Cardiovascular magnetic resonance reference ranges for cardiac function and structure and recommendations for grading severity: the Healthy Hearts Consortium

Cardiovascular magnetic resonance reference ranges for cardiac function and structure and recommendations for grading severity: the Healthy Hearts Consortium | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Cardiovascular magnetic resonance reference ranges for cardiac function and structure and recommendations for grading severity: the Healthy Hearts Consortium View ORCID Profile Liliana E. Szabo , Celeste McCracken , Dorina-Gabriela Condurache , Robin Bülow , Giovanni Donato Aquaro , Florian Andre , Le Thu-Thao , Dominika Suchá , Ahmed M. Salih , Roman Roy , Janek Salatzki , Nay Aung , Sucharitha Chadalavada , Aaron Mark Lee , Nicholas C. Harvey , Tim Leiner , Calvin W.L. Chin , Matthias G. Friedrich , Andrea Barison , Marcus Dörr , Zahra Raisi-Estabragh , Steffen E. Petersen doi: https://doi.org/10.1101/2025.04.07.25325351 Liliana E. Szabo a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK c Semmelweis University, Heart and Vascular Center , Budapest Hungary MBBS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Liliana E. Szabo Celeste McCracken d Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust , Oxford, OX3 9DU, UK MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dorina-Gabriela Condurache a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robin Bülow e Institute for Diagnostic Radiology and Neuroradiology, University Medicine, Ernst Moritz Arndt University Greifswald , Ferdinand-Sauerbruch-Straße 1, 17475 Greifswald, Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giovanni Donato Aquaro f Academic Radiology, Department of Surgical, Medical and Molecular pathology and of critical area, University of Pisa , Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Florian Andre g Department of Cardiology, Angiology and Pneumology, University of Heidelberg , Im Neuenheimer Feld 410, Heidelberg, 69120, Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Le Thu-Thao h National Heart Centre Singapore, 5 Hospital Drive , Singapore 169609, Singapore i Cardiovascular ACP, Duke-NUS Medical School , Singapore PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dominika Suchá j University Medical Center Utrecht, Department of Radiology and Nuclear Medicine , Utrecht, the Netherlands MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ahmed M. Salih a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK k PRIME Lab, Scientific Research Center, University of Zakho , Zakho, Kurdistan Region, Iraq PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roman Roy a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janek Salatzki a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK g Department of Cardiology, Angiology and Pneumology, University of Heidelberg , Im Neuenheimer Feld 410, Heidelberg, 69120, Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nay Aung a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBBS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sucharitha Chadalavada a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aaron Mark Lee a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK MBBS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicholas C. Harvey l MRC Lifecourse Epidemiology Centre, University of Southampton , Southampton, SO16 6YD, UK m NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust , Southampton, SO16 6YD, UK MB BChir, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tim Leiner j University Medical Center Utrecht, Department of Radiology and Nuclear Medicine , Utrecht, the Netherlands n Mayo Clinic, Department of Radiology , Rochester, Minnesota, USA MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Calvin W.L. Chin h National Heart Centre Singapore, 5 Hospital Drive , Singapore 169609, Singapore i Cardiovascular ACP, Duke-NUS Medical School , Singapore MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthias G. Friedrich g Department of Cardiology, Angiology and Pneumology, University of Heidelberg , Im Neuenheimer Feld 410, Heidelberg, 69120, Germany o Department of Medicine and Diagnostic Radiology, McGill University , Montreal, Quebec, Canada MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Barison p Cardiology and Cardiovascular Medicine, Fondazione Toscana Gabriele Monasterio , Pisa, Italy MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marcus Dörr q Department of Internal Medicine B, Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, University Medicine Greifswald , Ferdinand-Sauerbruch-Straße 1,17475 Greifswald, Germany r DZHK (German Centre for Cardiovascular Research), partner site Greifswald , Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zahra Raisi-Estabragh a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBChB, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steffen E. Petersen a William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London , Charterhouse Square, London, EC1M 6BQ, UK b Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust , West Smithfield, London, EC1A 7BE, UK MBBS, DPhil Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: s.e.petersen{at}qmul.