Herukka S-K, Remes AM, Waldemar G, Soininen H, Mecocci P, van der Flier WM, Lötjönen J, Hasselbalch SGet al., 2019, Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study., Curr Alzheimer Res, Vol: 16, Pages: 91-101

BACKGROUND: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. OBJECTIVES: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. METHODS: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. RESULTS: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). CONCLUSION: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.

Journal article

Balaban G, Halliday BP, Costa CM, Bai W, Porter B, Rinaldi CA, Plank G, Rueckert D, Prasad SK, Bishop MJet al., 2018, Fibrosis Microstructure Modulates Reentry in Non-ischemic Dilated Cardiomyopathy: Insights From Imaged Guided 2D Computational Modeling, Frontiers in Physiology, Vol: 9, ISSN: 1664-042X

Aims: Patients who present with non-ischemic dilated cardiomyopathy (NIDCM) andenhancement on late gadolinium magnetic resonance imaging (LGE-CMR), are at highrisk of sudden cardiac death (SCD). Further risk stratification of these patients basedon LGE-CMR may be improved through better understanding of fibrosis microstructure.Our aim is to examine variations in fibrosis microstructure based on LGE imaging, andquantify the effect on reentry inducibility and mechanism. Furthermore, we examine therelationship between transmural activation time differences and reentry.Methods and Results: 2D Computational models were created from a single short axisLGE-CMR image, with 401 variations in fibrosis type (interstitial, replacement) and density,as well as presence or absence of reduced conductivity (RC). Transmural activationtimes (TAT) were measured, as well as reentry incidence and mechanism. Reentrieswere inducible above specific density thresholds (0.8, 0.6 for interstitial, replacementfibrosis). RC reduced these thresholds (0.3, 0.4 for interstitial, replacement fibrosis) andincreased reentry incidence (48 no RC vs. 133 with RC). Reentries were classified as rotor,micro-reentry, or macro-reentry and depended on fibrosis micro-structure. Differencesin TAT at coupling intervals 210 and 500ms predicted reentry in the models (sensitivity89%, specificity 93%). A sensitivity analysis of TAT and reentry incidence showed thatthese quantities were robust to small changes in the pacing location.Conclusion: Computational models of fibrosis micro-structure underlying areas ofLGE in NIDCM provide insight into the mechanisms and inducibility of reentry, andtheir dependence upon the type and density of fibrosis. Transmural activation times,measured at the central extent of the scar, can potentially differentiate microstructureswhich support reentry.

Journal article

van Essen TA, den Boogert HF, Cnossen MC, de Ruiter GCW, Haitsma I, Polinder S, Steyerberg EW, Menon D, Maas AIR, Lingsma HF, Peul WC, Cecilia A, Hadie A, Vanni A, Judith A, Krisztina A, Norberto A, Nada A, Lasse A, Azasevac A, Audny A, Anna A, Hilko A, Gerard A, Kaspars A, Philippe A, Luisa AM, Camelia B, Rafael B, Ronald B, Pal B, Ursula B, Romuald B, Ronny B, Francisco Javier B, Bo-Michael B, Antonio B, Remy B, Habib B, Thierry B, Maurizio B, Luigi B, Christopher B, Federico B, Harald B, Erta B, Morten B, Hugo DB, Pierre B, Peter B, Alexandra B, Vibeke B, Joanne B, Camilla B, Andras B, Monika B, Emiliana C, Rosa CM, Peter C, Lozano Guillermo C, Marco C, Elsa C, Carpenter K, Ana M C-L, Francesco C, Giorgio C, Arturo C, Giuseppe C, Maryse C, Mark C, Jonathan C, Lizzie C-K, Johnny C, Jamie CD, Marta C, Amra C, Nicola C, Endre C, Marek C, Claire D-F, Francois D, Pierre D, Helen D, Veronique DK, Francesco DC, Bart D, Godard DRCW, Dula D, Ding S, Diederik D, Abhishek D, Emma D, Jens D, Guy-Loup D, George E, Heiko E, Ari E, Patrick E, Erzsebet E, Martin F, Valery FL, Feng J, Kelly F, Francesca F, Gilles F, Ulderico F, Shirin F, Alex F, Pablo G, Damien G, Dashiell G, Gao G, Karin G, Pradeep G, Alexandre G, Lelde G, Benoit G, Ben G, Jagos G, Pedro GA, Francesca G, Russell GL, Deepak G, Juanita HA, Iain H, Jed HA, Raimund H, Eirik H, Daniel H, Astrid H, Stefan H, Lindsay H, Jilske H, Peter HJ, Kristine HA, Bram J, Stefan J, Mike J, Bojan J, Jiang J-Y, Kelly J, Konstantinos K, Mladen K, Ari K, Maija K, Thomas K, Riku K, Angelos KG, Balint K, Erwin K, Ksenija K, Daniel K, Lars-Owe K, Noemi K, Alfonso L, Linda L, Steven L, Fiona L, Christian L, Rolf L, Valerie L, Jin L, Leon L, Roger L, Hester L, Dirk L, Angels L, Andrew MIR, Stephen M, Marc M, Marek M, Sebastian M, Alex M, Geoffrey M, Didier M, Francisco ML, Costanza M, Armando M, Hugues M, Alessandro M, Julia M, Charles M, Catherine M, Bela M, David M, Tomas M, Cristina M-K, Davide M, Visakh M, Lynnette M, Holger M, Nandeshet al., 2018, Variation in neurosurgical management of traumatic brain injury: A survey in 68 centers participating in the CENTER-TBI study, Acta Neurochirurgica, Vol: 161, Pages: 435-449, ISSN: 0001-6268

