ORCID Profile
0000-0002-5694-5340
Current Organisations
University College London
,
King's College London
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Publisher: Elsevier BV
Date: 09-2020
Publisher: IOP Publishing
Date: 17-10-2022
Abstract: Objective. Dose-rate effects in Gamma Knife radiosurgery treatments can lead to varying biologically effective dose (BED) levels for the same physical dose. The non-convex BED model depends on the delivery sequence and creates a non-trivial treatment planning problem. We investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per iso-centre beam-on times and delivery sequence. Approach. We implement two dedicated optimisation algorithms. One approach relies on mixed-integer linear programming (MILP) using a purposely developed convex underestimator for the BED to mitigate local minima issues at the cost of computational complexity. The second approach (local optimisation) is faster and potentially usable in a clinical setting but more prone to local minima issues. It sequentially executes the beam-on time (quasi-Newton method) and sequence optimisation (local search algorithm). We investigate the trade-off between time to convergence and solution quality by evaluating the resulting treatment plans’ objective function values and clinical parameters. We also study the treatment time dependence of the initial and optimised plans using BED 95 (BED delivered to 95% of the target volume) values. Main results. When optimising the beam-on times and delivery sequence, the local optimisation approach converges several orders of magnitude faster than the MILP approach (minutes versus hours–days) while typically reaching within 1.2% (0.02–2.08%) of the final objective function value. The quality parameters of the resulting treatment plans show no meaningful difference between the local and MILP optimisation approaches. The presented optimisation approaches remove the treatment time dependence observed in the original treatment plans, and the chosen objectives successfully promote more conformal treatments. Significance. We demonstrate the feasibility of using an inverse planning approach within a reasonable time frame to ensure BED-based objectives are achieved across varying treatment times and highlight the prospect of further improvements in treatment plan quality.
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 06-01-2015
DOI: 10.1007/S11548-014-1142-5
Abstract: Realistic modelling of soft tissue biomechanics and mechanical interactions between tissues is an important part of biomechanically-informed surgical image-guidance and surgical simulation. This submission details a contact-modelling pipeline suitable for implementation in explicit matrix-free FEM solvers. While these FEM algorithms have been shown to be very suitable for simulation of soft tissue biomechanics and successfully used in a number of image-guidance systems, contact modelling specifically for these solvers is rarely addressed, partly because the typically large number of time steps required with this class of FEM solvers has led to a perception of them being a poor choice for simulations requiring complex contact modelling. The presented algorithm is capable of handling most scenarios typically encountered in image-guidance. The contact forces are computed with an evolution of the Lagrange-multiplier method first used by Taylor and Flanagan in PRONTO 3D extended with spatio-temporal smoothing heuristics for improved stability and edge-edge collision handling, and a new friction model. For contact search, a bounding-volume hierarchy (BVH) is employed, which is capable of identifying self-collisions by means of the surface-normal bounding cone of Volino and Magnenat-Thalmann, in turn computed with a novel formula. The BVH is further optimised for the small time steps by reducing the number of bounding-volume refittings between iterations through identification of regions with mostly rigid motion and negligible deformation. Further optimisation is achieved by integrating the self-collision criterion in the BVH creation and updating algorithms. The effectiveness of the algorithm is demonstrated on a number of artificial test cases and meshes derived from medical image data. It is shown that the proposed algorithm reduces the cost of BVH refitting to the point where it becomes a negligible part of the overall computation time of the simulation. It is also shown that the proposed surface-normal cone computation formula leads to about 40 % fewer BVH subtrees that must be checked for self-collisions compared with the widely used method of Provot. The proposed contact-force formulation and friction model are evaluated on artificial test cases that allow for a comparison with a ground truth. The quality of the proposed contact forces is assessed in terms of trajectories and energy conservation a [Formula: see text]0.4 % drop off in total energy and highly plausible trajectories are found in the experiments. The friction model is evaluated through a benchmark problem with an analytical solution and a maximum displacement error of 8.2 %, and excellent agreement in terms of the stick/slip boundary is found. Finally, we show with realistic image-guidance ex les that the entire contact-modelling pipeline can be executed within a timeframe that is of the same order of magnitude as that required for standard FEM computations.
