Jan L. Bruse
Great Ormond Street Hospital
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Featured researches published by Jan L. Bruse.
Heart | 2017
Giovanni Biglino; Claudio Capelli; Jan L. Bruse; Giorgia M. Bosi; Andrew M. Taylor; Silvia Schievano
Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new ‘shape biomarkers’. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.
BMC Medical Imaging | 2016
Jan L. Bruse; Kristin McLeod; Giovanni Biglino; Hopewell Ntsinjana; Claudio Capelli; Tain-Yen Hsia; Maxime Sermesant; Xavier Pennec; Andrew M. Taylor; Silvia Schievano
BackgroundMedical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements.MethodsSteps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient’s anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters.ResultsThe computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors.ConclusionsThe suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.
The Journal of Thoracic and Cardiovascular Surgery | 2017
Jan L. Bruse; Abbas Khushnood; Kristin McLeod; Giovanni Biglino; Maxime Sermesant; Xavier Pennec; Andrew M. Taylor; Tain-Yen Hsia; Silvia Schievano; Sachin Khambadkone; Marc R. de Leval; Edward L. Bove; Adam L. Dorfman; G. Hamilton Baker; Anthony M. Hlavacek; Francesco Migliavacca; Giancarlo Pennati; Gabriele Dubini; Alison L. Marsden; Irene E. Vignon-Clementel; Richard Figliola
Objectives: Even after successful aortic coarctation repair, there remains a significant incidence of late systemic hypertension and other morbidities. Independently of residual obstruction, aortic arch morphology alone may affect cardiac function and outcome. We sought to uncover the relationship of arch 3‐dimensional shape features with functional data obtained from cardiac magnetic resonance scans. Methods: Three‐dimensional aortic arch shape models of 53 patients (mean age, 22.3 ± 5.6 years) 12 to 38 years after aortic coarctation repair were reconstructed from cardiac magnetic resonance data. A novel validated statistical shape analysis method computed a 3‐dimensional mean anatomic shape of all aortic arches and calculated deformation vectors of the mean shape toward each patients arch anatomy. From these deformations, 3‐dimensional shape features most related to left ventricular ejection fraction, indexed left ventricular end‐diastolic volume, indexed left ventricular mass, and resting systolic blood pressure were extracted from the deformation vectors via partial least‐squares regression. Results: Distinct arch shape features correlated significantly with left ventricular ejection fraction (r = 0.42, P = .024), indexed left ventricular end‐diastolic volume (r = 0.65, P < .001), and indexed left ventricular mass (r = 0.44, P = .014). Lower left ventricular ejection fraction, larger indexed left ventricular end‐diastolic volume, and increased indexed left ventricular mass were identified with an aortic arch shape that has an elongated ascending aorta with a high arch height‐to‐width ratio, a relatively short proximal transverse arch, and a relatively dilated descending aorta. High blood pressure seemed to be linked to gothic arch shape features, but this did not achieve statistical significance. Conclusions: Independently of hemodynamically important arch obstruction or residual aortic coarctation, specific aortic arch shape features late after successful aortic coarctation repair seem to be associated with worse left ventricular function. Analyzing 3‐dimensional shape information via statistical shape modeling can be an adjunct to long‐term risk assessment in patients after aortic coarctation repair.
Journal of Craniofacial Surgery | 2016
Maik Tenhagen; Jan L. Bruse; Naiara Rodriguez-Florez; Freida Angullia; Alessandro Borghi; Maarten J. Koudstaal; Silvia Schievano; O Jeelani; David Dunaway
AbstractThree-dimensional (3D) imaging is an important tool for diagnostics, surgical planning, and evaluation of surgical outcomes in craniofacial procedures. Gold standard for acquiring 3D imaging is computed tomography that entails ionizing radiations and, in young children, a general anaesthesia. Three-dimensional photographic imaging is an alternative method to assess patients who have undergone calvarial reconstructive surgery. The aim of this study was to assess the utility of 3D handheld scanning photography in a cohort of patients who underwent spring-assisted correction surgery for scaphocephaly. Pre- and postoperative 3D scans acquired in theater and at the 3-week follow-up in clinic were postprocessed for 9 patients. Cephalic index (CI), head circumference, volume, sagittal length, and coronal width over the head at pre-op, post-op, and follow-up were measured from the 3D scans. Cephalic index from 3D scans was compared with measurements from planar x-rays. Statistical shape modeling (SSM) was used to calculate the 3D mean anatomical head shape of the 9 patients at the pre-op, post-op, and follow-up. No significant differences were observed in the CI between 3D and x-ray. Cephalic index, volume, and coronal width increased significantly over time. Mean shapes from SSM visualized the overall and regional 3D changes due to the expansion of the springs in situ. Three-dimensional handheld scanning followed by SSM proved to be an efficacious and practical method to evaluate 3D shape outcomes after spring-assisted cranioplasty in individual patients and the population.
