ventricular structure in arvc: going beyond volumes as a measure

RESEARCH Open Access

Ventricular structure in ARVC: goingbeyond volumes as a measure of riskKristin McLeod1,4* , Samuel Wall1,4, Ida Skrinde Leren2,3,4, Jørg Saberniak2,3,4 and Kristina Hermann Haugaa2,3,4

Abstract

Background: Altered right ventricular structure is an important feature of Arrhythmogenic Right VentricularCardiomyopathy (ARVC), but is challenging to quantify objectively. The aim of this study was to go beyondventricular volumes and diameters and to explore if the shape of the right and left ventricles could be assessedand related to clinical measures. We used quantifiable computational methods to automatically identify andanalyse malformations in ARVC patients from Cardiovascular Magnetic Resonance (CMR) images. Furthermore,we investigated how automatically extracted structural features were related to arrhythmic events.

Methods: A retrospective cross-sectional feasibility study was performed on CMR short axis cine images of 27 ARVCpatients and 21 ageing asymptomatic control subjects. All images were segmented at the end-diastolic (ED) andend-systolic (ES) phases of the cardiac cycle to create three-dimensional (3D) bi-ventricle shape models for eachsubject. The most common components to single- and bi-ventricular shape in the ARVC population were identifiedand compared to those obtained from the control group. The correlations were calculated between identifiedARVC shapes and parameters from the 2010 Task Force Criteria, in addition to clinical outcomes such as ventriculararrhythmias.

Results: Bi-ventricle shape for the ARVC population showed, as ordered by prevalence with the percent of totalvariance in the population explained by each shape: global dilation/shrinking of both ventricles (44 %), elongation/shortening at the right ventricle (RV) outflow tract (15 %), tilting at the septum (10 %), shortening/lengthening ofboth ventricles (7 %), and bulging/shortening at both the RV inflow and outflow (5 %). Bi-ventricle shapes weresignificantly correlated to several clinical diagnostic parameters and outcomes, including (but not limited to)correlations between global dilation and electrocardiography (ECG) major criteria (p = 0.002), and base-to-apexlengthening and history of arrhythmias (p = 0.003). Classification of ARVC vs. control using shape modes yieldedhigh sensitivity (96 %) and moderate specificity (81 %).

Conclusion: We presented for the first time an automatic method for quantifying and analysing ventricular shapesin ARVC patients from CMR images. Specific ventricular shape features were highly correlated with diagnosticindices in ARVC patients and yielded high classification sensitivity. Ventricular shape analysis may be a novelapproach to classify ARVC disease, and may be used in diagnosis and in risk stratification for ventricular arrhythmias.

Keywords: ARVC, CMR, Principal component analysis, Shape modes, Risk assessment

* Correspondence: [email protected] Modelling Department, Simula Research Laboratory, PO Box 134,Oslo, Norway4Center for Cardiological Innovation, Oslo, NorwayFull list of author information is available at the end of the article

© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

McLeod et al. Journal of Cardiovascular Magnetic Resonance (2016) 18:73 DOI 10.1186/s12968-016-0291-9

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BackgroundArrhythmogenic right ventricular cardiomyopathy (ARVC)is an inherited cardiomyopathy affecting approximately 1 in5000 individuals [1]. The disease is characterized by desmo-somal dysfunction, leading to cell necrosis and fibro-fattyreplacement of the myocardium, with disease generallystarting in the right ventricle. Desmosomal dysfunctionleads to changes in heart structure and function also affect-ing the electrical propagation through the ventricles, whichmay cause life-threatening ventricular arrhythmias.Diagnosis and treatment is relatively straightforward in

severe cases of definite ARVC. However, in mild-to-moderate cases diagnosis is challenging due to the com-plex nature of the disease, and treatment planning ismade on a case-by-case basis. Diagnosis of ARVC is cur-rently guided by the Task Force criteria (TFC), revised in2010 [2]. Cardiovascular Magnetic Resonance (CMR) iscommonly used for diagnostic purposes in ARVC pa-tients to identify global and regional structural abnor-malities, dysfunction and for identification of fatty/fibrotic regions [3, 4]. Although regional structural ab-normalities at the inflow tract, outflow tract and apex ofthe RV are known in these patients [5], quantitativemeasures of structural abnormalities are generally lim-ited to global measures including end-diastolic volume(EDV), end-systolic volume (ESV) or ejection fraction(EF) by CMR, in addition to right ventricular outflowtract (RVOT) diameter from echocardiography. Thoughtechniques for imaging and assessment of the entire RVare being developed using 3D echo [6], echocardio-graphic assessment of ARVC has typically been limitedto 2D images, giving information for only a small part ofthe RV. Structural abnormalities can be qualitativelyanalysed from images by visual assessment. However,comparing qualitative measures from different observersand different patients is challenging and has severallimitations [5].Descriptive three-dimensional (3D) measures can give

more information in terms of localisation and size of anabnormality, e.g. bulging or aneurysms. In terms of com-putational methods, the normal structure (shape) can bedescribed by the mean (average) shape in the populationand the degree of variation (i.e. the variance) can be de-scribed by the modes (shapes) around the mean. Thistask was recently addressed for the left ventricle (LV)using a mean shape model and deforming the mean toeach subject by assigning landmarks, such as the base,apex and junctions between the ventricles, to personalisethe shape and to correlate with clinical measures inatherosclerosis patients [7]. A similar approach usingprincipal component analysis (PCA) [8], but without re-quiring landmarks to compute the shape modes, wasused to correlate right ventricle (RV) shapes with clinicalmeasures in Tetralogy of Fallot patients [9].