Publisher: SPIE
Date: 26-02-2009
DOI: 10.1117/12.811588
Publisher: Proceedings of the National Academy of Sciences
Date: 05-11-2013
Abstract: Beta-amyloid plaque accumulation, glucose hypometabolism, and neuronal atrophy are hallmarks of Alzheimer’s disease. However, the regional ordering of these biomarkers prior to dementia remains untested. In a cohort with Alzheimer’s disease mutations, we performed an integrated whole-brain analysis of three major imaging techniques: amyloid PET, [ 18 F]fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism followed by atrophy. Critically, however, not all regions lose metabolic function, and not all regions atrophy, even when there is significant amyloid deposition. These regional disparities have important implications for clinical trials of disease-modifying therapies.
Publisher: American Medical Association (AMA)
Date: 09-2014
Publisher: American Association for Cancer Research (AACR)
Date: 07-2020
DOI: 10.1158/1055-9965.EPI-20-0606
Abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.
Publisher: Oxford University Press (OUP)
Date: 23-12-2008
DOI: 10.1093/NAR/GKM950
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 12-2020
Publisher: IOP Publishing
Date: 27-02-2007
DOI: 10.1088/0031-9155/52/6/005
Abstract: The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.
Publisher: Springer Science and Business Media LLC
Date: 27-04-2022
DOI: 10.1038/S41598-022-08032-3
Abstract: COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms.
Publisher: Elsevier BV
Date: 10-2009
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: BMJ
Date: 08-2018
DOI: 10.1136/BMJOPEN-2018-021944
Abstract: The major unmet need in multiple sclerosis (MS) is for neuroprotective therapies that can slow (or ideally stop) the rate of disease progression. The UK MS Society Clinical Trials Network (CTN) was initiated in 2007 with the purpose of developing a national, efficient, multiarm trial of repurposed drugs. Key underpinning work was commissioned by the CTN to inform the design, outcome selection and drug choice including animal models and a systematic review. This identified seven leading oral agents for repurposing as neuroprotective therapies in secondary progressive MS (SPMS). The purpose of the Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial (MS-SMART) will be to evaluate the neuroprotective efficacy of three of these drugs, selected with distinct mechanistic actions and previous evidence of likely efficacy, against a common placebo arm. The interventions chosen were: amiloride (acid-sensing ion channel antagonist) fluoxetine (selective serotonin reuptake inhibitor) and riluzole (glutamate antagonist). Patients with progressing SPMS will be randomised 1:1:1:1 to amiloride, fluoxetine, riluzole or matched placebo and followed for 96 weeks. The primary outcome will be the percentage brain volume change (PBVC) between baseline and 96 weeks, derived from structural MR brain imaging data using the Structural Image Evaluation, using Normalisation, of Atrophy method. With a s le size of 90 per arm, this will give 90% power to detect a 40% reduction in PBVC in any active arm compared with placebo and 80% power to detect a 35% reduction (analysing by analysis of covariance and with adjustment for multiple comparisons of three 1.67% two-sided tests), giving a 5% overall two-sided significance level. MS-SMART is not powered to detect differences between the three active treatment arms. Allowing for a 20% dropout rate, 110 patients per arm will be randomised. The study will take place at Neuroscience centres in England and Scotland. MS-SMART was approved by the Scotland A Research Ethics Committee on 13 January 2013 (REC reference: 13/SS/0007). Results of the study will be submitted for publication in a peer-reviewed journal. NCT01910259 2012-005394-31 ISRCTN28440672 .
Publisher: Wiley
Date: 17-11-2014
DOI: 10.1002/ANA.24296
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11866763_113
Abstract: This paper considers the problem of automatic classification of textured tissues in 3D MRI. More specifically, it aims at validating the use of features extracted from the phase of the MR signal to improve texture discrimination in bone segmentation. This extra information provides better segmentation, compared to using magnitude only features. We also present a novel multiscale scheme to improve the speed of pixel based classification algorithm, such as support vector machines. This algorithm dramatically increases the speed of the segmentation process by an order of magnitude through a reduction of the number of pixels that needs to be classified in the image.
Publisher: Oxford University Press (OUP)
Date: 20-06-2019
DOI: 10.1093/BRAIN/AWZ136
Abstract: Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer’s disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic ersity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one in iduals (117 posterior cortical atrophy, 106 typical Alzheimer’s disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer’s disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer’s disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer’s disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer’s disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippoc al, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer’s disease showed early hippoc al atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer’s disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer’s disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer’s disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.