Revised Selected Papers of the 6th International Workshop on Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges - Volume 9534 | 2015
Jan L. Bruse; Kristin McLeod; Giovanni Biglino; Hopewell Ntsinjana; Claudio Capelli; Tain-Yen Hsia; Maxime Sermesant; Xavier Pennec; Andrew M. Taylor; Silvia Schievano
Coarctation of the Aorta CoA is a cardiac defect that requires surgical intervention aiming to restore an unobstructed aortic arch shape. Many patients suffer from complications post-repair, which are commonly associated with arch shape abnormalities. Determining the degree of shape abnormality could improve risk stratification in recommended screening procedures. Yet, traditional morphometry struggles to capture the highly complex arch geometries. Therefore, we use a non-parametric Statistical Shape Model based on mathematical currents to fully account for 3D global and regional shape features. By computing a template aorta of a population of healthy subjects and analysing its transformations towards CoA arch shape models using Partial Least Squares regression techniques, we derived a shape vector as a measure of subject-specific shape abnormality. Results were compared to a shape ranking by clinical experts. Our study suggests Statistical Shape Modelling to be a promising diagnostic tool for improved screening of complex cardiac defects.
The Annals of Thoracic Surgery | 2017
Jan L. Bruse; Elena Cervi; Kristin McLeod; Giovanni Biglino; Maxime Sermesant; Xavier Pennec; Andrew Taylor; Silvia Schievano; Tain Yen Hsia; Andrew M. Taylor; Sachin Khambadkone; Marc R. de Leval; T.-Y. Hsia; Edward L. Bove; Adam L. Dorfman; G. Hamilton Baker; Anthony M. Hlavacek; Francesco Migliavacca; Giancarlo Pennati; Gabriele Dubini; Alison L. Marsden; Irene E. Vignon-Clementel; Richard Figliola
BACKGROUND Aortic arch reconstruction after hypoplastic left heart syndrome (HLHS) palliation can vary widely in shape and dimensions between patients. Arch morphology alone may affect cardiac function and outcome. We sought to uncover the relationship of arch three-dimensional shape features with functional and short-term outcome data after total cavopulmonary connection (TCPC). METHODS Aortic arch shape models of 37 patients with HLHS (age, 2.89 ± 0.99 years) were reconstructed from magnetic resonance data before TCPC completion. A novel, validated statistical shape analysis method was used to compute a three-dimensional anatomic mean shape from the cohort and calculate the deformation vectors of the mean shape toward each patients specific anatomy. From these deformations, three-dimensional shape features most related to ventricular ejection fraction, indexed end-diastolic volume, and superior cavopulmonary pressure were extracted by partial least-square regression analysis. Shape patterns relating to intensive care unit and hospital lengths of stay after TCPC were assessed. RESULTS Distinct deformation patterns, which result in an acutely mismatched aortic root and ascending aorta, and a gothic-like transverse arch, correlated with increased indexed end-diastolic volume and higher superior cavopulmonary pressure but not with ejection fraction. Specific arch morphology with pronounced transverse arch and descending aorta mismatch also correlated with longer intensive care unit and hospital lengths of stay after TCPC completion. CONCLUSIONS Independent of hemodynamically important arch obstruction, altered aortic morphology in HLHS patients appears to have important associations with higher superior cavopulmonary pressure and with short-term outcomes after TCPC completion as highlighted by statistical shape analysis, which could act as adjunct to risk assessment in HLHS.
IEEE Transactions on Biomedical Engineering | 2017
Jan L. Bruse; Maria A. Zuluaga; Abbas Khushnood; Kristin McLeod; Hopewell Ntsinjana; Tain-Yen Hsia; Maxime Sermesant; Xavier Pennec; Andrew M. Taylor; Silvia Schievano
Objective: Todays growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensional patient shape data and results depend heavily on the chosen clustering distance metrics and linkage functions. In this study, we sought to assess clustering classification performance of various distance/linkage combinations and of different types of input data to obtain clinically meaningful shape clusters. Methods: We present a processing pipeline combining automatic segmentation, statistical shape modeling, and agglomerative hierarchical clustering to automatically subdivide a set of 60 aortic arch anatomical models into healthy controls, two groups affected by congenital heart disease, and their respective subgroups as defined by clinical diagnosis. Results were compared with traditional morphometrics and principal component analysis of shape features. Results: Our pipeline achieved automatic division of input shape data according to primary clinical diagnosis with high F-score (0.902 ± 0.042) and Matthews correlation coefficient (0.851 ± 0.064) using the correlation/weighted distance/linkage combination. Meaningful subgroups within the three patient groups were obtained and benchmark scores for automatic segmentation and classification performance are reported. Conclusion: Clustering results vary depending on the distance/linkage combination used to divide the data. Yet, clinically relevant shape clusters and subgroups could be found with high specificity and low misclassification rates. Significance: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and medical device development in complex disease.
