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Automatic Quantification of the Myocardial Extracellular

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Published: 5th Sep 2017 in Health

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Automatic Quantification of the Myocardial Extracellular Volume by Cardiac Computed Tomography: Synthetic ECV by CCT

Thomas A Treibel, MBBS1,2, Marianna Fontana, PhD,1,2, Jennifer A Steeden PhD2,3, Arthur Nasis, MD1, Jason Yeung, MBBS4, Steven K White, BSc, MBChB1,2, Sri Sivarajan4, Shonit Punwani, PhD4, Francesca Pugliese, PhD1, Stuart A Taylor, MD4, James C Moon, MD1,2, Steve Bandula, PhD4

1Barts Heart Centre, St Bartholomew’s Hospital, London, UK.

2Institute of Cardiovascular Science, University College London, London, UK.

3UCL Centre for Medical Image Computing, Department of Medical Physics, London, UK.

4Centre for Medical Imaging, University College London, London, UK.

Manuscript Type: Original Manuscript

Manuscript: 3924 words (all including)


No conflict of interest declared.

Funding: TAT and SB are supported by Doctoral Research Fellowships from the NIHR, UK (NIHR­DRF­ 2013-06-102 / NIHR­DRF­ 2011­04­008). MF and SKW are supported by Clinical Research Training Fellowships from the British Heart Foundation (grants FS/12/ 56/29723 and FS/10/72/28568). JCM is directly and indirectly supported by the University College London Hospitals NIHR Biomedical Research Centre and Biomedical Research Unit at Barts Hospital, respectively. FP: this work form part of the translational portfolio of the Cardiovascular Biomedical Research Unit at Barts, which is supported and funded by the NIHR. SAT is an NIHR senior investigator. This work was undertaken at University College London Hospital, which received a proportion of funding from the UK Department of Health National Institute for Health Research Biomedical Research Centres funding scheme.



The quantification of myocardial extracellular volume fraction (ECV) by Cardiac Computed Tomography (CCT) can identify changes in the extracellular space due to fibrosis or infiltration. Current methodologies require laboratory blood hematocrit (Hct) measurement – which complicates the technique.  The attenuation of blood (HUblood) is known to change with anemia. We hypothesized that the relationship between Hct and HUblood could be calibrated to rapidly generate a synthetic ECV without the need to formally measure Hct.

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Methods: This retrospective study received institutional review board approval. The association between Hct and HUblood was derived from forty non-contrast thoracic CT scans using regression analysis. Synthetic Hct was then used to calculate synthetic ECV, and in turn compared with ECV using blood Hct in a validation cohort with mild interstitial expansion due to fibrosis (aortic stenosis, n=28, ECVCT = 28±4%) and severe interstitial expansion due to amyloidosis (n=27; ECVCT = 54±11%, p<0.001). For histological validation, synthetic ECV was correlated with collagen volume fraction (CVF) in a separate cohort with aortic stenosis (n=18). All CT scans were performed at 120kV and 160 mAs.

Results: HUblood was a good predictor of Hct (R2=0.47; p<0.01), with the regression model (Hct = [0.51 * HUblood] + 17.4) describing the association. Synthetic ECV correlated well with conventional ECV (R2=0.96; p<0.01) with minimal bias and 2SD difference of 5.7%. Synthetic ECV correlated as well as conventional ECV with histological CVF (both R2=0.50, p<0.01). Finally, we implemented an automatic ECV plug-in for offline analysis.

Conclusion: Synthetic ECV by CCT provides instantaneous quantification of the myocardial extracellular space without the need for blood sampling.

KEYWORDS: Computed tomography; Myocardial tissue characterization; Extracellular matrix; Myocardial extracellular volume fraction; Myocardial fibrosis; cardiac amyloidosis.