ARVC diagnosis, monitoring of disease progressionand patient risk stratification are currently limited byexamination techniques, heterogeneous structural dis-ease progression and reliable markers of risk for lifethreatening events [10–13]. In this work we sought to gobeyond ventricular volumes and ejection fraction asmeasures of structural abnormalities by using quantifi-able computational methods to automatically identifyand analyse malformations in ARVC patients comparedto control subjects following the method described in[14]. Furthermore, we aimed to investigate if specificstructural shapes were related to clinical parameters andventricular arrhythmias in ARVC. We hypothesized thatthese specific shape features, quantified using moderncomputational approaches, would be associated withclinical outcome and could give additional informationon the disease.

MethodsStudy populationWe included 27 ARVC patients from the Unit of Gen-etic Cardiac Diseases, Oslo University Hospital, Rikshos-pitalet, Oslo, Norway. ARVC was diagnosed accordingto the current TFC [2] defined as definite ARVC (2major criteria, 1 major and 2 minor criteria, or 4 minorTFC diagnostic criteria from different categories), bor-derline ARVC (1 major and 1 minor or 3 minor TFCdiagnostic criteria) and possible ARVC (1 major or 2minor TFC diagnostic criteria). ARVC patients withshort-axis CMR available at the time of the study (2015)and scanned on a machine from the same vendor (Sie-mens) and with the same magnet strength (1.5 T) werechosen for the study. In addition, 21 asymptomatic sub-jects were recruited as ageing controls to compare thedifference between age-related and disease-related re-modelling. These controls were taken from a cohort ofpatients referred with suspected ischemic cardiac dis-ease, but with normal angiography excluding coronaryartery disease and with normal volumes and EF by echo-cardiography and by CMR. The control subjects wererandomly chosen, with no prior evaluation of the imagesto avoid bias in subject selection.Parameters from the TFC 2010 were collected from

the medical records, including electrocardiography(ECG) findings (depolarization and repolarization abnor-malities from resting ECG and analyses of signal-averaged ECG), structural and functional alterations byechocardiography and CMR, genetic findings and clin-ical outcome. Echocardiography was performed aspreviously described in [15] and parameters for ARVCdiagnosis by TFC were analysed. Ventricular arrhythmiaswere defined as documented non-sustained or sustainedventricular tachycardia by Holter, exercise test or ICDrecordings. Also, syncope of assumed cardiac origin and

McLeod et al. Journal of Cardiovascular Magnetic Resonance (2016) 18:73 of 14

aborted cardiac arrests were recorded. BSA wascomputed for each patient using the Dubois formula:BSA(m2) = 0.007184 ×weight(kg)0.425 × height(cm)0.725.

Cardiovascular magnetic resonanceIn order to analyse ARVC-specific structural abnormal-ities in comparison to normal ventricular structural pat-terns, CMR-based datasets of ARVC patients andcontrol subjects were collected from retrospective data-bases and pooled for analysis. All participants had theCMR performed with a Siemens 1.5 T scanner, and gavewritten informed consent. Short-axis CMR was acquiredfor each subject, fully covering both ventricles, withvoxel size between 1.33mm and 1.65mm and slicethickness of 6mm.

Mean anatomical model constructionAutomatic techniques to detect heart wall boundaries,based on image contrast between tissue and the bloodpool, were applied first. User interaction was then re-quired to visually check that the algorithms gave goodmatching between the contours and the images. If seg-mentation errors were observed (i.e. if the image seg-mentation method was unable to correctly identify theborders), correction of the surface models was per-formed manually. All segmentation was performed withthe Segment software, MedViso (http://www.medviso.-com/) [16] by a single user, prior to any statisticalanalysis.Fully automatic methods for pre-processing the data

from the segmentation were developed to enablepopulation-wide comparison and analysis of the struc-ture. The anatomical models were pre-processed tofirstly correct for the slice misalignment caused bybreath holds, then to eliminate differences in orientationand pose due to the different image locations, and finallyto account for the different temporal sampling of eachimage sequence. Once all subjects were aligned to acommon space with a common temporal sampling, theED and ES phases were identified and the mean shapes

for each time instance were computed individually on eachsurface model (the LV endocardium, LV epicardium andRV endocardium see Fig. 1). Note that the RV epicardiumwas not included given the thin wall of the RV, and result-ing difficulties in delineating these from CMR images. SeeAdditional file 1 for a full description of the methods usedto obtain 3D models from the CMR images.The mean (average) and shape modes (dominant shape

patterns) in the data were computed from anatomicalmodels that represented the shape of the ventricles (theendocardial and epicardial surfaces) in 3D, following astatistical analysis framework using PCA, as described indetail in [17] and summarised in Fig. 2. The cut-off forthe number of shapes was set to the number required tocapture 80 % of the shape variance in the population.Further shapes can be computed (up to one less thanthe number of subjects in the population = 26), howeveronly the most significant, and most widely presentshapes in the population were of interest. Subsequentshapes captured little variance in the population andwere thus considered to be subject-specific rather thanpopulation-wide (i.e. outlier features). Shapes were com-puted separately for each population.