Journal of Medical Devices-transactions of The Asme | 2017
Jan L. Bruse; Giuliano Giusti; Catriona Baker; Elena Cervi; Tain-Yen Hsia; Andrew M. Taylor; Silvia Schievano
Patients born with a single functional ventricle typically undergo three-staged surgical palliation in the first years of life, with the last stage realizing a cross-like total cavopulmonary connection (TCPC) of superior and inferior vena cavas (SVC and IVC) with both left and right pulmonary arteries, allowing all deoxygenated blood to flow passively back to the lungs (Fontan circulation). Even though within the past decades more patients survive into adulthood, the connection comes at the prize of deficiencies such as chronic systemic venous hypertension and low cardiac output, which ultimately may lead to Fontan failure. Many studies have suggested that the TCPCs inherent insufficiencies might be addressed by adding a cavopulmonary assist device (CPAD) to provide the necessary pressure boost. While many device concepts are being explored, few take into account the complex cardiac anatomy typically associated with TCPCs. In this study, we focus on the extra cardiac conduit vascular graft connecting IVC and pulmonary arteries as one possible landing zone for a CPAD and describe its geometric variability in a cohort of 18 patients that had their TCPC realized with a 20mm vascular graft. We report traditional morphometric parameters and apply statistical shape modeling to determine the main contributors of graft shape variability. Such information may prove useful when designing CPADs that are adapted to the challenging anatomical boundaries in Fontan patients. We further compute the anatomical mean 3D graft shape (template graft) as a representative of key shape features of our cohort and prove this template graft to be a significantly better approximation of population and individual patients hemodynamics than a commonly used simplified tube geometry. We therefore conclude that statistical shape modeling results can provide better models of geometric and hemodynamic boundary conditions associated with complex cardiac anatomy, which in turn may impact on improved cardiac device development.
American Journal of Obstetrics and Gynecology | 2017
Andrea Dall’Asta; Silvia Schievano; Jan L. Bruse; G. Paramasivam; Christine Tita Kaihura; David Dunaway; C. Lees
BACKGROUND: The antenatal detection of facial dysmorphism using 3‐dimensional ultrasound may raise the suspicion of an underlying genetic condition but infrequently leads to a definitive antenatal diagnosis. Despite advances in array and noninvasive prenatal testing, not all genetic conditions can be ascertained from such testing. OBJECTIVES: The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3‐dimensional ultrasound volumes and statistical shape modeling. STUDY DESIGN: Thirteen normal and 7 abnormal stored 3‐dimensional ultrasound fetal face volumes were analyzed, at a median gestation of 29+4 weeks (25+0 to 36+1). The 20 3‐dimensional surface meshes generated were aligned and served as input for a statistical shape model, which computed the mean 3‐dimensional face shape and 3‐dimensional shape variations using principal component analysis. RESULTS: Ten shape modes explained more than 90% of the total shape variability in the population. While the first mode accounted for overall size differences, the second highlighted shape feature changes from an overall proportionate toward a more asymmetric face shape with a wide prominent forehead and an undersized, posteriorly positioned chin. Analysis of the Mahalanobis distance in principal component analysis shape space suggested differences between normal and abnormal fetuses (median and interquartile range distance values, 7.31 ± 5.54 for the normal group vs 13.27 ± 9.82 for the abnormal group) (P = .056). CONCLUSION: This feasibility study demonstrates that objective characterization and quantification of fetal facial morphology is possible from 3‐dimensional ultrasound. This technique has the potential to assist in utero diagnosis, particularly of rare conditions in which facial dysmorphology is a feature.
Journal of Cardiovascular Magnetic Resonance | 2016
Jan L. Bruse; Hopewell Ntsinjana; Claudio Capelli; Giovanni Biglino; Kristin McLeod; Maxime Sermesant; Xavier Pennec; Tain-Yen Hsia; Silvia Schievano; Andrew M. Taylor
Background Left ventricular ejection fraction (LVEF) late after arterial switch operation (ASO) is often normal. However, some studies have shown increased indexed end diastolic volume (iEDV) and ventricular mass when compared to healthy controls. We sought to identify LV morphological differences between patients with transposition of the great arteries (TGA post ASO) and matched healthy control subjects. We hypothesised that besides a mere increase in ventricular volume, characteristic shape features can be associated with LV morphology post ASO. Ventricular shape being difficult to quantify using traditional morphometrics, a novel, validated non-parametric statistical shape modelling framework (SSM) was used to analyse 3D anatomical models without the need for landmarking.