BackgroundNeurosurgical management of traumatic brain injury (TBI) is challenging, with only low-quality evidence. We aimed to explore differences in neurosurgical strategies for TBI across Europe.MethodsA survey was sent to 68 centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. The questionnaire contained 21 questions, including the decision when to operate (or not) on traumatic acute subdural hematoma (ASDH) and intracerebral hematoma (ICH), and when to perform a decompressive craniectomy (DC) in raised intracranial pressure (ICP).ResultsThe survey was completed by 68 centers (100%). On average, 10 neurosurgeons work in each trauma center. In all centers, a neurosurgeon was available within 30 min. Forty percent of responders reported a thickness or volume threshold for evacuation of an ASDH. Most responders (78%) decide on a primary DC in evacuating an ASDH during the operation, when swelling is present. For ICH, 3% would perform an evacuation directly to prevent secondary deterioration and 66% only in case of clinical deterioration. Most respondents (91%) reported to consider a DC for refractory high ICP. The reported cut-off ICP for DC in refractory high ICP, however, differed: 60% uses 25 mmHg, 18% 30 mmHg, and 17% 20 mmHg. Treatment strategies varied substantially between regions, specifically for the threshold for ASDH surgery and DC for refractory raised ICP. Also within center variation was present: 31% reported variation within the hospital for inserting an ICP monitor and 43% for evacuating mass lesions.ConclusionDespite a homogeneous organization, considerable practice variation exists of neurosurgical strategies for TBI in Europe. These results provide an incentive for comparative effectiveness research to determine elements of effective neurosurgical care.

Journal article

Attard M, Dawes T, Simoes Monteiro de Marvao A, Biffi C, Shi W, Wharton J, Rhodes C, Ghataorhe P, Gibbs J, Howard L, Rueckert D, Wilkins M, O'Regan Det al., 2018, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: Three dimensional analysis of cardiac magnetic resonance imaging, EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging, ISSN: 2047-2412

AimsWe sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress.Methods and resultsIn 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen.ConclusionUsing computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.

Journal article

Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Biffi C, Doumou G, De Marvao A, O’Regan DP, Rueckert Det al., 2018, Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images, MICCAI ShapeMI Workshop, Pages: 258-267, ISSN: 0302-9743

© 2018, Springer Nature Switzerland AG. In 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.

Conference paper

Dawes T, Simoes Monteiro de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det al., 2018, Identifying the optimal regional predictor of right ventricular global function: a high resolution 3D cardiac magnetic resonance study, Anaesthesia, Vol: 74, Pages: 312-320, ISSN: 0003-2409

Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.

Journal article

Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan D, Cook S, Glocker B, Matthews P, Rueckert Det al., 2018, Learning-based quality control for cardiac MR images, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

Journal article

Bozek J, Makropoulos A, Schuh A, Fitzgibbon S, Wright R, Glasser MF, Coalson TS, O'Muircheartaigh J, Hutter J, Price AN, Cordero-Grande L, Teixeira RPAG, Hughes E, Tusor N, Baruteau KP, Rutherford MA, Edwards AD, Hajnal JV, Smith SM, Rueckert D, Jenkinson M, Robinson ECet al., 2018, Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project, NeuroImage, Vol: 179, Pages: 11-29, ISSN: 1053-8119

We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36-44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.