Publisher: Elsevier BV
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 2011
DOI: 10.1016/J.NEUROIMAGE.2010.07.039
Abstract: Ambitious international efforts are underway to produce gene-knockout mice for each of the 25,000 mouse genes, providing a new platform to study mammalian development and disease. Robust, large-scale methods for morphological assessment of prenatal mice will be essential to this work. Embryo phenotyping currently relies on histological techniques but these are not well suited to large volume screening. The qualitative nature of these approaches also limits the potential for detailed group analysis. Advances in non-invasive imaging techniques such as magnetic resonance imaging (MRI) may surmount these barriers. We present a high-throughput approach to generate detailed virtual histology of the whole embryo, combined with the novel use of a whole-embryo atlas for automated phenotypic assessment. Using in idual 3D embryo MRI histology, we identified new pituitary phenotypes in Hesx1 mutant mice. Subsequently, we used advanced computational techniques to produce a whole-body embryo atlas from 6 CD-1 embryos, creating an average image with greatly enhanced anatomical detail, particularly in CNS structures. This methodology enabled unsupervised assessment of morphological differences between CD-1 embryos and Chd7 knockout mice (n=5 Chd7(+/+) and n=8 Chd7(+/-), C57BL/6 background). Using a new atlas generated from these three groups, quantitative organ volumes were automatically measured. We demonstrated a difference in mean brain volumes between Chd7(+/+) and Chd7(+/-) mice (42.0 vs. 39.1mm(3), p<0.05). Differences in whole-body, olfactory and normalised pituitary gland volumes were also found between CD-1 and Chd7(+/+) mice (C57BL/6 background). Our work demonstrates the feasibility of combining high-throughput embryo MRI with automated analysis techniques to distinguish novel mouse phenotypes.
Publisher: SPIE
Date: 23-02-2012
DOI: 10.1117/12.911787
Publisher: Elsevier BV
Date: 08-2007
DOI: 10.1016/J.MEDIA.2007.03.003
Abstract: Magnetic resonance (MR) imaging is a widely available and well accepted non invasive imaging technique. Development of automatic and semi-automatic techniques to analyse MR images has been the focus of much research and numerous publications. However, most of this research only uses the magnitude of the acquired complex MR signal, discarding the phase information. In MR, the phase relates to the magnetic properties of tissues, information which is not found in the magnitude signal. As a result, phase is a complement to the magnitude signal and can improve the segmentation and analysis of MR images. In this paper, we consider the automatic classification of textured tissues in 3D MRI. Specifically, we include features extracted from the phase of the MR signal to improve texture discrimination in the bone segmentation. Our approach does not require phase unwrapping, with the MR signal processed in its complex form. The extra information extracted from the phase provides better segmentation, compared to only using magnitude features. The segmentation approach is integrated within a novel multiscale scheme, designed to improve the speed of pixel based classification algorithms, such as support vector machines. An order of magnitude increase is obtained, by reducing the number of pixels that need to be classified.
Publisher: BMJ
Date: 05-01-2016
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 03-2020
Publisher: SPIE
Date: 08-03-2007
DOI: 10.1117/12.711234
Publisher: Springer Science and Business Media LLC
Date: 15-10-2018
DOI: 10.1038/S41467-018-05892-0
Abstract: The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype ( p = 7.18 × 10 −4 ) or temporal stage ( p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Publisher: Springer Berlin Heidelberg
Date: 2008
DOI: 10.1007/978-3-540-85988-8_84
Abstract: Previously almost all biomechanically-based time-critical surgical simulation has ignored the well established features of tissue mechanical response of anisotropy and time-dependence. We address this issue by presenting an efficient solution procedure for anisotropic viscohyperelastic constitutive models which allows use of these in nonlinear explicit dynamic finite element algorithms. We show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. When combined with high performance GPU execution the complete framework is suitable for time-critical simulation applications such as interactive surgical simulation and intraoperative image registration.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Springer Science and Business Media LLC
Date: 17-06-2023
DOI: 10.1007/S00330-023-09736-4
Abstract: Surgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings. In a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings. Using CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators’ two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81–3.27 mm) and 2.03 mm (IQR 0.99–3.84 mm) for the two annotators, respectively. rs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa. Readout-segmented diffusion-weighted imaging and colour tissue mapping provide 1–2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma. • Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects. • Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21–2.03 mm of the nerve’s true intraoperative location. • Reproducible results were obtained on different scanners.