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Introduction Cardiovascular magnetic resonance (CMR) imaging offers precise quantification of cardiac structure and function. However, its clinical utility is often limited by the absence of robust, standardized reference ranges and severity grading thresholds. Aims The aim of this study was to establish age-, sex-, and ethnicity-specific reference ranges and severity grading criteria for CMR-derived ventricular and atrial parameters in healthy adults, accounting for variations between two post-processing software tools. Methods and results We analyzed CMR scans from the Healthy Hearts Consortium (HHC), which includes six multi-ethnic international cohorts. Images were automatically segmented using cvi42 (Circle Cardiovascular Imaging) and suiteHEART (Neosoft), with visual and statistical quality control. Ventricular and atrial volumes, myocardial mass, and ejection fractions were derived using short- and long-axis protocols; parameters were indexed to body surface area and height. We defined reference ranges as normal up to the 95% of the prediction interval (PI), and abnormalities as mild up to 99.73%, moderate at 99.73%, and severe at 99.99%, respectively. The final dataset included 4,624 women (51.0%) and 4,435 men (49.0%), with a mean age of 61 ± 13 years (range 18–83), and a multi-ethnic population (81.6% White, 5.6% South Asian, 5.3% Mixed/Other, 3.8% Black, 3.7% Chinese). Minor systematic differences were observed between cvi42 and suiteHEART, particularly in atrial parameters. Conclusions Our work provides an evidence-based framework for CMR severity grading, offering age-, sex-, and ethnicity-stratified thresholds for mild, moderate, and severe deviations from the reference. These reference values support improved diagnostic accuracy, better risk stratification, and enhanced comparability of CMR findings worldwide. Download figure Open in new tab Graphical abstract Footnote: This graphical abstract summarises the methodology and findings of our study on severity grading using cardiovascular magnetic resonance (CMR). It illustrates the dataset, quality control steps, software tools used and the derivation of population-specific reference ranges and severity grading classification. All reference ranges are available on the Healthy Hearts Consortium website ( www.healthy-hearts.org.uk ). Abbreviations: CMR: cardiovascular magnetic resonance; QC: quality control; EDV: end-diastolic volume; ESV: end-systolic volume; SV: stroke volume; EF: ejection fraction; LV: left ventricle. Introduction Cardiovascular magnetic resonance (CMR) imaging is the gold standard method for deriving quantitative measures of cardiac chamber size, function and tissue composition, helping clinicians to distinguish between pathological and healthy conditions 1 , 2 . Its high spatial resolution, multi-parametric tissue characterization and lack of ionizing radiation make it an indispensable tool in clinical cardiology. Despite these advantages, the utility of CMR is significantly limited by the availability of robust, standardized reference ranges for key cardiac parameters. Accurate post-processing of cine CMR images is an important factor in ensuring the reliability of derived cardiac metrics. In recent years, advancements in post-processing software based on deep-learning algorithms have revolutionized CMR analysis by automating processes, minimizing operator dependency and improving measurement consistency 3 . These tools enable precise and reproducible quantification of cardiac parameters, which is especially critical for detecting and interpreting subtle distinctions between physiological and pathological states. In a recent meta-analysis, we demonstrated significant variation between published CMR healthy reference ranges attributable to population and technical factors 4 . To address this shortcoming, we established the Healthy Hearts Consortium (HHC), an international collaborative comprising over 9,000 CMR studies from verified healthy individuals. This dataset is unique in its comprehensive representation of the adult age spectrum and ethnic diversity, making it one of the most robust resources for deriving globally relevant CMR reference ranges 5 . Using this dataset, we subsequently derived age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics, providing a more standardized framework for clinical and research applications. The 2025 update on CMR reference ranges further reinforces the importance of standardized, population-specific reference values, highlighting the broader range of factors that influence CMR-derived parameters 6 . While these updated reference ranges enhance the identification of results of quantitative CMR imaging within and outside the reference range, grading the severity of deviations is essential for clinical decision-making. Severity grading (mild, moderate, severe) provides a more nuanced assessment of cardiac dysfunction, guiding treatment prioritization, disease monitoring and therapeutic approaches 7 . However, despite its clinical importance, methods for severity grading are not standardized, limiting their widespread implementation in routine practice across diverse patient populations. This study builds on our previous work and aims to establish robust severity grading for key CMR-derived cardiac parameters using two widely used CMR post-processing software solutions in a large cohort of verified healthy adults from the HHC. We provide clinicians with a comprehensive framework for interpreting patient-specific