Automatic Quantification of the Myocardial Extracellular

Automatic Quantification of the Myocardial extracellularAutomatic Quantification of the Myocardial Extracellular slew byCardiac Computed Tomography celluloid ECV by CCTThomas 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, PhD41Barts Heart perfume, St Bartholomews Hospital, London, UK.2Institute of Cardiovascular Science, University College London, London, UK.3UCL Centre for Medical Image Computing, De get aroundment of Medical Physics, London, UK.4Centre for Medical imaging, University College London, London, UK.Manuscript Type Original ManuscriptManuscript 3924 words (all including)No conflict of interest declared. bread and thoter 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 bio checkup 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. sit down 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.ABSTRACT TT1BackgroundThe quantification of myocardial extracellular wad element (ECV) by Cardiac Computed Tomography (CCT) can identify changes in the extracellular space due to fibrosis or infiltration. Current methodologies require science laboratory pedigree hemat ocrit (Hct) boobbeatment which complicates the technique. The attenuation of daub (HU crinkle) is known to change with anemia. We hypothesized that the race betwixt Hct and HU production concern could be calibrated to rapidly generate a semisynthetic ECV without the need to formally measure Hct.Methods This retrospective study received institutional review shape up approval. The association between Hct and HUblood was derived from forty non- blood line thoracic CT run downs utilize regression analysis. Synthetic Hct was then apply to calculate synthetic ECV, and in turn compared with ECV apply blood Hct in a well-groundedation cohort with mild interstitial expansion due to fibrosis (aortic stenosis, n=28, ECVCT = 284%) and severe interstitial expansion due to mealyosis (n=27 ECVCT = 5411%, psynthetic ECV was correlated with collagen heap fraction (CVF) in a separate cohort with aortic stenosis (n=18). All CT scans were performed at 120kV and 160 mAs.Results HUbloo d was a good predictor of Hct (R2=0.47 p), with the regression model (Hct = 0.51 * HUblood + 17.4) describing the association. Synthetic ECV correlated well with formulaic ECV (R2=0.96 p with minimal bias and 2SD balance of 5.7%. Synthetic ECV correlated as well as conventional ECV with histological CVF (both R2=0.50, p). 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 mental imagery Myocardial tissue characterization Extracellular matrix Myocardial extracellular the great unwashed fraction Myocardial fibrosis cardiac amyloidosis.LIST OF ABBREVIATIONSAL amyloidosis = Immunoglobulin light-chain amyloidosisAS = Aortic stenosisCCT = Cardiac computed tomographyCMR = Cardiovascular magnetic resonanceCVF = Collagen quite a little fractionECV = Extracellular volume fractionHU = Hounsfield unitsINTRODUCTI ONExtracellular volume fraction (ECV) quantification by cardiac computed tomography (CCT) 1-5 and cardiovascular magnetic resonance (CMR) 6, 7 is a promising red-hot 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.20MATERIAL AND METHODSThis 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. excision criteria were uncontrolled arrhythmia or impair ed renal function (estimated glomerular filtration rate ECV CCT Protocols. The CCT protocol consisted of three steps first, a low sexually transmitted disease non- line of descent scan to obtain baseline attenuations second, secernate 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, Ger many an(prenominal)).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 ( tubing 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 sect ions with a B20f kernel. All pre- and post contrast acquisitions were performed and reconstructed with the identical 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 victimisation 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 rivet of the right atrium. The mean area of these ROIs were 4.81.2cm2. 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 contra st 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 (HU) corrected by the blood volume of distribution (1 Hematocrit)ECV = (1 Hematocrit) x (HUtissue / HUblood)Synthetic Hematocrit and ECV Methodology1. Derivation of synthetic HematocritTo derive a regression model predicting hematocrit from pre-contrast HUblood, clinical unenhanced CT scans of the thorax were retrospectively canvas (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 include 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 (compar ed 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 analogue regression line between laboratory HUblood and Hct.2. Creation of a synthetic ECV EquationBlood hematocrit was substituted by the derived synthetic Hct to derive a synthetic ECV Synthetic ECV = (1 synthetic Hct) x (HUtissue / HUblood)3. Validation of synthetic ECVFor 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.13a. Clinical Validation Cohort In order to test synthetic ECV across a range of ECV values, the cohort used by our concourse 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), compr ising of 26 patients with systemic amyloidosis (21 males, age 5510 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 