Publisher: Elsevier BV
Date: 05-2014
DOI: 10.1016/J.MEDIA.2014.03.003
Abstract: Determining corresponding regions between an MRI and an X-ray mammogram is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes between the two image acquisitions. In this work we propose an intensity-based image registration framework, where the biomechanical transformation model parameters and the rigid-body transformation parameters are optimised simultaneously. Patient-specific biomechanical modelling of the breast derived from diagnostic, prone MRI has been previously used for this task. However, the high computational time associated with breast compression simulation using commercial packages, did not allow the optimisation of both pose and FEM parameters in the same framework. We use a fast explicit Finite Element (FE) solver that runs on a graphics card, enabling the FEM-based transformation model to be fully integrated into the optimisation scheme. The transformation model has seven degrees of freedom, which include parameters for both the initial rigid-body pose of the breast prior to mammographic compression, and those of the biomechanical model. The framework was tested on ten clinical cases and the results were compared against an affine transformation model, previously proposed for the same task. The mean registration error was 11.6±3.8mm for the CC and 11±5.4mm for the MLO view registrations, indicating that this could be a useful clinical tool.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 18-09-2013
Publisher: Elsevier BV
Date: 12-2010
DOI: 10.1016/J.PBIOMOLBIO.2010.09.011
Abstract: Surgical simulators provide another tool for training and practising surgical procedures, usually restricted to the use of cadavers. Our surgical simulator utilises Finite Element (FE) models based on linear elasticity. It is driven by displacements, as opposed to forces, allowing for realistic simulation of both deformation and haptic response at real-time rates. To achieve demanding computational requirements, the stiffness matrix K, which encompasses the geometrical and physical properties of the object, is precomputed, along with K⁻¹. Common to many surgical procedures is the requirement of cutting tissue. Introducing topology modifications, such as cutting, into these precomputed schemes does however come as a challenge, as the precomputed data needs to be modified, to reflect the new topology. In particular, recomputing K⁻¹ is too costly to be performed during the simulation. Our topology modification method is based upon updating K⁻¹ rather than entirely recomputing the matrix. By integrating condensation, we improve efficiency to allow for interaction with larger models. We can further enhance this by redistributing computational load to improve the system's real-time response. We exemplify our techniques with results from our surgical simulation system.
Publisher: SPIE
Date: 26-02-2009
DOI: 10.1117/12.812460
Publisher: Wiley
Date: 07-02-2014
DOI: 10.1002/HBM.22468
Publisher: Cold Spring Harbor Laboratory
Date: 25-03-2019
DOI: 10.1101/582155
Abstract: The Dementias Platform UK (DPUK) Data Portal is a data repository facilitating access to data for 3 370 929 in iduals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform (UKSeRP) infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 in idual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Project are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.
Publisher: Oxford University Press (OUP)
Date: 09-07-2014
DOI: 10.1093/BRAIN/AWU176
Publisher: IOP Publishing
Date: 15-12-2011
DOI: 10.1088/0031-9155/57/2/455
Abstract: Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.
Publisher: Springer Berlin Heidelberg
Date: 2007
DOI: 10.1007/978-3-540-75757-3_28
Abstract: With the advent of biomarkers such as 11C-PIB and the increase in use of PET, automated methods are required for processing and analyzing datasets from research studies and in clinical settings. A common preprocessing step is the calculation of standardized uptake value ratio (SUVR) for inter-subject normalization. This requires segmented grey matter (GM) for VOI refinement. However 11C-PIB uptake is proportional to amyloid build up leading to inhomogeneities in intensities, especially within GM. Inhomogeneities present a challenge for clustering and pattern classification based approaches to PET segmentation as proposed in current literature. In this paper we modify a MR image segmentation technique based on expectation maximization for 11C-PIB PET segmentation. A priori probability maps of the tissue types are used to initialize and enforce anatomical constraints. We developed a Bézier spline based inhomogeneity correction techniques that is embedded in the segmentation algorithm and minimizes inhomogeneity resulting in better segmentations of 11C-PIB PET images. We compare our inhomogeneity with a global polynomial correction technique and validate our approach using co-registered MRI segmentations.
Publisher: SPIE
Date: 08-03-2007
DOI: 10.1117/12.711083
Publisher: Wiley
Date: 03-05-2013
DOI: 10.1002/MRM.24306
Abstract: Effective methods for high-throughput screening and morphometric analysis are crucial for phenotyping the increasing number of mouse mutants that are being generated. Automated segmentation propagation for embryo phenotyping is an emerging application that enables noninvasive and rapid quantification of substructure volumetric data for morphometric analysis. We present a study to assess and validate the accuracy of brain and kidney volumes generated via segmentation propagation in an ex vivo mouse embryo MRI atlas comprising three different groups against the current "gold standard"--manual segmentation. Morphometric assessment showed good agreement between automatically and manually segmented volumes, demonstrating that it is possible to assess volumes for phenotyping a population of embryos using segmentation propagation with the same variation as manual segmentation. As part of this study, we have made our average atlas and segmented volumes freely available to the community for use in mouse embryo phenotyping studies. These MRI datasets and automated methods of analyses will be essential for meeting the challenge of high-throughput, automated embryo phenotyping.