metrics while accounting for demographic- and post-processing method-related variability. These efforts represent a critical step towards improving standardization and facilitating its applicability across diverse clinical and research settings. Methods Healthy Hearts Consortium This study utilizes data from the HHC, an international collaborative effort comprising over 9,000 CMR studies from healthy individuals. The consortium, described in detail elsewhere 5 , includes participants from six contributing cohorts: Utrecht (The Netherlands) 8 , Pisa (Italy) 9 , Heidelberg (Germany) 10 , Singapore 11 , the Study of Health in Pomerania (SHIP, Germany) 12 and the UK Biobank 13 . For the UK Biobank healthy subset, the methodology for defining healthy status, mitigating selection bias and ensuring broader ethnic representation was previously detailed 5 . We recalibrated the demographic, socioeconomic, lifestyle, and morbidity profile of this group to reflect that of the general population using data from the Health Survey for England. This resulted in a healthy UK Biobank subset (n = 7,672) free from cardiovascular disease, including a range of ethnicities and with a comparable profile to the general population. The data are managed at the William Harvey Research Institute, Queen Mary University of London, and include anonymized CMR images in DICOM (Digital Imaging and Communications in Medicine) format, demographics, body size measurements and selected technical data (scanner vendor and magnet strength). Further information on the HHC resource is available in Table S1 and on the linked website 14 : https://www.healthy-hearts.org.uk . CMR image segmentation We used two commercially available software solutions with batch processing capabilities: cvi42 (version 5.11.2, Circle Cardiovascular Imaging, Calgary, Alberta, Canada) and suiteHEART (version 5.0.0, Neosoft, Pewaukee, Wisconsin, USA). Both software packages generated comprehensive measurements of left and right ventricular and atrial volumes and function. We employed a fully automated segmentation workflow to ensure reproducibility without manual adjustments. The segmentation algorithms delineated the endocardial and epicardial contours in standard short-axis and long-axis (2-, 3- and 4-chamber) views. Short-axis images covering the left and right ventricles were processed according to predefined protocols endorsed by the Society for Cardiovascular Magnetic Resonance (SCMR) 15 . Three segmentation methods were used to contour the endocardial and epicardial borders of the ventricles using cvi42: 1) Smooth: endocardial contours excluding papillary muscles and trabeculation from the left ventricular (LV) mass (included in the blood pool). 2) Papillary: smooth endocardial contours including papillary muscles (excluding trabecular tissue) in the LV mass. 3) Anatomical: papillary muscles and trabecular tissue are included in the LV mass. Only smooth and anatomical approaches to segmentation are supported by suiteHEART. Atrial segmentation was uniformly performed on long-axis images, and no alternative segmentation modes were available for the atria. Quality control We implemented a comprehensive approach for quality control (QC) to ensure the accuracy and reliability of the analyzed CMR images. This multi-step QC approach included manual visual and statistical QC. First, we conducted manual QC using a previously described scoring system, which evaluates image acquisition, planning, and segmentation quality at the cardiac chamber level in the whole HHC cohort 5 . Each chamber’s image acquisition, planning and segmentation quality was assigned one of three scores: 1 = ‘perfect’, 2 = ‘satisfactory’, and 3 = ‘unacceptable for clinical use’. All metrics derived from chamber views receiving a score of 3 were excluded from further analysis. Following the visual QC, non-sensical values were removed based on objective criteria (detailed in Figure S1 and S2 ). Finally, a statistical QC was applied to remove biologically implausible values. The 3-SD method was used to identify and remove extreme values across the dataset. Calculation of CMR metrics LV and right ventricular (RV) parameters were derived from the full short-axis stack in accordance with the segmentation protocols described earlier. Ventricular volumes were determined using the ‘sum of discs’ method. Atrial parameters were obtained using the biplane method based on long-axis imaging, deriving left atrial (LA) metrics from the 2- and 4-chamber views using the area-length method, while right atrial (RA) metrics were derived from 4-chamber images. For this manuscript, the severity grades reported were determined using suiteHEART and cvi42, which applied both smooth and anatomical segmentation approaches. A comprehensive set of metrics generated by both software solutions is available in the Supplementary material and on the project website for reference ( https://www.healthy-hearts.org.uk ). We report the following clinically relevant metrics: LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV stroke volume (LVSV), LV ejection fraction (LVEF), LV myocardial mass in end-diastole (LVM), RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), RV stroke volume (RVSV), RV ejection fraction (RVEF), LA end-systolic volume (LAESV), LA ejection fraction (LAEF), RA end-systolic volume (RAESV), and RA ejection fraction (RAEF). Additional