AL amyloidosis = Immunoglobulin light-chain amyloidosis

AS = Aortic stenosis

CCT = Cardiac computed tomography

CMR = Cardiovascular magnetic resonance

CVF = Collagen volume fraction

ECV = Extracellular volume fraction

HU = Hounsfield units


Extracellular volume fraction (ECV) quantification by cardiac computed tomography (CCT) 1-5 and cardiovascular magnetic resonance (CMR) 6, 7 is a promising new imaging biomarker for interstitial expansion due to myocardial fibrosis and cardiac amyloid deposition. Emerging data suggests ECV predicts outcome as well as left ventricular ejection fraction 8, 9 and there is increasing interest in targeting the interstitium during the development of heart failure therapy.10 Current methodologies for ECV quantification require blood hematocrit (Hct) measurement, which adds a layer of complexity and is potentially a barrier to easy clinical implementation. Alternatively, for CMR, Treibel et al. recently proposed a synthetic ECV technique, removing the need for Hct measurement by utilizing the relationship between relaxivity of blood and lab measured Hct.11 It is unknown if a similar approach can be used for CCT, although a relationship between anemia and unenhanced blood attenuation has been observed.12-17 For example the “aortic ring sign” and “dense intra-ventricular septum” on unenhanced thoracic CTs suggest underlying anemia.17-19 We hypothesized that the relationship between Hct and unenhanced blood attenuation (HUblood) could be used to estimate a synthetic Hct, permitting immediate synthetic ECV calculation without blood sampling. We used existing patient cohorts1, 4 to investigate how synthetic ECV (a) compares to conventional ECV, and (b) correlates with the reference standard collagen volume fraction. We also tested implementation of an automated synthetic ECV measurement plug-in within the open-source DICOM viewer OsiriX.20


This study is a retrospective analysis of prospectively acquired data, received local ethical approval and conformed to the principles of the Helsinki Declaration. The study received no industry support.  All participants provided informed and written consent. Exclusion criteria were uncontrolled arrhythmia or impaired renal function (estimated glomerular filtration rate <45mL/min). Prior to the scan, following insertion of an intravenous cannula, a 2-mL blood sample was collected and sent for complete blood cell count analysis.

ECV CCT Protocols.

The CCT protocol consisted of three steps: first, a low dose non-contrast scan to obtain baseline attenuations; second, contrast administration with a contrast-enhanced 1-minute acquisition and a 5 minute delay to allow blood to myocardial contrast equilibration; third, a repeat scan to re-measure blood and myocardial attenuations.

CCT examinations were performed on a 64-detector row CT scanner (Somatom Sensation 64; Siemens Medical Solutions, Germany).1, 4 A topogram was used to plan CT volumes from the level of the aortic valve to the inferior aspect of the heart, typically a 10 cm slab. Cardiac scans (tube voltage, 120 kV; tube current-time product, 160 mAs; section collimation, 64 detector rows, 1.2-mm section thickness; gantry rotation time, 330 msec) were acquired with prospective gating (65%-75% of R-R interval), and reconstructed into 3-mm-thick axial sections with a B20f kernel. All pre- and post contrast acquisitions were performed and reconstructed with the same parameters and matched the level of the pre-contrast scan.

The iodinated contrast material used was iohexol (Omnipaque 300; Nycomed Amersham, Oslo, Norway; 300 mg of iodine per milliliter) at a standard dose of 1mL/kg and injection rate of 3ml/sec without a saline chaser.

Image Analysis.

CCT image analysis was performed using a free and open-source Digital Imaging and Communications in Medicine viewer (OsiriX v4.1.2; Pixmeo, Bernex, Switzerland) independently by two experienced readers blinded to all other study data. For Hct estimation, regions of interest (ROIs) were placed in in a single axial slice in the center of the right atrium. The mean area of these ROIs were 4.8±1.2cm2. ROIs were drawn in the myocardial left ventricular septum and blood pool in the contrast-enhanced 1-minute acquisition in axial sections and propagated to the pre-contrast and post contrast acquisitions. Myocardial and blood attenuation values (pre-and post contrast only) were used to calculate the ECV fraction from the ratio of the change in blood and myocardial attenuation (ΔHU) corrected by the blood volume of distribution (1 – Hematocrit):

ECV  =  (1 – Hematocrit) x (ΔHUtissue / ΔHUblood)

Synthetic Hematocrit and ECV Methodology

1. Derivation of synthetic Hematocrit

To derive a regression model predicting hematocrit from pre-contrast HUblood, clinical unenhanced CT scans of the thorax were retrospectively analyzed (120 kV; reconstructed at 5mm slice thickness and B70F soft tissue kernel). These were consecutive clinical CT scans of the thorax for investigation of malignancy, fibrosis or infection. Datasets were included if the patients had a contemporaneous paired laboratory measured Hct (within 20 days, median 8 days). HUblood was analyzed in a single axial slice through the center of the right atrium. This was chosen to minimize beam-hardening artifact from the spine (compared to aortic blood pool) and partial voluming of papillary muscles in the left or right ventricular blood pool. Synthetic Hct was obtained from the equation describing the linear regression line between laboratory HUblood and Hct.