Publisher: Public Library of Science (PLoS)
Date: 22-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
DOI: 10.1109/TOH.2011.66
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2008
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11566489_100
Publisher: SPIE
Date: 06-03-2008
DOI: 10.1117/12.771862
Publisher: SPIE
Date: 06-03-2008
DOI: 10.1117/12.770058
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2010
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 12-2015
DOI: 10.1016/J.MEDIA.2015.07.004
Abstract: We present a framework for simulating cross-sectional or longitudinal biomarker data sets from neurodegenerative disease cohorts that reflect the temporal evolution of the disease and population ersity. The simulation system provides a mechanism for evaluating the performance of data-driven models of disease progression, which bring together biomarker measurements from large cross-sectional (or short term longitudinal) cohorts to recover the average population-wide dynamics. We demonstrate the use of the simulation framework in two different ways. First, to evaluate the performance of the Event Based Model (EBM) for recovering biomarker abnormality orderings from cross-sectional datasets. Second, to evaluate the performance of a differential equation model (DEM) for recovering biomarker abnormality trajectories from short-term longitudinal datasets. Results highlight several important considerations when applying data-driven models to sporadic disease datasets as well as key areas for future work. The system reveals several important insights into the behaviour of each model. For ex le, the EBM is robust to noise on the underlying biomarker trajectory parameters, under-s ling of the underlying disease time course and outliers who follow alternative event sequences. However, the EBM is sensitive to accurate estimation of the distribution of normal and abnormal biomarker measurements. In contrast, we find that the DEM is sensitive to noise on the biomarker trajectory parameters, resulting in an over estimation of the time taken for biomarker trajectories to go from normal to abnormal. This over estimate is approximately twice as long as the actual transition time of the trajectory for the expected noise level in neurodegenerative disease datasets. This simulation framework is equally applicable to a range of other models and longitudinal analysis techniques.
Publisher: Elsevier BV
Date: 04-2009
DOI: 10.1016/J.MEDIA.2008.10.001
Abstract: Efficient and accurate techniques for simulation of soft tissue deformation are an increasingly valuable tool in many areas of medical image computing, such as biomechanically-driven image registration and interactive surgical simulation. For reasons of efficiency most analyses are based on simplified linear formulations, and previously almost all have ignored well established features of tissue mechanical response such as anisotropy and time-dependence. We address these latter issues by firstly presenting a generalised anisotropic viscoelastic constitutive framework for soft tissues, particular cases of which have previously been used to model a wide range of tissues. We then develop an efficient solution procedure for the accompanying viscoelastic hereditary integrals which allows use of such models in explicit dynamic finite element algorithms. We show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. Finally we describe the implementation of a new GPU-based finite element scheme for soft tissue simulation using the CUDA API. Even with the inclusion of more elaborate constitutive models as described the new implementation affords speed improvements compared with our recent graphics API-based implementation, and compared with CPU execution a speed up of 56.3 x is achieved. The validity of the viscoelastic solution procedure and performance of the GPU implementation are demonstrated with a series of numerical ex les.