parameters, including LV and RV cardiac output, the LVM/LVEDV ratio, and maximum volumes for both atria, are provided in the Supplementary material. Body size adjustments To accommodate commonly used body-size adjustments in clinical practice, we present volumetric, and mass measurements indexed to both body surface area (BSA) and height, consistent with previous work 5 . Participant height and weight, measured at the time of imaging, were used to calculate BSA using the Mosteller formula (BSA = √[height (cm) × weight (kg) / 3600]). The main manuscript presents BSA-indexed values, while reference ranges indexed to both BSA and height are provided in the Supplemental material and on our project website. Stratification data Each HHC contributor provided details on the CMR scanner vendor and magnet strength (1.5 Tesla or 3.0 Tesla). Participant age was recorded during imaging, whereas sex and ethnicity were self-reported. The cohorts from Utrecht, Heidelberg, and SHIP comprised individuals from White/Caucasian ethnic backgrounds. The Pisa cohort included one individual of Black ethnicity, and the remainder was of White/Caucasian ethnic backgrounds. The Singapore cohort contained only individuals of “Chinese” descent. Participants from the UK Biobank had the following ethnic categories: “White,” “Black or Black British,” “Asian or Asian British,” “Chinese,” “Mixed,” and “Other”. According to UK government guidance, “Asian or Asian British” encompasses Indian, Pakistani, Bangladeshi, or “any other Asian” backgrounds; for simplicity, we label this group “South Asian” 16 . Statistical analysis Statistical analysis was conducted using RStudio version 4.1.3 17 . Descriptive statistics, including mean, standard deviation (SD), and range, were used to summarize the dataset. Age was categorized into the following groups: 18-29, 30-39, 40-49, 50-59, 60-69, and over 70 years (ages inclusive). To examine associations between ethnicity and CMR metrics, we fitted a series of linear regression models for each variable. This exemplary analysis was restricted to results derived using smooth segmentation and adjusted for body surface area (BSA) for simplicity; however, other segmentation types are expected to yield comparable results. The outcome variable was standardized (z-score) within the full dataset to enable interpretation of effect sizes across metrics. Each model included ethnicity (categorical, with White individuals as the reference group), age (standardized), and sex as covariates. Confidence intervals were calculated at the 95% level, and p-values were adjusted for multiple comparisons across all models using the Benjamini-Hochberg false discovery rate (FDR) method. Statistical significance was defined as an FDR-adjusted p -value < 0.05. Reference ranges are reported as the upper and lower bound of the 95% prediction interval (PI), which provides an estimate of the likely range for a new observation based on the specified parameters. We defined mild deviation from the reference range as values lying between the 95% and 99.73% PI (corresponding to an interval between 2 and 3 SDs), moderate deviation from the reference range as values between the 99.73% and 99.99% intervals (3-4 SDs), and severe deviation from the reference range as values exceeding the 99.99% range (>4 SDs). Grading categories of “Reference Range,” “Mild,” “Moderate,” and “Severe” capture the progression of deviation from the reference range in each metric. In the main manuscript, we present reference ranges and severity grading derived from participants of White ethnicity, using smooth segmentation and BSA-adjustment. This subgroup was selected due to its large sample size, as well as the frequent clinical use of smooth segmentation and BSA adjustment. Results for all other ethnicities and segmentation types are provided in the Supplementary Material. Results Quality control Our robust three-tiered quality control mechanism was designed to ensure the reliability and validity of results across six datasets. Figure S1 and S2 illustrate QC results for cvi42 and suiteHEART, respectively, indicating the number of excluded cases at each stage. The datasets were derived from the UK Biobank healthy subset (n=7,672), the SHIP study healthy subset (n=695), Heidelberg, Germany (n=192), Utrecht, the Netherlands (n=133), Pisa, Italy (n=354), and Singapore (n=180). The initial quality control stage involved identifying and removing incomplete or ineligible cases. Visual quality control was a critical step in refining the data by addressing inconsistencies or errors detectable through expert review. Most values were removed from LA (cvi42 n=1502; suiteHEART n=1466) and RA (n=706; n=2301) metrics, primarily due to foreshortening. The statistical QC step resulted in the removal of fewer than 1% of cases. Population characteristics After applying stringent quality control, the final cohort consisted of 9,059 CMR scans, encompassing 4,624 women (51.0%) and 4,435 men (49.0%) with a mean age of 61 ± 13 years (range 18–83 years). Most patients were of White ethnic background (n=7,396; 81.6%), with smaller representations from South Asian (n=509; 5.6%), mixed/other (n=478; 5.3%), Black (n=341; 3.8%), and Chinese (n=335; 3.7%) populations ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Baseline characteristics Footnote: Values are n(%). SHIP: Study of Health in Pomerania; UKB: UK Biobank; T: Tesla. Comparison of software solutions For the anatomical and smooth segmentation, cvi42 and suiteHEART produced comparable results across volumetric parameters, including LVEDV, LVESV and LVSV ( Figures S3 and S4 ). On average, suiteHEART demonstrated slightly higher mean values across most ventricular parameters compared to cvi42 using both anatomical and smooth segmentation. For derived parameters (LVEF and RVEF), differences were minimal, suggesting a high degree of consistency between the software tools for functional assessments. In the atrial measurements, suiteHEART generally produced slightly lower mean values than cvi42 ( Figures S5 ). Trends with age, sex, ethnicity Left and right ventricular volumes were lower in older participants across all ethnicities and in both sexes ( Figure 1 ), while ejection fraction (LVEF, RVEF) tended to be slightly higher with age. This pattern was consistent in both men and women. Download figure Open in new tab Figure 1: Trends of CMR metrics stratified by age and ethnicity Footnote: Points are fitted age estimates from linear regression models relating age to CMR metric, where each metric, sex and ethnic group was modelled separately. In linear regression models adjusted for age and sex, Chinese and Asian participants had consistently lower LVMi compared to White participants, with standardized β estimates of – 0.11 (95% CI: –0.14, –0.08; p <0.001) and –0.20 (–0.23, –0.17; p <0.001), respectively ( Figure 2 , Supplementary Table 2 ). In contrast, Black individuals had significantly higher LVMi (β = 0.08 [0.05, 0.12]; p <0.001) and LVM/LVEDV ratio (β = 0.14 [0.10, 0.18]; p <0.001), indicating more concentric remodelling. Chinese and Asian individuals also had significantly lower indexed biventricular volumes (e.g., LVEDVi: –0.19 and –0.32; both p <0.001; RVEDVi: –0.17 and –0.35; both p <0.001) and smaller atrial volumes (e.g., LAESVi and RAESVi). Mixed/Other individuals showed generally modest or nonsignificant differences compared to the White ethnicity group. Download figure Open in new tab Figure 2: Association between ethnicity and CMR metrics Footnote: Standardized β coefficients (with 95% confidence intervals) from linear regression models are shown for each ethnic group, using White participants as the reference using the smooth contour approach. Each model was adjusted for age and sex. P-values were corrected for multiple comparisons using the false discovery rate method. Severity ranges Our analyses yielded a comprehensive set of reference ranges and severity grading for LV and RV volumes, myocardial masses, and function, as well as atrial volumes and function, stratified by age groups, sex, ethnicity, software methods, segmentation type, and indexing approach Our analyses yielded a comprehensive set of reference ranges and severity gradings for left and right ventricular volumes, myocardial mass, and functional parameters, as well as atrial volumes and function. These were stratified by age group, sex, ethnicity, software type, segmentation method, and indexing approach ( Figures 3 , 4 , 5 , 6 ). Download figure Open in new tab Figure 3: LV and RV reference ranges and severity grading for White ethnicity by age group, with papillary muscles and trabeculae excluded from the LV Mass (smooth segmentation), indexed by BSA area using suiteHEART Footnote: BSA: Body surface area; LV: left ventricle; LVEF: LV ejection fraction; LVEDVi: LV end-diastolic volume indexed by BSA; LVESVi: LV end-systolic volume indexed by BSA; LVM: LV mass; LVMi: LV mass indexed by BSA; LVSVi: LV stroke volume indexed by BSA; RV: right ventricle; RVEF: RV ejection fraction; RVEDVi; RV end-diastolic volume indexed by BSA; RVESVi: RV end-systolic volume indexed by BSA; RVSVi: RV stroke volume indexed by BSA. Download figure Open in new tab Figure 4. LV and RV reference ranges and severity grading for white ethnicity by age group, with papillary muscles and trabeculae excluded from the LV mass (smooth segmentation), indexed by BSA using cvi42 Footnote: BSA: Body surface area; LV: left ventricle; LVEF: LV ejection fraction; LVEDVi: LV end-diastolic volume indexed by BSA; LVESVi: LV end-systolic volume indexed by BSA; LVM: LV mass; LVMi: LV mass indexed by BSA; LVSVi: LV stroke volume indexed by BSA; RV: right ventricle; RVEF: RV ejection fraction; RVEDVi; RV end-diastolic volume indexed by BSA; RVESVi: RV end-systolic volume indexed by BSA; RVSVi: RV stroke volume indexed by BSA. Download figure Open in new tab Figure 5. Atria: LA and RA reference ranges and severity grading for White ethnicity, indexed by BSA using suiteHEART Footnote: BSA; body surface area; LA: left atrium; LAEF: LA ejection fraction; LAESVi: LA end-systolic volume indexed by BSA; RA: right atrium; RAEF: RA ejection fraction; RAESVi: RA end-systolic volume indexed by BSA. Download figure Open in new tab Figure 6. Atria: LA and RA reference ranges and severity grading for White ethnicity, indexed by BSA using cvi42 Footnote: BSA; body surface area; LA: left atrium; LAEF: LA ejection fraction; LAESVi: LA end-systolic volume indexed by BSA; RA: right atrium; RAEF: RA ejection fraction; RAESVi: RA end-systolic volume indexed by BSA. Figures S5 and S6 provide age- and ethnicity-stratified severity grading visualizations for selected ventricular and atrial metrics. The green bands indicate the 95% reference range, while