2. Creation of a synthetic ECV Equation

Blood hematocrit was substituted by the derived synthetic Hct to derive a synthetic ECV: Synthetic ECV  =  (1 – synthetic Hct) x (ΔHUtissue / ΔHUblood)

3. Validation of synthetic ECV

For validation, we used existing patient cohorts to investigate how synthetic ECV (a) compares to conventional ECV with laboratory blood hematocrit,4 and (b) correlates with the reference standard collagen volume fraction.1

3a. Clinical Validation Cohort

In order to test synthetic ECV across a range of ECV values, the cohort used by our group to validate ECV by CT in amyloidosis was chosen; this comprised of two sub-groups with differing degrees of extracellular volume expansion: I. patients with cardiac amyloidosis (typically high ECV), comprising of 26 patients with systemic amyloidosis (21 males, age 55±10 years; 18 with transthyretin amyloidosis; 8 with systemic AL amyloidosis) with varying degrees of cardiac involvement; II. A comparator group of 27 age- and sex-matched patients with severe aortic stenosis (19 male, age 68±8 years) who typically exhibit only mild ECV elevation. Scans were performed between January and December 2013. In the clinical cohort, contrast administration was performed using a bolus only approach with a 1 mL/kg bolus of iohexol and post-contrast imaging at 1 minute (for segmentation) and 5 minutes (for post contrast analysis), as validated by our group previously.4

3b. Histological Validation Cohort

For histological validation, the performance of synthetic ECV against a histological measure of fibrosis, the collagen volume fraction (CVF), was tested in a second smaller cohort of patients with severe AS, who underwent intra-operative biopsy (no overlap with clinical cohort). This cohort had been used by our group to validate ECV by CT again histology:1 Consenting severe AS patients (n = 17, median age 71±10 years, 76% male) underwent CCT between July 2010 and February 2012. Biopsies were obtained and stained with picrosirius red for histological measurement of collagen volume fraction (CVF) as previously described.21 In the histology cohort, contrast administration followed primed iodinated contrast material infusion (bolus plus maintenance infusion) with a 1 mL/kg bolus of iohexol followed by a maintenance infusion of at a rate of 1.88 mL/kg per hour for 25 minutes, when the post contrast imaging was performed.1

4. OsiriX Plugin

To facilitate offline analysis and to exemplify future inline automation by scanner manufacturers, an automatic synthetic ECV plug-in was developed for OsiriX.

Statistical analysis

Analyses were performed using SPSS (Chicago, IL, USA, version 22). All data are presented as mean ± SD. Normal distribution was assessed by using the Kolmogorov-Smirnov test. Differences were assessed using unpaired, two-sided student t-tests (significance level p<0.05). Agreement between conventional and synthetic ECV was analyzed using the Bland-Altman method. The significance of the difference between two correlation coefficients was tested using the Fisher r-to-z transformation.


Step 1. Derivation cohort

40 thoracic CT scans with contemporaneous Hct samples within 20 days (mean 8±7 days) of the scan were included (n=40, 53% male, age 60±20 years) with a broad range of Hct (mean 38.2±6.0%; range 24.7-50.7%) and HUblood (mean 40±8; range 20-55). The linear regression equation was:  (sHct = [0.51 * HUblood] + 17.4) with R2=0.47 p<0.001 (Figure 1).