Statistical correlation of shape features with clinicaldiagnostic indicesPCA was applied to each processed data set to extract thedominant shape trends (called shape modes, hereafter re-ferred to as ‘shapes’) in each population (in order of dom-inance). Using this method, the shape of a given patient isdescribed as a linear combination of these shapes, provid-ing a reduced-order representation of the structure ofeach patient’s heart. Shapes are visualised at ±1 standarddeviation (SD), though the sign is arbitrary in PCA and isnot significant of a ‘positive’ or ‘negative’ shape. Two typesof analysis were performed for each population; singleventricle analysis of both the RV and LV (to highlightventricle-specific features), and bi-ventricle analysis (to ac-count for ventricular interaction). In both the control andARVC populations, five shapes were sufficient for

Fig. 1 Two views of a short-axis CMR with the subject-specific 3D bi-ventricle model overlaid. The LV endocardium is shown in white, the LVepicardium is shown in red, and the RV endocardium in blue


http://www.medviso.com/

http://www.medviso.com/

capturing 80 % of the variance in shape observed, bothwhen computing the shapes individually for each ventricle,and when computing combined shape modes (i.e. 5 RVmodes, 5 LV endocardium modes, 5 LV epicardiummodes and 5 biventricle modes each captured 80 % of thevariance).In order to analyse the relative statistical importance

of each ventricle independently, and to determine wherein the cardiac cycle shape metrics were more indicativeof clinical measures, canonical correlation analysis(CCA) was performed. Using CCA, the correlation be-tween the combination of all of the dominant shapemodes in the dataset and the clinical outcomes wascomputed. This type of analysis was performed to ac-count for the fact that each subject heart is a combin-ation of modes (shapes), and these combinations couldinteract in the disease state.Correlations between biventricle shapes and the clinical

indices were computed using both non-parametric statis-tical tests (Kendall’s τb and Spearman’s ρ), and a paramet-ric statistical test (Pearson’s r). For each clinical index,the correlation was computed individually due to theoverlap of the indices (i.e. univariate analysis, as opposedto multivariate analysis). The cut-off for statistical signifi-cance was set at p < 0.05.The currents distance [18] was used to compute a global

measure of the difference between the subject-specific bi-ventricle shapes and the mean bi-ventricle shape models(ARVC or control). This was performed to test if a simple

1-parameter global bi-ventricle shape metric could differ-entiate between the two populations. The currents dis-tance is a global measure of the difference between twosets of currents (each representing a structural model of agiven subject). Using a leave-one-out protocol, the bi-ventricle mean of the control group was computed from20 subjects (N-1), and the distance to the excluded subject(the Nth subject) was computed in the space of currents.Similarly, the distance from the ARVC patients to the con-trol bi-ventricle mean was also computed in the space ofcurrents. In addition to this global measure, a summedregional measure was computed by adding together theshape mode loadings after scaling and normalisation. Asimilar leave-one-out protocol was applied to thissummed regional measure.To test the predictive power of using just the distance-

to-mean global measure or the summed regional measureto distinguish the groups in terms of bi-ventricular shape,a k-nearest-neighbour classifier [19] (with three neigh-bours) was constructed for each measure. The k-nearest-neighbour classifier finds the closest k (in our case k = 3)neighbours with respect to the measure of interest (in ourcase the distance-to-mean or summed regional measure)and based on the labels of the closest neighbours, a pre-dicted label is assigned, along with the probability of be-longing to the predicted class. We used a leave-one-outapproach with this algorithm, where for each subject, theclassifier was trained with the labeled data of the other 26subjects, and the trained classifier then used to predict the

Fig. 2 The statistical analysis framework to analyse the anatomical differences between ARVC patients and asymptomatic control subjects byconsidering the mean (average) anatomy as the model of interest and describing all other subjects as a deformation of this model. The subject-specific deformations can be analysed to extract dominant anatomical features in the population (modes), as well as studying the relationshipbetween the anatomical features and clinical diagnostic parameters. A real-world example is shown by considering the geography of the earth,representing this in 3D with a scale model (a globe), projecting this to 2D (a map), and extracting dominant geographical features such asaltitude, to correlate with an external geographical parameter (temperature for example)


class of the left-out subject. Based on the predicted labelsand probabilities computed from each leave-one-out ex-periment, ROC curves were generated and the optimalcut-off (i.e. the threshold value above which subjects areclassed as AVRC) was chosen by optimising the accuracy,specificity and sensitivity. The Matlab software fromMathworks (http://www.mathworks.com/) (version2012b) was used for the classification, with built-in func-tions to perform the k-nearest-neighbour training (usingthe ClassificationKNN function), and the prediction basedon this trained classifier (using the predict function).

ResultsClinical characteristicsTwenty-seven ARVC patients were included (13 (48 %)male, age 38 ± 14 years) as well as 21 controls (12 (57 %)

male, age 64 ± 8). Among the ARVC patients, 12 (44 %)had a history of ventricular tachycardia (VT) or ven-tricular fibrillation (VF), 4 (15 %) patients had survived acardiac arrest and 8 (30 %) reported previous syncope.Furthermore, 8 (30 %) had an implantable cardioverterdefibrillator (ICD) implanted after the CMR examin-ation. Regarding criteria from the 2010 TFC, 12 (44 %)fulfilled major criteria on ECG, 2 had ventricular fat in-filtration and 6 had fibrosis by CMR. The number ofmajor and minor criteria according to the TFC variedfrom 1 to 5 and 0 to 2 respectively (see Table 1 for allclinical diagnostic indices).