Publisher: SPIE
Date: 08-03-2007
DOI: 10.1117/12.710510
Publisher: Cold Spring Harbor Laboratory
Date: 19-03-2023
DOI: 10.1101/2023.03.14.23287211
Abstract: Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised in iduals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Cognitive performance (working memory, attention, reasoning, motor control) was assessed in participants of a voluntary biobank in July, 2021 (Round 1), and April, 2022 (Round 2). Participants, drawn from the COVID Symptom Study smartphone app, comprised in iduals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. 3335 in iduals completed Round 1, of whom 1768 also completed Round 2. At Round 1, in iduals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = −0.14 standard deviations, SDs) than negative controls. Deficits were largest for positive in iduals with ≥ 12 weeks of symptoms (N = 495, β = −0.22 SDs). Effects were comparable to hospital presentation during illness (N = 281, β = −0.31 SDs), and 10 years age difference (60-70 years vs. 50-60 years, β = −0.21 SDs) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive in iduals who did not feel recovered from COVID-19, whereas in iduals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected in iduals persisted at almost 2 years since initial infection. Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for in iduals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in in iduals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer’s Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency. Abstracts were screened from a PubMed search query (COVID-19) AND (long COVID) AND (cognitive impairment), which returned 409 results between 2020 and January 20, 2023. Multiple systematic reviews and meta-analyses reported consistent observation of cognitive deficits following SARS-CoV-2 infection. Most studies of cognitive impairment have used small s les of less than 200 participants (including any controls), hospitalised cohorts, and measured cognitive impairment through self-report or dichotomised quantitative scales. Only one study was found with a s le size of more than 1,000 in iduals, included cases and controls across both community and hospital settings, and used objective cognitive testing that allowed quantitative estimation of the scale of any cognitive impairment. Previous studies have also been limited insofar as focusing on earlier infections in the first year of the COVID-19 pandemic, prior to introduction of vaccination and emerging variants. Studies focusing on longitudinal follow-up for those hospitalised with COVID-19 or with long COVID have found low rates of full recovery from long-term symptoms at up to one year since infection, including cognitive impairment. We report quantitatively on cognitive impairment following SARS-CoV-2 infection, from a large dataset of 4,000 in iduals with and without test-confirmed SARS-CoV-2 infection and a range of associated symptom durations, with mostly community-based cases. Importantly, we undertook two rounds of cognitive testing allowing longitudinal tracking of cognitive performance. Our longitudinal methods allowed us to report on deficits up to two years since infection, and following infections with SARS-CoV-2 variants that have emerged over 2021 and 2022, not previously studied in the context of COVID-19 and cognition. This study adds to existing evidence of cognitive deficits following SARS-CoV-2 infection, but finds important exceptions. At initial testing in mid-2021, cognitive deficits are not found for in iduals who self-report as feeling recovered from COVID-19, even for those with longest symptom duration. In follow-up testing in mid-2022, we find that deficits appear persistent for those with earlier infections and ongoing symptoms, consistent with previous smaller studies. More research is required to monitor those experiencing persistent cognitive impairment and understand the mechanisms underlying recovery.
Publisher: Springer Science and Business Media LLC
Date: 23-04-2020
DOI: 10.1007/S10654-020-00633-4
Abstract: The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 in iduals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 in idual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2010
DOI: 10.1142/S0219467810003731
Abstract: Colonoscopy is considered the gold standard for detection and removal of precancerous polyps in the colon. Being a difficult procedure to master, exposure to a large variety of patient and pathology scenarios is crucial for gastroenterologists' training. Currently, most training is done on patients under supervision of experienced gastroenterologists. Being able to undertake a majority of training on simulators would greatly reduce patient risk and discomfort. A next generation colonoscopy simulator is currently under development, which aims to address the shortfalls of existing simulators. The simulator consists of a computer simulation of the colonoscope camera view and a haptic device that allows insertion of an instrumented colonoscope to drive the simulation and provide force feedback to the user. The simulation combines physically accurate models of the colonoscope, colon and surrounding tissues and organs with photorealistic visualization. It also includes the capability to generate randomized case scenarios where complexity of the colon physiology, pathology and environmental factors, such as colon preparation, can be tailored to suit training requirements. The long term goal is to provide a metrics based training and skill evaluation system that is not only useful for trainee instruction but can be leveraged for skills maintenance and eventual certification.