Step 2. Creation of the synthetic ECV Equation

Blood hematocrit was substituted by the derived synthetic Hct to derive a synthetic ECV: Synthetic ECV  =  (1 –([0.51 * HUblood] + 17.4)x (ΔHUtissue / ΔHUblood)

Step 3. Validation

Step 3a. Clinical cohort

Baseline characteristics of twenty-six systemic amyloidosis and twenty-seven AS patients are shown in Table 1.In this cohort, Hct were mean 41.4±3.8% (range 29.3-47.4%) and HUblood mean 40.2±3.9 (range 29.3-50.1). Synthetic ECV, calculated using the regression model to derive HCT,and conventional ECV were highly correlated (R2=0.96; p<0.001) with a 5.7% SD of differences and minimal bias (2.4%) on Bland-Altman analysis (Figure 2). ECVCT was significantly higher in amyloid patients with definitive cardiac involvement than aortic stenosis (54±11% versus 28±4%, p<0.001).

Step 3b. Histology cohort

Baseline characteristics of the histology cohort are described in Table 2.The mean histological CVF of the 17 biopsies was 18 ± 8% (range 5% to 40%), Hct were 40.2±4.6% (range 29.4-46.4%) and HUblood 37.7±4.2 (range 29.5-45.1). Synthetic and conventional ECV both correlated well with collagen volume fraction (R2 = 0.50, p < 0.001 vs. R2 = 0.50, p < 0.001; Figure 3) and did not differ statistically on Fisher r-to-z transformation (p = 0.8).

Step 4. Automatic synthetic ECV plug-in in OsiriX

Example output of the OsiriX plugin are shown in Figure 4, and the code is provided in the supplementary data. This plugin involves three simple steps: I. Manual segmentation of the blood pool in the pre- and post-contrast images; II. The plug-in automatically estimates blood hematocrit using the attenuation relationship defined above; III. The plug-in produces a three-dimensional myocardial ECV volume, where each image voxel represents an ECV value.


Inter- and intra-observer agreement was excellent for myocardial (ICC = 0.92 and ICC = 0.94, respectively) and blood pool (ICC = 0.96 and ICC = 0.99, respectively) attenuation measurements. Similarly for ECV, excellent agreement was found (ICC = 0.95 and ICC = 0.98, respectively).

Repeat sampling variability was tested in 44 patients who underwent two samples a median of 4 hours apart. Test:retest variability of laboratory hematocrit was higher than expected (n=44, variability 10% with hct:hct R2=0.86.11


Identifying interstitial heart disease is important for diagnosis and prognosis,10 and myocardial extracellular volume fraction (ECV) can be measured non-invasively by CCT.1-4 However, its measurement is complicated by the necessity for venous blood sampling, image analysis and then offline ECV calculation. This process is cumbersome and a major obstacle for implementing this technique into routine clinical practice. In this manuscript, we simplify the technique by calculating ECV without blood hematocrit. This development arose out of a need to simplify ECV measurement to make it more clinically applicable. We utilize the relationship between hematocrit and blood attenuation (the attenuation of blood decreases with anemia)12-14, 17-19 to derive a synthetic hematocrit for immediate synthetic ECV calculation without blood sampling.

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We show that synthetic ECV was highly correlated to conventional ECV, and had a similar association to the histologic reference standard of CVF. The implementation of an offline automated processing tool provides a significant aid to workflow, allowing for ECV measurement in routine clinical practice.  Automated synthetic ECV can be implemented inline on CT scanners with test performances approaching that of conventional ECV measurement. ECV quantification by CT, despite it lower signal to noise ratio, has key advantage over CMR: The CT approach is cheaper and widely available, can be completed in 5 minutes, and the scanner design can accommodate patients with obesity and claustrophobia (CMR is not suitable in around 10% of patients due to claustrophobia or many cardiac pacemakers).22 Furthermore, ECV by CCT can provide high-resolution 3D ECV volumes with whole heart acquisition and limited cardiac motion. Finally, the concentration of iodine has a linear relationship with the CT attenuation value, which is not affected by fast exchange mechanism like CMR T1 mapping (depending on cell size and contrast dose, fast transcytolemmal water-exchange may reach its limits), which do not apply to CT.23, 24

ECV (by CMR or CT) allows quantification of a key pathophysiological pathway in heart failure: interstitial expansion due to diffuse myocardial fibrosis (or in rare cases by deposition of amyloid fibrils).1-4 As the CMR field is showing, ECV is diagnostic in certain diseases, tracks myocardial remodelling and predicts outcome.25, 26 Interstitial expansion can be global (hypertension, aortic stenosis) or focal (hypertrophic or dilated cardiomyopathy), therefore high spatial resolution and whole heart coverage is important. Due to the aforementioned advantages of CT over CMR, ECV by CT will undoubtedly receive greater attention as part of comprehensive assessment of the heart by CT coronary angiography, perfusion and myocardial tissue characterization.