Single ventricle analysisThe RV shape features (shown in Fig. 3) have visiblyidentifiable differences. This was in contrast to the LV

Table 1 Table of clinical parameters

ARVC diagnosis TFC major TFC minor CMR major CMR minor VT/VF ICD Cardiac arrest Syncope ECG major SA-ECG Fat Fibrosis

1 1 0 0 0 0 0 0 0 0 0 0 1

1 1 0 0 0 0 0 0 1 0 0 0 0

3 2 0 0 0 1 1 1 1 0 0 0 0

3 1 2 0 1 1 0 0 0 0 1 1 1

3 2 2 1 0 0 0 0 0 1 1 0 1

3 2 1 1 0 0 0 0 0 0 0 0 0

3 2 1 1 0 1 0 0 1 1 0 0 0

3 5 1 0 1 1 1 1 1 1 1 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0

3 2 0 1 0 1 1 1 1 0 0 0 1

2 1 1 0 0 0 0 0 0 0 1 0 0

3 2 1 0 0 1 1 1 1 0 1 0 0

3 4 1 1 0 1 1 0 0 1 1 0 0

3 5 0 1 0 1 1 0 0 1 1 0 1

3 4 1 1 0 1 1 0 0 1 1 0 1

3 2 1 0 0 1 0 0 1 1 0 0 0

3 5 0 1 0 1 1 0 1 1 1 0 0

3 2 0 0 0 0 0 0 0 1 0 0 0

3 1 2 0 0 0 0 0 0 1 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 1 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0

3 3 1 1 0 1 0 0 0 1 0 0 0

3 2 0 1 0 0 0 0 0 1 0 0 0

2 1 1 1 0 0 0 0 0 0 1 1 0

Clinical findings and diagnostic indices from the 2010 TFC [2] for the 27 ARVC patients. Patients reported as having an ICD were scanned prior to implantation.ARVC diagnosis: 3 = definite, 2 = borderline, 1 = possibleMajor number of TFC major criteria, Minor number of TFC minor criteria, VT/VF history of ventricular arrhythmias, ICD implantable cardioverter defibrillator,ECG major electrocardiogram major TFC criteria, SA-ECG TFC from signal-averaged electrocardiogram


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shapes, which showed few visible distinguishing features.The first five RV shapes at ±1 standard deviation (SD)are shown in Fig. 3 from different views to emphasisethe largest shape difference observed for each (in termsof differences from the control group mean). Sincesimilar shapes were found at ED and ES for the RVshapes, only the ES shapes are shown (ES was slightlymore discriminant, see Table 2). The shapes were or-dered according to how commonly they were presentin this population (from most to least common). Thefirst ARVC RV shape, which explained 44 % of theshape variance in the population, appeared to showdilation/shrinking, particularly around the apical re-gion. The second RV shape explained 15 % of the vari-ance and showed elongation/shortening at the RVoutflow tract. The third RV shape, which explained10 % of the variance, showed a tilting from the apex tothe base at the septum (i.e. RV septum tilting inwards/outwards at the base with respect to the location ofthe apex). The fourth RV shape, which explained 7 %of the variance, showed lengthening/shortening in theRV for this group. Finally, the fifth RV shape, whichexplained 5 % of the variance, showed abnormal

bulging/shrinking at both the RV inlet and outlet (incontrast to RV shape 2 which dilates at +1SD, but notin combination to the RVOT elongation). Meanwhile,labelling of the LV shapes according to what they ana-tomically represent was not possible given the smallamount of visually distinguishable shape differencesbetween the first LV shape and the other LV shapes.The first shapes computed for the LV alone in this sec-tion of the analysis were visually similar to the first LVshapes computed from the bi-ventricle analysis foreach population (described in the following section)and are shown in Fig. 4.The canonical correlations between the combination

of the first five shapes for each ventricle individually,and separately for the LV endocardium and LV epicar-dium (representing >80 % of the variance in the popula-tion in all configurations) and clinical parameters,computed using CCA, are given in Table 2. Commoncorrelations were found for the LV and the RV for thenumber of major criteria, history of arrhythmias, historyof cardiac arrest and presence of fibrosis. Additional cor-relations were found in the RV for ECG major criteriaand signal-averaged ECG, and in the LV for gender.

(See figure on previous page.)Fig. 3 The first five RV shapes, in descending order from most prominent in the population to least prominent, for the control group (left) andthe ARVC group (right) shown at ±1 standard deviation (SD) at the ES phase. In each box, the mean for the group (control or ARVC) is shown asreference in black wireframe, and the shape at each extreme (±1 SD) is shown in solid white. The yellow arrows indicate the location anddirection of greatest shape variance in the ARVC shapes, which showed global dilation/shrinking for shape 1, elongation/shortening at the RVoutflow tract for shape 2, titling (leaning) from apex to base at the septum for shape 3, lengthening/shortening for shape 4 and bulging/shortening at both the RV inlet and RV outlet for shape 5. These shapes are consistent with known shape abnormalities in these patients. Notethat the sign (+/-) is not indicative of positive/negative change in PCA and is thus arbitrarily assigned. Abbreviations: inf: inferior, sep: septal, ant:anterior, fw: RV free wall