yellow, orange, and red zones denote mild, moderate, and severe deviations, respectively. These plots illustrate the age-dependent trajectory of key cardiac parameters within and beyond normative bounds across the studied populations. Full reference ranges and severity grading across all subgroup combinations—including segmentation method, software platform, body size indexation approach, and all ethnic groups—are provided in Supplementary Tables S3–S103 . Discussion In this multi-cohort analysis, we present the largest and most comprehensive study establishing robust reference ranges and severity grading thresholds for CMR-derived ventricular and atrial parameters in healthy adults. By analyzing a large, ethnically diverse population, we define population-specific reference values, ensuring greater accuracy in assessing cardiac structure and function across different demographic groups. Additionally, we introduce severity grading thresholds (mild, moderate, and severe) to standardize the classification of cardiac chamber deviations from the reference range. To ensure broad applicability and reproducibility, we employed two widely used post-processing software solutions, cvi42 and suiteHEART, generating findings applicable across diverse clinical and research settings. The 2019 EACVI expert consensus paper was the first to propose a severity grading framework for CMR-derived cardiac parameters 7 . While this was a crucial step toward standardization, it was based primarily on expert consensus and its reference datasets were limited in size and lacked ethnic diversity. By deriving statistically defined severity grading thresholds from a large, multi-ethnic cohort, our study addresses these limitations and provides an evidence-based framework for classifying cardiac abnormalities. Our findings align with prior evidence that ethnicity influences cardiac morphology, particularly in ventricular mass 4 . Black individuals exhibited the highest LVMi, while White individuals had lower LVMi and showed a more pronounced decline in older ages. These findings suggest that ethnicity-specific reference ranges may be necessary to avoid misclassification of ventricular hypertrophy and related conditions in clinical practice. Ultimately, by incorporating age-, sex-, and ethnicity-stratified reference values, our study ensures that physiological variations are appropriately accounted for, minimizing the risk of misclassification and diagnostic errors across diverse populations. In this study, we used artificial intelligence (AI)-based segmentation tools that are increasingly integrated into clinical CMR workflows. While automated algorithms improve the consistency of contouring and accelerate post-processing, human oversight remains essential to verify the accuracy of segmentation and identify potential errors that may not be readily detected by AI 18 . To ensure high data integrity, we implemented a rigorous QC pipeline, combining statistical outlier detection with expert visual inspection. This dual approach was particularly critical in synthesizing data from multiple cohorts with varying imaging sequences, acquisition protocols and scanner models. While some evidence suggests that statistical QC alone may suffice in highly standardized large-scale epidemiological studies 19 , the heterogeneity of acquisition methods and the clinical importance of establishing robust reference ranges necessitate additional expert oversight. By integrating AI segmentation with stringent QC procedures, we ensured that our severity grading thresholds and reference ranges were both clinically meaningful and technically robust, providing a reliable benchmark for identifying and categorizing cardiac pathology in clinical practice and research. Here, our choice of reporting prediction intervals over standard summary statistics in defining reference ranges ensures a probabilistic understanding of future observations 20 , rather than describing past data variability as standard meta-analysis techniques do. Zhan et al. 21 recently demonstrated a similar advantage by employing a Bayesian hierarchical meta-analysis, which models heterogeneity and reports credible intervals as a Bayesian counterpart to PI. Their work used values reported in previous studies, whereas our core centre reanalysis directly assessed the measurements, ensuring consistency and control over methodological variations. Finally, this study contributes to the ongoing effort to enhance the quality and standardization of CMR services by establishing robust, population-specific reference ranges and severity grading 6 , 7 , 15 , 22 . While these reference ranges provide a valuable benchmark, they should be adapted to local clinical practices and interpreted within the broader context of individual patient characteristics. Ensuring the accuracy and consistency of CMR interpretation requires the combination of high-quality imaging, robust post-processing, and a structured framework of reporting 15 , 23 . Certification or formal competency evaluations for clinicians interpreting CMR—whether cardiologists or radiologists—play a crucial role in maintaining standardized expertise and ensuring the correct application of reference values in clinical decision-making. Limitations Several limitations should be acknowledged. First, the severity grading thresholds established in this study are derived using two specific