The study has limitations. In the derivation cohort, the mean interval between Hct samples and CT 8 days. Normal within-subject variation in Hct between 1 day and 1-2 months in a healthy adult is actually very low (3%), but together with an analytical variation (3%) this may explain a relative change of >10% between two successive Hct values.27 The control cohort used in this study comprised of patients with AS rather then healthy volunteers, but, given the exposure to ionizing radiation and contrast, patients with AS were deemed as adequate control cohort, avoiding exposure of healthy volunteers. For the same reasons, variability of repeat synthetic ECV was not tested.

Development and validation were performed using a single scanner platform, therefore this regression model is only valid for 120 kV and an X-ray tube used in a specific CT vendor. Spectrum of the X-rays emitted by a CT X-ray tube substantially varies among CT vendors. In addition, low KV scans are increasingly used to reduce radiation exposure to the patients. Consequently, multiple regression models for different KV settings as well as for different CT vendors should be carefully prepared for synthetic ECV by CCT.

Other factors that may affect the attenuation of blood such as temperature28 and other blood constituents such as macromolecules, fat and iron require further investigation. The 64-slice-CT-system employed here reflects commonly available systems, but did not offer iterative reconstruction algorithms, dual energy acquisition and larger detector arrays that allow acquisition of whole heart, isotropic volumes of in one heart beat and at low radiation dose.

In single-source 64 detector rows CT, myocardial CT attenuation is not homogenous due to artifacts, especially in the inferior wall and lateral wall. In the current study, we only included data from ROIs in the left ventricular septum. The accuracy of synthetic ECV should be validated in other segments in LV myocardium, if synthetic ECV by CT is more widely available and used in patients. Furthermore, 3D image registration and processing, reduces the errors of whole heart ECV maps.29


Synthetic hematocrit derived from the relationship between blood hematocrit and blood attenuation allows quantification of the myocardial extracellular volume fraction by cardiac computed tomography without the need for blood sampling. ECV shows great potential, allowing myocardial tissue characterization with negligible effect on workflow and radiation dose. However wider adoption requires simplification and automation of the established technique – synthetic ECV offers this.


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Figure 1: Derivation of synthetic hematocrit from the attenuation of blood

Thoracic CT scans (n=40, 53% male, age 60±20 years) with contemporaneous hematocrit samples (mean interval 8.8±7.3 days) of the scan were used to create a regression line between hematocrit (Hct; 38.2±6.0%; range 24.7-50.7%) and blood attenuation (HUblood; 40.7±8.0; range 19.5-55.2). The regression line between Hct and HUblood was linear (R2=0.47 p<0.001) with a regression equation for synthetic Hct = [0.51 * HUblood] + 17.4).

Figure 2: Validation of synthetic ECV vs conventional ECV in AS and Amyloid

Synthetic ECV, calculated using the regression model,and conventional ECV were highly correlated (R2=0.96; p<0.001; left image) with a 5.7% SD of differences and minimal bias (2.4%) on Bland-Altman analysis (right image).

Figure 3: Histological Validation of Synthetic ECV

Synthetic and conventional ECV both correlated well with collagen volume fraction (R2 = 0.50, p < 0.001 vs. R2 = 0.50, p < 0.001) and did not differ statistically.

Figure 4: OsiriX Plugin workflow

To facilitate offline analysis and allow future inline automation, an automatic synthetic ECV plug-in was developed for Osirix. Following manual segmentation of the blood pool in the pre- and post-contrast images, the plug-in automatically estimates blood hematocrit using the attenuation relationship defined above, and produces a three-dimensional myocardial ECV volume from pre- and post-contrast CCT data.



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