Table 2 Table of correlations

ClinicalIndices

LV endocardium LV epicardium RV endocardium

ED phase ES phase ED phase ES phase ED phase ES phase

BSA p = 0.46 p = 0.17 p = 0.06 p = 0.05 p = 0.32 p = 0.12

Gender p = 0.20 p = 0.03 p = 0.05 p = 0.03 p = 0.22 p = 0.08

No. major p = 0.24 p = 0.02 p = 0.14 p = 0.005 p = 0.04 p = 0.004

No. minor p = 0.57 p = 0.94 p = 0.81 p = 0.94 p = 1.00 p = 0.88

VT/VF p = 0.17 p = 0.18 p = 0.05 p = 0.12 p = 0.04 p = 0.03

Cardiac arrest p = 0.43 p = 0.63 p = 0.02 p = 0.13 p = 0.07 p = 0.04

Syncope p = 0.78 p = 0.41 p = 0.26 p = 0.77 p = 0.90 p = 0.57

ECG major p = 0.18 p = 0.17 p = 0.21 p = 0.15 p = 0.02 p = 0.04

SAECG p = 0.12 p = 0.22 p = 0.07 p = 0.11 p = 0.19 p = 0.003

Fat p = 0.21 p = 0.49 p = 0.19 p = 0.22 p = 0.46 p = 0.61

Fibrosis p = 0.04 p = 0.31 p = 0.12 p = 0.083 p = 0.04 p = 0.33

Table of canonical correlations between the first 5 shape modes, and each of the clinical indices (significant correlations with p < 0.05 shown in bold). Theindividual RV shapes are shown in Fig. 3. The individual LV shapes are not shown, though an example of the first LV shape can be see in Fig. 4 from thebi-ventricle analysisLV left ventricle, RV right ventricle, BSA body surface area, No. major number of major task force criteria (TFC), No. minor number of minor TFC, VT/VF history ofventricular arrhythmias, ECG major major TFC from electrocardiogram, SAECG major TFC from signal-averaged ECG, ED end-diastole, ES end-systole


Bi-ventricular analysisThe mean structure and most dominant shape featuresfor each population were computed for the bi-ventriclemodels at ED and ES. Similar results were found at bothphases for all shapes and the results at ES for each groupfor the first shape at ±1 SD are shown in Fig. 4. At -1SD, some differences between the ARVC group and thecontrol group at the septum are visible, as shown withyellow arrows in the middle and bottom rows. For theremaining shapes, the RV part of the bi-ventricle shapeswere visually similar to those computed individually forthe RV in Fig. 3 (and are thus not shown again), and asin the single-ventricle analysis of the LV, little LV shapedifferences were visually distinguishable.In order to highlight the identified individual shape

features that make up the ARVC population, CMR im-ages of the patients with the largest bi-ventricle shapeloadings are shown in Fig. 5. Using these outliers, it is

possible to visualise the abnormalities on the imagesdirectly. Note that T1-weighted images are shown forvisualisation purposes only and were not used for theanalysis.Using the global measure of currents computed from

the bi-ventricle mean, the predictive accuracy from thetrained classifier (trained from the currents distancemeasures only) was 75 %, with 85 % accuracy of predic-tion for the ARVC subjects (i.e. high sensitivity) and62 % accuracy for the control group (i.e. low specificity).The currents distances are plotted for each subject inFig. 6 (left), for the control group (coloured in blue) andthe ARVC group (plotted in red). The subjects that weremisclassified with the trained classifier are indicated withcrosses. The receiver operating characteristic (ROC)curve summarising the performance of the classifier isshown in Fig. 6 (right). Using a single measure ofdistance amounts to quantifying global bi-ventricle

+1 S.D mean -1 S.D

ARVC - ES

+1 S.D mean -1 S.D

Controls - ES

Fig. 4 The mean and first PCA shape (shown at ±1SD) for the ARVC group (left) and the control group (right) at the end systolic (ES) frame.The first shapes capture the size variance in each population, since no size correction was performed prior to analysis. The yellow arrows at -1SDhighlight the narrow RV that is tilted towards the LV, the yellow circle at -1SD highlights the narrow apex, the yellow circle at the mean showsa bulge in the RV, and the yellow double sided arrows at +1SD highlight the dilation of the RV. Note that the top row is showing the RV in frontof the LV

Bi-ventricle shape 1 - global RV dilation

SA -

base-apex length

LA SA

SA

RV inlet/outlet lengthening

Bi-ventricle shape 3 septal tilting

T1 LA

T1 LA S

Fig. 5 CMR of the patients with the largest shape loadings for each shape. The short axis (SA) views around the basal and apical regions showedglobal dilation in patient 26, the SA and T1-weighted long axis (LA) images for patient 24 displayed the elongation at the RV outflow tract (RVOT),the T1-weighted LA image of patient 3 showed the tilting of the RV from the apex to the base at the septum, the LA view showed shortening ofthe RV (shown in the SA view) for patient 4, and the SA view of patient 16 showed dilation at the RV inlet and outlet. Note that the T1 imagesshown here are for visualisation purposes only, but were not used as a part of this study


differences and thus neglects more localised abnormal-ities such as bulging or elongation.The same classifier trained similarly on the summed