software packages (cvi42, suiteHEART). The provided severity grading thresholds may not apply to images analyzed using different image analysis software. For volumetry calculations, geographical assumptions were applied. Furthermore, while the HHC dataset includes greater ethnic diversity, over 80% of the CMR studies were performed in White individuals, limiting generalizability to underrepresented ethnic groups. Furthermore, while the severity grading thresholds were derived based on expert consensus, the selected cut-off values require further validation in the clinical context. Additionally, it is important to acknowledge that our reference ranges were derived from a population that did not include competitive or highly active endurance athletes. Exercise-induced cardiac remodeling can significantly influence chamber sizes and functional parameters 24 , and our reference values may not be applicable to individuals engaging in regular intensive physical activity. Future work should consider the inclusion of athlete-specific reference ranges to ensure appropriate interpretation in this subgroup. Conclusions This study establishes statistically robust reference ranges and severity grading thresholds for CMR-derived ventricular and atrial parameters in healthy adults. By incorporating two widely used post-processing software solutions, we provide a standardized, population-specific CMR interpretation framework, ensuring greater reproducibility and clinical applicability. Data Availability All data produced in the present work are contained in the manuscript. Additional information Ethics statement This study complies with the Declaration of Helsinki. Analysis of the UK Biobank was covered by ethical approval from the NHS National Research Ethics Service on 17th June 2011(Ref 11/NW/0382) and extended on 18th June 2021(Ref 21/NW/0157) with written informed consent obtained from all participants. For all other cohorts, appropriate ethical approval was verified by each contributing site. Conflict of interest SEP provides Consultancy to Circle Cardiovascular Imaging, Inc., Calgary, Alberta, Canada. MGF is an advisor to and shareholder of Circle Cardiovascular Imaging, Inc., Calgary, Alberta, Canada. Neosoft provided temporary free batch processing licences and financial support to analyze the HHC using their software solution. Funding support LS was supported by the Barts Charity (G-002389). LS (EKÖP-2024-272) acknowledges support from the New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. ZRE recognizes the National Institute for Health and Care Research (NIHR) Integrated Academic Training programme, which supports her Academic Clinical Lectureship post. CM is supported by the Oxford NIHR Biomedical Research Centre (IS-BRC-1215-20008). D-GC was supported by a Barts Charity Clinical Research Training Fellowship (G-002777) and the Healthy Hearts Consortium to Impact (HHC-Impact, AC1B1A3R). AS was supported by the British Heart Foundation (PG/21/10619). RR recognizes the NIHR Integrated Academic Training programme, which supports his Academic Clinical Fellowship post (ACF-2024-19-008). This work acknowledges the support of the NIHR Barts Biomedical Research Centre (NIHR203330); a delivery partnership of Barts Health National Health Service (NHS) Trust, Queen Mary University of London, St George’s University Hospitals NHS Foundation Trust, and St George’s University of London. Barts Charity (G-002346) contributed to fees required to access UK Biobank data (access application 2964). TL acknowledges support from the Netherlands Organization for Health Research and Development (grant number 90700432). NCH is supported by the UK Medical Research Council (MC_PC_21003; MC_PC_21001); NIHR Southampton Biomedical Research Centre, University of Southampton; and University Hospital Southampton NHS Foundation Trust, United Kingdom. This paper is supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded by the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). References ↵ Leiner T , Bogaert J , Friedrich MG , Mohiaddin R , Muthurangu V , Myerson S et al. SCMR Position Paper (2020) on clinical indications for cardiovascular magnetic resonance . BioMed Central , 2020 doi: 10.1186/s12968-020-00682-4 . OpenUrl CrossRef ↵ Kawel-Boehm N , Hetzel SJ , Ambale-Venkatesh B , Captur G , Francois CJ , Jerosch-Herold M et al. Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update . BioMed Central , 2020 doi: 10.1186/s12968-020-00683-3 . OpenUrl CrossRef PubMed ↵ Siegersma KR , Leiner T , Chew DP , Appelman Y , Hofstra L , Verjans JW . Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist . Netherlands Heart Journal 2019 ; 27 : 403 – 413 . OpenUrl PubMed ↵ Raisi-Estabragh Z , Kenawy AAM , Aung N , Cooper J , Munroe PB , Harvey NC et al. Variation in left ventricular cardiac magnetic resonance normal reference ranges: systematic review and meta-analysis . Eur Heart J Cardiovasc Imaging 2020 ; : 1 – 11 . ↵ Raisi-Estabragh Z , Szabo L , McCracken C , Bülow R , Aquaro GD , Andre F et al. Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium . JACC Cardiovasc Imaging 2024 ; 17 : 746 – 762 . OpenUrl PubMed ↵ Kawel-Boehm N , Hetzel SJ , Ambale-Venkatesh B , Captur G , Chin CW , François CJ et al. Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2025 update . Journal of Cardiovascular Magnetic Resonance 2025 ; : 101853 . ↵ Petersen SE , Khanji MY , Plein S , Lancellotti P , Bucciarelli-Ducci C . European Association of Cardiovascular Imaging expert consensus paper: A comprehensive review of cardiovascular magnetic resonance normal values of cardiac chamber size and aortic root in adults and recommendations for grading severity . Eur Heart J Cardiovasc Imaging 2019 ; 20 : 1321 – 1331 . OpenUrl PubMed ↵ Eikendal ALM , Bots ML , Haaring C , Saam T , Van Der Geest RJ , Westenberg JJM et al. Reference values for cardiac and aortic magnetic resonance imaging in healthy, young caucasian adults . PLoS One 2016 ; 11 : 1 – 17 . OpenUrl CrossRef PubMed ↵ Aquaro GD , Camastra G , Monti L , Lombardi M , Pepe A , Castelletti S et al. Reference values of cardiac volumes, dimensions, and new functional parameters by MR: A multicenter, multivendor study . J Magn Reson Imaging 2017 ; 45 : 1055 – 1067 . OpenUrl PubMed ↵ Riffel JH , Schmucker K , Andre F , Ochs M , Hirschberg K , Schaub E et al. Cardiovascular magnetic resonance of cardiac morphology and function: impact of different strategies of contour drawing and indexing . Clinical Research in Cardiology 2019 ; 108 : 411 – 429 . OpenUrl PubMed ↵ Le TT , Tan RS , De Deyn M , Goh EPC , Han Y , Leong BR et al. Cardiovascular magnetic resonance reference ranges for the heart and aorta in Chinese at 3T . Journal of Cardiovascular Magnetic Resonance 2016 ; 18 : 21 . OpenUrl CrossRef PubMed ↵ Bülow R , Ittermann T , Dörr M , Poesch A , Langner S , Völzke H et al. Reference ranges of left ventricular structure and function assessed by contrast-enhanced cardiac MR and changes related to ageing and hypertension in a population-based study . Eur Radiol 2018 ; 28 : 3996 – 4005 . OpenUrl PubMed ↵ Petersen SE , Aung N , Sanghvi MM , Zemrak F , Fung K , Paiva JM et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort . Journal of Cardiovascular Magnetic Resonance 2017 ; 19 : 18 . OpenUrl CrossRef PubMed ↵ Healthy Hearts Consortium . https://www.healthy-hearts.org.uk (January 22 2025). . ↵ Schulz-Menger J , Bluemke DA , Bremerich J , Flamm SD , Fogel MA , Friedrich MG et al. Standardized image interpretation and post-processing in cardiovascular magnetic resonance - 2020 update: Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task Force on Standardized Post-Processing . Journal of Cardiovascular Magnetic Resonance 2020 ; 22 : 1 – 22 . OpenUrl CrossRef PubMed ↵ UK Government . Ethnic groups: detailed information . GOV.UK. https://www.ethnicity-facts-figures.service.gov.uk/style-guide/ethnic-groups/ (13 February 2025). . ↵ R Core Team . R: A language and environment for statistical computing . . ↵ Szabo L , Raisi-Estabragh Z , Salih A , McCracken C , Ruiz Pujadas E , Gkontra P et al. Clinician’s guide to trustworthy and responsible artificial intelligence in cardiovascular imaging . Front Cardiovasc Med 2022 ; 9 . ↵ Chadalavada S , Rauseo E , Salih A , Naderi H , Khanji M , Vargas JD et al. Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study . European Heart Journal - Imaging Methods and Practice 2024 ; 2 . ↵ Spence JR , Contini ME , Stanley DJ . Introducing prediction intervals for sample means . Biochem Med (Zagreb ) 2024 ; 34 : 387 – 396 . OpenUrl ↵ Zhan Y , Friedrich MG , Dendukuri N , Lu Y , Chetrit M , Schiller I et al. Meta-Analysis of Normal Reference Values for Right and Left Ventricular Quantification by Cardiovascular Magnetic Resonance . Circ Cardiovasc Imaging 2024 ; 17 . ↵ Vasan RS , Levy D , Larson MG , Benjamin EJ . Interpretation of echocardiographic measurements: A call for standardization . Am Heart J 2000 ; 139 : 412 – 422 . OpenUrl CrossRef PubMed Web of Science ↵ Westwood M , Almeida AG , Barbato E , Delgado V , Dellegrottaglie S , Fox KF et al. Competency-based cardiac imaging for patient-centred care. A statement of the European Society of Cardiology (ESC) . Eur Heart J 2023 ; 44 : 4771 – 4780 . OpenUrl PubMed ↵ Csecs I , Czimbalmos C , Toth A , Dohy Z , Suhai IF , Szabo L , et al. The impact of sex, age and training on biventricular cardiac adaptation in healthy adult and adolescent athletes: Cardiac magnetic resonance imaging study . Eur J Prev Cardiol 2020 ; 27 ( 5 ): 540 – 549 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted April 08, 2025. 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Szabo , Celeste McCracken , Dorina-Gabriela Condurache , Robin Bülow , Giovanni Donato Aquaro , Florian Andre , Le Thu-Thao , Dominika Suchá , Ahmed M. Salih , Roman Roy , Janek Salatzki , Nay Aung , Sucharitha Chadalavada , Aaron Mark Lee , Nicholas C. Harvey , Tim Leiner , Calvin W.L. Chin , Matthias G. Friedrich , Andrea Barison , Marcus Dörr , Zahra Raisi-Estabragh , Steffen E. Petersen medRxiv 2025.04.07.25325351; doi: https://doi.org/10.1101/2025.04.07.25325351 Share This Article: Copy Citation Tools Cardiovascular magnetic resonance reference ranges for cardiac function and structure and recommendations for grading severity: the Healthy Hearts Consortium Liliana E. Szabo , Celeste McCracken , Dorina-Gabriela Condurache , Robin Bülow , Giovanni Donato Aquaro , Florian Andre , Le Thu-Thao , Dominika Suchá , Ahmed M. Salih , Roman Roy , Janek Salatzki , Nay Aung , Sucharitha Chadalavada , Aaron Mark Lee , Nicholas C. Harvey , Tim Leiner , Calvin W.L. Chin , Matthias G. Friedrich , Andrea Barison , Marcus Dörr , Zahra Raisi-Estabragh , Steffen E. 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