Publisher: National Institute for Health and Care Research
Date: 05-2020
DOI: 10.3310/EME07030
Abstract: Neuroprotective drugs are needed to slow or prevent neurodegeneration and disability accrual in secondary progressive multiple sclerosis. Amiloride, fluoxetine and riluzole are repurposed drugs with potential neuroprotective effects. To assess whether or not amiloride, fluoxetine and riluzole can reduce the rate of brain volume loss in people with secondary progressive multiple sclerosis over 96 weeks. The secondary objectives that were assessed were feasibility of a multiarm trial design approach, evaluation of anti-inflammatory effects, clinician- and patient-reported efficacy and three mechanistic substudies. A multicentre, multiarm, randomised, double-blind, placebo-controlled, parallel-group Phase IIb trial with follow-up at 4, 8, 12, 24, 36, 48, 72 and 96 weeks. Patients, investigators (including magnetic resonance imaging analysts), and treating and independent assessing neurologists were blinded to the treatment allocation. The target s le size was 440 patients. Thirteen UK clinical neuroscience centres. Participants were aged 25–65 years, had secondary progressive multiple sclerosis with evidence of disease progression independent of relapses in the previous 2 years, and had an Expanded Disability Status Scale score of 4.0–6.5. Patients were ineligible if they could not have a magnetic resonance imaging scan had a relapse or steroids in the previous 3 months or had epilepsy, depression, bipolar disorder, glaucoma, bleeding disorders or significant organ comorbidities. Exclusion criteria were concurrent disease-modified treatments, immunosuppressants or selective serotonin reuptake inhibitors. Participants received amiloride (5 mg), fluoxetine (20 mg), riluzole (50 mg) or placebo (randomised 1 : 1 : 1 : 1) twice daily. The primary end point was magnetic resonance imaging-derived percentage brain volume change at 96 weeks. Secondary end points were new/enlarging T2 lesions, pseudoatrophy, and clinician- and patient-reported measures (including the Expanded Disability Status Scale, Multiple Sclerosis Functional Composite, Symbol Digit Modalities Test, low-contrast letter visual acuity, Multiple Sclerosis Impact Scale 29 items, version 2, Multiple Sclerosis Walking Scale, version 2, and questionnaires addressing pain and fatigue). The exploratory end points included measures of persistent new T1 hypointensities and grey matter volume changes. The substudies were advanced magnetic resonance imaging, optical coherence tomography and cerebrospinal fluid analyses. Between December 2014 and June 2016, 445 patients were randomised (analysed) to amiloride [ n = 111 (99)], fluoxetine [ n = 111 (96)], riluzole [ n = 111 (99)] or placebo [ n = 112 (99)]. A total of 206 randomised patients consented to the advanced magnetic resonance imaging substudy, 260 consented to the optical coherence tomography substudy and 70 consented to the cerebrospinal fluid substudy. No significant difference was seen between the active drugs and placebo in percentage brain volume change at week 96 as follows (where negative values mean more atrophy than placebo): amiloride minus placebo 0.0% (Dunnett-adjusted 95% confidence interval –0.4% to 0.5%), fluoxetine minus placebo –0.1% (Dunnett-adjusted 95% confidence interval –0.5% to 0.3%) riluzole minus placebo –0.1% (Dunnett-adjusted 95% confidence interval –0.6% to 0.3%). There was good adherence to study drugs. The proportion of patients experiencing adverse events was similar in the treatment and placebo groups. There were no emergent safety issues. There was a lower than expected uptake in the cerebrospinal fluid substudy. A multiarm Phase II paradigm is efficient in determining which neuroprotective agents to take through to Phase III trials. Amiloride, fluoxetine and riluzole were not effective in reducing the brain atrophy rate in people with secondary progressive multiple sclerosis. Mechanistic pathobiological insight was gained. To use the information gained from the Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial (MS-SMART) to inform future trial design as new candidate agents are identified. Current Controlled Trials ISRCTN28440672, NCT01910259 and EudraCT 2012-005394-31. This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation Vol. 7, No. 3. See the NIHR Journals Library website for further project information. This trial also received funding from the UK MS Society and the US National Multiple Sclerosis Society.
Publisher: Oxford University Press (OUP)
Date: 04-07-2022
DOI: 10.1093/BRAINCOMMS/FCAC182
Abstract: Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer’s disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Publisher: SPIE
Date: 21-03-2014
DOI: 10.1117/12.2042780
Publisher: Elsevier BV
Date: 06-2013
Publisher: Elsevier BV
Date: 10-2007
DOI: 10.1016/J.ACRA.2007.06.021
Abstract: The segmentation of textured anatomy from magnetic resonance images (MRI) is a difficult problem. We present an approach that uses features extracted from the magnitude and phase of the MRI signal to segment the bones in the knee. Moreover, we show that by incorporating shape information, more accurate and anatomically valid segmentations are obtained. Eighteen volunteers were scanned in a whole-body 3T clinical scanner using a transmit-receive quadrature extremity coil. A gradient-echo sequence was used to acquire three-dimensional (3D) volumes of raw complex image data consisting of phase and magnitude information. These images were manually segmented and features were extracted using a bank of Gabor filters. The extracted features were then used to train a support vector machine (SVM) classifier. Each image was then automatically segmented using both the SVM classifier and a 3D active shape model (ASM) driven by the classifier. The use of phase and magnitude information from both echoes obtained the most accurate classifier results with an average dice similarity coefficient of 0.907. The use of 3D ASMs further improved the robustness, accuracy and anatomic validity of the segmentations with an overall DSC of 0.922 and an average point to surface error along the bone-cartilage interface of 0.73 mm. Our results demonstrate that the incorporation of phase and multiple echoes improve the results obtained by the classifier. Moreover, we show that 3D ASMs provide a robust and accurate way of using the classifier to obtain anatomically valid segmentation results.