688 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 proceedings (for post contrast analysis), as authorize by our group previously.43b. Histological Validation CohortFor 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 be en used by our group to validate ECV by CT again histology1 Consenting severe AS patients (n = 17, median age 7110 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 exposit.21 In the histology cohort, contrast administration followed primed iodinated contrast material extract (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.14. OsiriX PluginTo facilitate offline analysis and to exemplify future inline automation by scanner manufacturers, an automatic synthetic ECV plug-in was highly- real 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, isobilateral student t-tests (significance level p). 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.RESULTSTT2 cadence 1. Derivation cohort40 thoracic CT scans with contemporaneous Hct samples within 20 days (mean 87 days) of the scan were included (n=40, 53% male, age 6020 years) with a broad range of Hct (mean 38.26.0% range 24.7-50.7%) and HUblood (mean 408 range 20-55). The linear regression equation was (sHct = 0.51 * HUblood + 17.4) with R2=0.47 p ( condition 1).Step 2. Creation of the synthetic ECV EquationBlood hematocrit was substituted by the derived synthetic Hct to derive a synthetic ECV Synthetic ECV = (1 (0.51 * HUblood + 17.4)x (HUtissue / HUblood)Step 3. Validation Step 3a. Clinical cohortBaseline characteristics of twenty-six systemic amyloidosis and tw enty-seven AS patients are basen in Table 1.In this cohort, Hct were mean 41.43.8% (range 29.3-47.4%) and HUblood mean 40.23.9 (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) 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 (5411% versus 284%, pStep 3b. Histology cohortBaseline 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 40.24.6% (range 29.4-46.4%) and HUblood 37.74.2 (range 29.5-45.1). Synthetic and conventional ECV both correlated well with collagen volume fraction (R2 = 0.50, p vs. R2 = 0.50, p 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 event o utput 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 multidimensional myocardial ECV volume, where each image voxel represents an ECV value.ReproducibilityInter- and intra-observer proportionateness 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. Testretest variability of laboratory hematocrit was higher than expected (n=44, variability 10% with hcthct R2=0.86.11DISCUSSIONIdentifying int erstitial heart disease is important for diagnosis and prognosis,10 and myocardial extracellular volume fraction (ECV) can be measured non-invasively by CCT.1-4 hitherto, its measurement is complicated by the compulsion 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 modify 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.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 feeler 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 established 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 solely 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, 24ECV (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.LimitationsTT3The 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 easily 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 much(prenominal) 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 allowance and processing, reduces the errors of whole heart ECV maps.29CONCLUSIONSynthetic 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.REFERENCES1.Bandula S, White SK, Flett AS, et al. Measurement of myocardial extracellular volume fraction by using correspondence contrast-enhanced CT validation against histologic findings. Radiology. 2013269396-403.2.Nacif MS, Kawel N, Lee JJ, et al. Interstitial myocardial fibrosis assessed as extracellular volume fraction with low-radiation-dose cardiac CT. 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Journal of computer assisted tomography. 19793506-510.29.Nacif MS, Liu Y, Yao J, et al. 3D left ventricular extracellular volume fraction by low-radiation dose cardiac CT assessment of interstitial myocardial fibrosis. J Cardiovasc Comput Tomogr. 2013751-57.FIGURESFigure 1 Derivation of synthetic hematocrit from the attenuation of bloodThoracic CT scans (n=40, 53% male, age 6020 years) with contemporaneous hematocrit samples (mean interval 8.87.3 days) of the scan were use d to create a regression line between hematocrit (Hct 38.26.0% range 24.7-50.7%) and blood attenuation (HUblood 40.78.0 range 19.5-55.2). The regression line between Hct and HUblood was linear (R2=0.47 p) with a regression equation for synthetic Hct = 0.51 * HUblood + 17.4).Figure 2 Validation of synthetic ECV vs conventional ECV in AS and AmyloidSynthetic ECV, calculated using the regression model,and conventional ECV were highly correlated (R2=0.96 p) with a 5.7% SD of differences and minimal bias (2.4%) on Bland-Altman analysis (right image).Figure 3 Histological Validation of Synthetic ECVSynthetic and conventional ECV both correlated well with collagen volume fraction (R2 = 0.50, p vs. R2 = 0.50, p ) and did not differ statistically.Figure 4 OsiriX Plugin workflowTo 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.