bi-ventricle shape loadings from the regional bi-ventricleshape descriptors gave a predictive accuracy of 90 %,with 96 % accuracy of prediction for the ARVC subjects(i.e. high sensitivity) and 81 % accuracy for the controlgroup (i.e. moderate specificity). The summed shapeloadings (after normalisation and scaling) are plotted foreach subject in Fig. 7 (left), for the control group(coloured in blue) and the ARVC group (plotted in red).The subjects that were misclassified with the trainedclassifier are indicated with crosses. The only misclassi-fied ARVC patient was one with a borderline diagnosisof ARVC. The receiver operating characteristic (ROC)curve summarising the performance of the classifier isshown in Fig. 7 (right).The statistically significant (p < 0.05) ρ values from Spear-

man’s rank correlation coefficient test are shown in Fig. 8

between the clinical diagnostic indices and each bi-ventricle shape (left) and with clinical metrics com-puted automatically from the Segment software,shown for comparison (right), showing only the ab-solute values of ρ (since degree of correlation andnot direction is of interest in this study). Similar re-sults were found for Kendall’s rank correlation coef-ficient and Pearson’s product-moment correlationcoefficient. Note that given the small population sizeand exploratory nature of this study, correction formultiple correlations was not performed.The bi-ventricle shape loadings are plotted against the

clinical diagnostic indices in Fig. 9 for the most statisti-cally significant shape/index combinations. The shapeloadings represent how present each shape was in eachpatient (e.g. how elongated the RVOT was from shape2). Clear trends were visible, where larger loadings ofshape 1 appear to suggest more major criteria, and simi-larly for shape 5. Patients with ECG major criteria had

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Fig. 6 Left: The currents distances (see [17] for a full description of this distance measure) plotted for all subjects with controls shown in blue,ARVC shown in red. Crosses denote misclassified subjects (i.e. controls misclassified as ARVC, ARVC misclassified as controls) from the classifiertrained on the currents distance measures in a leave-one-out protocol. The line that divides the predicted groups is shown in black. Right: TheROC curve from the classification (blue line) and predictive accuracy that would result from random guess (dashed diagonal black line)

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itivi

ty)

Fig. 7 Left: The sum of the shape loadings (after normalisation and scaling) for all subjects with controls shown in blue, ARVC shown in red.Crosses denote misclassified subjects (i.e. controls misclassified as ARVC, ARVC misclassified as controls) from the classifier trained on the sum ofthe loadings using a leave-one-out protocol. Note that the misclassified ARVC patient was a patient with a borderline diagnosis of ARVC. The linethat divides the predicted groups is shown in black. Right: The ROC curve from the classification (blue line) and predictive accuracy that wouldresult from random guess (dashed diagonal black line)


larger loadings of shape 1. Larger loadings of shape 4and 5 appear to suggest higher rates of arrhythmias.

DiscussionIn this study, using computational methods, CMR datafrom ARVC patients and controls was summarized intomodels of ventricular structure dominated by 5 particularventricular shapes features for both the single ventricle

(i.e. 5 RV shapes, 5 LV endocardium shapes, 5 LV epicar-dium shapes) and bi-ventricle analyses (5 combined LV/RV shapes). This represents a novel approach to ARVCimaging by using true 3D structure of the ventricles incontrast to traditional measures of function and simplechamber diameters. The dominant RV shapes in theARVC population were global RV dilation/shrinking,elongation/shortening at the RVOT, tilting at the septum,

Fig. 8 Absolute ρ values computed using Spearman’s non-parametric statistical test plotted for the bi-ventricle shapes showing only the statisti-cally significant (p < 0.05) values. Correlations with additional standard clinical indices, computed from the Segment software [16], are also shown:LVEDV/RVEDV: left ventricle (LV)/right ventricle (RV) end-diastolic volume, RVESV: RV end- systolic volume; LVEF: LV ejection fraction

Fig. 9 Loadings of bi-ventricle shape 1 plotted against the number of major TFC criteria (top left), ECG major criteria (top right), bi-ventricle shape4 plotted against history of arrhythmias (bottom left), and bi-ventricle shape 5 plotted against the number of major TFC criteria (bottom right).The loadings represent how present the shape is in each patient. Note that shape 1 represented global dilation of the RV, shape 4 representedbase-apex length of the RV, and shape 5 represented bulging at the inlet and outlet of the RV (see Fig. 5)


shortening/lengthening of the ventricles and bulging/shortening at the RV inlet and outlet. All these are knownARVC structural features, which with the presented toolcan now be quantitatively measured in new patients. Ourstudy also related the importance of the different shapefeatures to ARVC patients’ clinical findings and outcome.We suggest that this image shape analysis approach inARVC may help understanding of ARVC development,may help monitor disease progression, and potentiallyhelp risk stratification of ventricular arrhythmias.

Frequency of different shapes and clinical relationshipsThe most common RV shape (shape 1) was character-ized by RV dilation and explained 44 % of the shapevariance in the population. RV dilation is known as anearly sign of ARVC [15]. Shape 1 correlated to ECG cri-teria, which are also known to be an early sign of ARVC[11]. Interestingly, bi-ventricle shape 1 (which also rep-resented RV dilation) was also correlated to fibrosis byCMR. Shortening/lengthening of the ventricles (shape 4)and bulging/shortening at the RV inlet and outlet (shape5) were less common in the ARVC population, but weremore highly correlated with adverse clinical outcomessuch as ventricular arrhythmias. The correlations betweenthe bi-ventricle shape modes and clinical symptoms wereconsistent with expected relationships between shape ab-normalities and ECG major criteria, signal-averaged ECG,history of arrhythmias, syncope, as well as presence of fi-brosis on CMR. Interestingly, while volume measureswere correlated to VT/VF, the first shape was not corre-lated, despite the fact that this shape captured the dilationof both ventricles. This suggests that the shape maycontain other features not related to dilation that are notdirectly visible.We correlated the shapes to clinical parameters in-