Publisher: Oxford University Press (OUP)
Date: 30-01-2018
DOI: 10.1093/BRAIN/AWX341
Publisher: Springer International Publishing
Date: 2015
DOI: 10.1007/978-3-319-19992-4_56
Abstract: The event-based model constructs a discrete picture of disease progression from cross-sectional data sets, with each event corresponding to a new biomarker becoming abnormal. However, it relies on the assumption that all subjects follow a single event sequence. This is a major simplification for sporadic disease data sets, which are highly heterogeneous, include distinct subgroups, and contain significant proportions of outliers. In this work we relax this assumption by considering two extensions to the event-based model: a generalised Mallows model, which allows subjects to deviate from the main event sequence, and a Dirichlet process mixture of generalised Mallows models, which models clusters of subjects that follow different event sequences, each of which has a corresponding variance. We develop a Gibbs s ling technique to infer the parameters of the two models from multi-modal biomarker data sets. We apply our technique to data from the Alzheimer's Disease Neuroimaging Initiative to determine the sequence in which brain regions become abnormal in sporadic Alzheimer's disease, as well as the heterogeneity of that sequence in the cohort. We find that the generalised Mallows model estimates a larger variation in the event sequence across subjects than the original event-based model. Fitting a Dirichlet process model detects three subgroups of the population with different event sequences. The Gibbs s ler additionally provides an estimate of the uncertainty in each of the model parameters, for ex le an in idual's latent disease stage and cluster assignment. The distributions and mixtures of sequences that this new family of models introduces offer better characterisation of disease progression of heterogeneous populations, new insight into disease mechanisms, and have the potential for enhanced disease stratification and differential diagnosis.
Publisher: Springer Berlin Heidelberg
Date: 2010
DOI: 10.1007/978-3-642-15745-5_48
Abstract: Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. Speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in medical image-guidance problems in which both speed and accuracy are vital.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 08-2010
DOI: 10.1097/CMR.0B013E32833BD0EC
Abstract: Proton magnetic resonance spectroscopy at 8.5 T ex vivo was used to investigate skin lesions for metabolic signatures to predict malignancy or indicate malignant potential. Magnetic resonance spectroscopy was performed on biopsy tissue obtained from 63 skin lesions and five melanoma metastases from 55 patients. S les were grouped and compared according to five clinically significant distinctions: melanoma (n=38) or nonmelanoma (n=30), primary melanoma (n=33) or secondary melanoma (involved nodes and distant metastases, n=5), primary melanoma (n=33) or nevi (n=8), malignant (n=46) or nonmalignant (n=22), and melanocytic (n=46) or nonmelanocytic (n=22). In all comparisons, the average magnetic resonance spectrum of each class lay within 1 standard deviation of the average spectrum of the other class. There was a higher average choline metabolite signal intensity in melanoma-containing biopsies compared with nonmelanoma biopsies. Discriminant analysis based on the intensity of the choline resonance alone achieved 69% accuracy in separation of melanoma and nonmelanoma tissue. Inclusion of other metabolite resonances in the analysis did not increase discrimination accuracy. Tissue heterogeneity in conventionally collected full thickness skin biopsies and possible biochemical variance within in idual tissue types limit classification accuracy using the methods and magnetic field strength that were earlier reported to provide accurate discrimination in other cancer types.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11556121_104
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11569541_7
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: Cambridge University Press
Date: 17-03-2005
Publisher: American Medical Association (AMA)
Date: 06-01-2021
Publisher: Springer Science and Business Media LLC
Date: 21-09-2014
Publisher: Springer Science and Business Media LLC
Date: 05-03-2008
Publisher: Wiley
Date: 21-07-2017
Publisher: Elsevier BV
Date: 06-2010
DOI: 10.1016/J.CMPB.2009.09.002
Abstract: A large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our approach we perform registration of T1-weighted MR images in less than 1 min and show the same level of accuracy as a classical serial implementation when performing segmentation propagation. This technology could be of significant utility in time-critical applications such as image-guided interventions, or in the processing of large data sets.
Publisher: Springer Science and Business Media LLC
Date: 15-07-2022
DOI: 10.1038/S41467-022-30695-9
Abstract: International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.
Publisher: Springer International Publishing
Date: 2015
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sebastien Ourselin.