Journal article

Schlemper J, Yang G, Ferreira P, Scott A, McGill LA, Khalique Z, Gorodezky M, Roehl M, Keegan J, Pennell D, Firmin D, Rueckert Det al., 2018, Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 11070 LNCS, Pages: 295-303, ISSN: 0302-9743

© Springer Nature Switzerland AG 2018. Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~ 30 min using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications.

Journal article

Li Y, Alansary A, Cerrolaza J, Khanal B, Sinclair M, Matthew J, Gupta C, Knight C, Kainz B, Rueckert Det al., Fast multiple landmark localisation using a patch-based iterative network, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer Verlag, ISSN: 0302-9743

We propose a new Patch-based Iterative Network (PIN) for fast and accuratelandmark localisation in 3D medical volumes. PIN utilises a ConvolutionalNeural Network (CNN) to learn the spatial relationship between an image patchand anatomical landmark positions. During inference, patches are repeatedlypassed to the CNN until the estimated landmark position converges to the truelandmark location. PIN is computationally efficient since the inference stageonly selectively samples a small number of patches in an iterative fashionrather than a dense sampling at every location in the volume. Our approachadopts a multi-task learning framework that combines regression andclassification to improve localisation accuracy. We extend PIN to localisemultiple landmarks by using principal component analysis, which models theglobal anatomical relationships between landmarks. We have evaluated PIN using72 3D ultrasound images from fetal screening examinations. PIN achievesquantitatively an average landmark localisation error of 5.59mm and a runtimeof 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2Dstandard scan planes derived from the predicted landmark locations are visuallysimilar to the clinical ground truth.

Conference paper

Li Y, Khanal B, Hou B, Alansary A, Cerrolaza J, Sinclair M, Matthew J, Gupta C, Knight C, Kainz B, Rueckert Det al., Standard plane detection in 3D fetal ultrasound using an iterative transformation network, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer Verlag, ISSN: 0302-9743

Standard scan plane detection in fetal brain ultrasound (US) forms a crucialstep in the assessment of fetal development. In clinical settings, this is doneby manually manoeuvring a 2D probe to the desired scan plane. With the adventof 3D US, the entire fetal brain volume containing these standard planes can beeasily acquired. However, manual standard plane identification in 3D volume islabour-intensive and requires expert knowledge of fetal anatomy. We propose anew Iterative Transformation Network (ITN) for the automatic detection ofstandard planes in 3D volumes. ITN uses a convolutional neural network to learnthe relationship between a 2D plane image and the transformation parametersrequired to move that plane towards the location/orientation of the standardplane in the 3D volume. During inference, the current plane image is passediteratively to the network until it converges to the standard plane location.We explore the effect of using different transformation representations asregression outputs of ITN. Under a multi-task learning framework, we introduceadditional classification probability outputs to the network to act asconfidence measures for the regressed transformation parameters in order tofurther improve the localisation accuracy. When evaluated on 72 US volumes offetal brain, our method achieves an error of 3.83mm/12.7 degrees and3.80mm/12.6 degrees for the transventricular and transcerebellar planesrespectively and takes 0.46s per plane.

Conference paper

Cerrolaza JJ, Li Y, Biffi C, Gomez A, Sinclair M, Matthew J, Knight C, Kainz B, Rueckert Det al., 2018, 3D fetal skull reconstruction from 2DUS via deep conditional generative networks, International Conference on Medical Image Computing and Computer-Assisted Intervention, Pages: 383-391, ISSN: 0302-9743

© Springer Nature Switzerland AG 2018. 2D ultrasound (US) is the primary imaging modality in antenatal healthcare. Despite the limitations of traditional 2D biometrics to characterize the true 3D anatomy of the fetus, the adoption of 3DUS is still very limited. This is particularly significant in developing countries and remote areas, due to the lack of experienced sonographers and the limited access to 3D technology. In this paper, we present a new deep conditional generative network for the 3D reconstruction of the fetal skull from 2DUS standard planes of the head routinely acquired during the fetal screening process. Based on the generative properties of conditional variational autoencoders (CVAE), our reconstruction architecture (REC-CVAE) directly integrates the three US standard planes as conditional variables to generate a unified latent space of the skull. Additionally, we propose HiREC-CVAE, a hierarchical generative network based on the different clinical relevance of each predictive view. The hierarchical structure of HiREC-CVAE allows the network to learn a sequence of nested latent spaces, providing superior predictive capabilities even in the absence of some of the 2DUS scans. The performance of the proposed architectures was evaluated on a dataset of 72 cases, showing accurate reconstruction capabilities from standard non-registered 2DUS images.

Conference paper

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