cluded in the TFC, and to clinical outcome such as ven-tricular arrhythmias. Due to the small number ofincluded patients in this study, the correlations to clin-ical parameters were intended as a pilot study andshould be interpreted with care. However, these pilotresults indicate that studying the relationship betweensymptoms and the shape modes extracted frompopulation-based analysis could provide insight intowhich shape modes may be indicative of symptomaticARVC. Therefore, future studies should explore if theassessment of shape can predict ventricular arrhythmiaswhen new patients present large loadings of shape 4 and5. Furthermore, patients presenting with lower loadingsof shape 4 and 5 would potentially be at lower risk ofevents. Such analysis could thus be used to determinepatient risk of future events, as a secondary means ofsupport for existing clinical metrics. Given the explora-tory nature of the present study where no specific hy-potheses were pre-specified and where the objective was

to identify important shape features and their relation-ship with clinical outcomes, no correction for multipletesting was performed. With the number of comparisonsmade here, this increases the likelihood of making aType I error (i.e. identifying an effect that doesn’t exist).However, for this study, these are preferable to Type IIerrors (i.e. missing an effect when one existed) given thepotentially lethal nature of this condition.Correlating shape with clinical indices was addressed

in a recent study of regional structural abnormalities inmutation-positive ARVC patients using a manual, quali-tative identification of abnormal regions [11], in contrastto the automatic methods proposed in this work. Whilestatistically significant correlations were found betweenqualitatively defined structural severity and ECG findings(consistent with our findings), there was no significantcorrelation for SAECG or syncope (in contrast to ourfindings) from the qualitative shape descriptors.

Measurement of single versus bi ventricular shapesThe shape modes represent the most commonly occur-ring shape features in the population and can be used tostudy abnormalities at a population-level. In this study,both single-ventricle models and a combined bi-ventricular model were built from CMR images. Thesingle-ventricle models treat the shape abnormalities foronly a given ventricle and neglect the ventricular inter-action. Interestingly, despite the fact that there were noidentifiable shape abnormalities visible in the computedLV shapes (both when computing the LV shapes separ-ately, and when computing the combined bi-ventricleshapes), the LV shapes were correlated to clinical indices.Furthermore, differences between the endocardium andepicardium were found. Due to the size of the popula-tion in this study, few patients had severe ARVC withLV involvement, therefore making it difficult to identifypopulation-based LV features. In a larger study these fea-tures may become more apparent and may lead to LVshapes that are more qualitatively informative. Since LVinvolvement is an indication of severe ARVC it is notsurprising that LV shapes could be related to adverseoutcomes, though it is difficult to conclude this basedon the present study, and furthermore, it is difficult toconclude why the LV epicardium shapes were more cor-related than the LV endocardium shapes.Meanwhile, the bi-ventricular model can account

for the inherent coupling between the ventricles. Theadvantage of the single-ventricle models is that dis-covered shape features are easier to interpret, and the cor-relations between each ventricle and the clinical diagnosticindices can be analysed independently. The bi-ventricularmodel, on the other hand, is able to maintain the ventricu-lar interaction, but at the expense of more complicatedshape features being calculated. Statistical analysis was


performed by both analysing the correlation between indi-vidual shapes, and by studying the relationship betweenclinical indices and the combination of the most dominantshapes. Studying the shapes individually is important toidentify how each shape individually relates to clinical fea-tures. Studying the combination of shapes is important, aseach patient is a combination of shapes that may have ahigh degree of interaction. Therefore, by studying the rela-tionship between the combination of shapes and clinical in-dices we can understand how the total shape (combinationof shape modes) is related to clinical features, and how eachshape individually contributes to that relationship (as com-puted using CCA). Both approaches hold clinical merit forpatient risk stratification.The global bi-ventricle distance classification yielded

high sensitivity (i.e. 85 % of ARVC patients were cor-rectly identified as ARVC), but low specificity (i.e.62 % of control subjects were correctly classified).The classification from the bi-ventricle shape loadingsyielded higher accuracy, with high sensitivity (96 %)and moderate specificity (81 %). For ARVC diagnosis,high sensitivity is more important than high specifi-city due to the potentially life-threatening outcomesof this disease. The classification accuracy from theshape loadings was found to be very promising giventhat shape information alone was used to classify thesubjects. Thus the global measure used in conjunctionwith regional shape analysis could have potential usefor clinical secondary diagnostic support.

Clinical implicationsThe clinical implications of a shape-based imagingapproach in ARVC patients needs to be further ex-plored. Our clinical investigation was considered as afeasibility study, showing the potential of this methodfor ARVC diagnosing, monitoring of disease progres-sion and risk stratification for life-threatening events.In future studies, shape loadings could be computedfor new subjects by computing the deformation fromthe subject to the mean and projecting the deform-ation for each shape, which could thus quantify shapeabnormalities in new patients. In addition, future pro-spective studies should assess in a larger population ifthis type of analysis can be used to predict and risk-stratify patients based on 3D descriptors of ventricu-lar structure, directly following clinical manifestationsover time. Furthermore, the current study relied onimage segmentation tools that are not currently fullyautomated. Since the analysis of the quality of theimage segmentation was not the focus of this study, asingle observer performed all image processing tasksfor consistency, but no test/retest variability for theimage segmentation was performed. All other aspectsof the proposed methods are unbiased, and when

automatic tools for segmenting the structure fromCMR become available, the proposed measures willbe fully unbiased.

LimitationsStructural analysis in this work relied on extracting the3D structures of interest from CMR, which is a challen-ging task. In this study we used a combination of manualand automated approaches. Manual delineation of aregion of interest can be more straightforward thanautomatic methods, however, manual methods can betime consuming and subject to user-bias. Severalmethods for automatically or semi-automatically extract-ing (segmenting) the ventricles from cardiac images havebeen proposed (see [20] for a review of recent methods).The current analysis used such techniques to create 3Dmodels that extended up to the base and were cut flat atthe slice below the first valve insertion visible in eitherthe LV or RV. Including the valves would provide furtherdetails for the structure, particularly at the RVOT. Thislimitation is due to the challenge of extracting 3Dmodels that extend as far as the valves. Methods for thisare currently being developed, and when these becomeavailable, analysis of the full ventricle will be performed.Computing the shape modes in a population can be

performed either with a non-supervised method or witha supervised method. In terms of 3D shape, there can beseveral (even an infinite number) modes, in contrast tothe mode of a set of numbers, which has a single uniquevalue. The method used in this work was PCA, which isa popular non-supervised projection method and is con-venient for extracting the most dominating features in adataset [8]. Supervised methods may provide moreinsight into which shape features are related to specificindices, and can be performed in future work knowingwhich indices are relevant to study, as determined fromthe present analysis.The control group used in this study came from an

ageing population to establish the difference (if oneexisted) between age-related remodelling and disease-related remodelling. However, it would also be of inter-est to compare an age-matched healthy control group tothe ARVC group to distinguish and compare healthyand ARVC-related shape features. Additionally, the con-trol subjects used in this work were obtained from adataset of patients with suspected ischemic cardiac dis-ease and are hence not necessarily healthy control sub-jects. Thus, some of the control subjects may haveabnormal shape features related to non-cardiac condi-tions. If this is the case, the results in this work suggestthat the proposed methods are able to distinguish re-modelling due to ageing and other conditions fromARVC remodelling.


ConclusionsThis study showed for the first time a novel quantitativeapproach for assessing ARVC disease beyond state-of-the-art measures by quantifying ventricular shape abnor-malities in ARVC patients using computational methods.This work demonstrated the ability to use advancedshape analysis tools to extract shape features from CMR.We summarized the 5 most frequent ventricular shapesin ARVC patients and related these shapes to clinicalmanifestations. Our findings indicate the potential ofusing 3D shape analysis as support for future clinicaldecision-making by providing additional indicators ofdisease staging and arrhythmia risk.

Additional file

Additional file 1: File containing the supplementary material.(PDF 550 kb)

Abbreviations3D: Three-dimensional; 4D: Four-dimensional (3D + time);ARVC: Arrhythmogenic right ventricular cardiomyopathy/dysplasia;CCA: Canonical correlation analysis; CMR: Cardiovascular magnetic resonance;ECG: Electrocardiography; ED: End-diastolic phase; ES: End-systolic phase;ICD: Implantable cardioverter-defibrillator; LV: Left ventricle; PCA: PrincipalComponent analysis; ROC: Receiver operating characteristic; RV: Rightventricle; SAECG: Signal-averaged electrocardiography; TFC: Task force criteria

AcknowledgementsThis project was carried out as a part of the Centre for CardiologicalInnovation (CCI), Norway, funded by the Research Council of Norway.The authors would also like to thank Margareth Ribe for her help collectingthe data and gathering the clinical indices, Eric Davenne for his workcontributing to the slice misalignment correction, and Tommaso Mansi forproviding the CCA tools. The authors would like to thank the reviewers forthe useful suggestions, which we believe have significantly improved thereadability of the manuscript.

FundingKM, SW, ISL, JS and KH are members of the Centre for CardiologicalInnovation (CCI). KM and SW are funded by Simula Research Laboratory andthe Research Council of Norway. ISL is funded by the Norwegian HealthAssociation. JS and KH are funded by the South-Eastern Norway RegionalHealthy Authority.

Availability of data and materialsNot applicable.

Authors’ contributionsKM carried out the computational experiments and preparation of the data.JS, ISL and KH collected the patient data, carried out the diagnosis andgathered the diagnostic indices for each patient. KM carried out thestatistical analysis of the data. KM and SW, ISL and KH participated in thepreparation of the manuscript. All authors contributed to the projectconception and read and approved the manuscript.

Competing interestsThe authors declare that they have no competing interests.

Consent for publicationWritten informed consent was obtained from the patient for publication ofimages in this manuscript. The consent form is held by Oslo UniversityHospital, Rikshospitalet and is available for review by the Editor-in-Chief.

Ethics approval and consent to participateThe study complied with the Declaration of Helsinki and was approved bythe Regional Committees for Medical Research Ethics.

Author details1Cardiac Modelling Department, Simula Research Laboratory, PO Box 134,Oslo, Norway. 2Department of Cardiology and Institute for Surgical Research,Oslo University Hospital, Rikshospitalet, Oslo, Norway. 3University of Oslo,Oslo, Norway. 4Center for Cardiological Innovation, Oslo, Norway.

Received: 13 May 2016 Accepted: 4 October 2016

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