Quantification of porcine myocardial perfusion with modified dual bolus MRI-A prospective study with a PET reference

BackgroundThe reliable quantification of myocardial blood flow (MBF) with MRI, necessitates the correction of errors in arterial input function (AIF) caused by the T1 saturation effect. The aim of this study was to compare MBF determined by a traditional dual bolus method against a modified dual bolus approach and to evaluate both methods against PET in a porcine model of myocardial ischemia.MethodsLocal myocardial ischemia was induced in five pigs, which were subsequently examined with contrast enhanced MRI (gadoteric acid) and PET (O-15 water). In the determination of MBF, the initial high concentration AIF was corrected using the ratio of low and high contrast AIF areas, normalized according to the corresponding heart rates. MBF was determined from the MRI, during stress and at rest, using the dual bolus and the modified dual bolus methods in 24 segments of the myocardium (total of 240 segments, five pigs in stress and rest). Due to image artifacts and technical problems 53% of the segments had to be rejected from further analyses. These two estimates were later compared against respective rest and stress PET-based MBF measurements.ResultsValues of MBF were determined for 112/240 regions. Correlations for MBF between the modified dual bolus method and PET was rs = 0.84, and between the traditional dual bolus method and PET rs = 0.79. The intraclass correlation was very good (ICC = 0.85) between the modified dual bolus method and PET, but poor between the traditional dual bolus method and PET (ICC = 0.07).ConclusionsThe modified dual bolus method showed a better agreement with PET than the traditional dual bolus method. The modified dual bolus method was found to be more reliable than the traditional dual bolus method, especially when there was variation in the heart rate. However, the difference between the MBF values estimated with either of the two MRI-based dual-bolus methods and those estimated with the gold-standard PET method were statistically significant.</div

Quantification of porcine myocardial perfusion with modified dual bolus MRI : a prospective study with a PET reference

Abstract Background The reliable quantification of myocardial blood flow (MBF) with MRI, necessitates the correction of errors in arterial input function (AIF) caused by the T1 saturation effect. The aim of this study was to compare MBF determined by a traditional dual bolus method against a modified dual bolus approach and to evaluate both methods against PET in a porcine model of myocardial ischemia. Methods Local myocardial ischemia was induced in five pigs, which were subsequently examined with contrast enhanced MRI (gadoteric acid) and PET (O-15 water). In the determination of MBF, the initial high concentration AIF was corrected using the ratio of low and high contrast AIF areas, normalized according to the corresponding heart rates. MBF was determined from the MRI, during stress and at rest, using the dual bolus and the modified dual bolus methods in 24 segments of the myocardium (total of 240 segments, five pigs in stress and rest). Due to image artifacts and technical problems 53% of the segments had to be rejected from further analyses. These two estimates were later compared against respective rest and stress PET-based MBF measurements. Results Values of MBF were determined for 112/240 regions. Correlations for MBF between the modified dual bolus method and PET was rs = 0.84, and between the traditional dual bolus method and PET rs = 0.79. The intraclass correlation was very good (ICC = 0.85) between the modified dual bolus method and PET, but poor between the traditional dual bolus method and PET (ICC = 0.07). Conclusions The modified dual bolus method showed a better agreement with PET than the traditional dual bolus method. The modified dual bolus method was found to be more reliable than the traditional dual bolus method, especially when there was variation in the heart rate. However, the difference between the MBF values estimated with either of the two MRI-based dual-bolus methods and those estimated with the gold-standard PET method were statistically significant

Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R2SVM  = 0.81, R2RF  = 0.74, R2linear_regression  = 0.60; ρSVM = 0.76, ρRF = 0.76, ρlinear_regression = 0.71) and lower error (RMSESVM = 0.67 mL/g/min, RMSERF = 0.77 mL/g/min, RMSElinear_regression = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.</p

Myocardial Extracellular Volume Quantification by Cardiovascular Magnetic Resonance and Computed Tomography

PURPOSE OF REVIEW: This review article discusses the evolution of extracellular volume (ECV) quantification using both cardiovascular magnetic resonance (CMR) and computed tomography (CT). RECENT FINDINGS: Visualizing diffuse myocardial fibrosis is challenging and until recently, was restricted to the domain of the pathologist. CMR and CT both use extravascular, extracellular contrast agents, permitting ECV measurement. The evidence base around ECV quantification by CMR is growing rapidly and just starting in CT. In conditions with high ECV (amyloid, oedema and fibrosis), this technique is already being used clinically and as a surrogate endpoint. Non-invasive diffuse fibrosis quantification is also generating new biological insights into key cardiac diseases. CMR and CT can estimate ECV and in turn diffuse myocardial fibrosis, obviating the need for invasive endomyocardial biopsy. CT is an attractive alternative to CMR particularly in those individuals with contraindications to the latter. Further studies are needed, particularly in CT

Myocardial extracellular volume quantification by cardiovascularagn magnetic resonance and computed tomography

Purpose of review This review article discusses the evolution of extracellular volume (ECV) quantification using both cardiovascular magnetic resonance (CMR) and computed tomography (CT). Recent findings Visualizing diffuse myocardial fibrosis is challenging and until recently, was restricted to the domain of the pathologist. CMR and CT both use extravascular, extracellular contrast agents, permitting ECV measurement. The evidence base around ECV quantification by CMR is growing rapidly and just starting in CT. In conditions with high ECV (amyloid, oedema and fibrosis), this technique is already being used clinically and as a surrogate endpoint. Non-invasive diffuse fibrosis quantification is also generating new biological insights into key cardiac diseases. Summary CMR and CT can estimate ECV and in turn diffuse myocardial fibrosis, obviating the need for invasive endomyocardial biopsy. CT is an attractive alternative to CMR particularly in those individuals with contraindications to the latter. Further studies are needed, particularly in CT

Review of Journal of Cardiovascular Magnetic Resonance 2015

There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication

Cardiovascular Magnetic Resonance Imaging for the Investigation of Ischaemic Heart Disease

Introduction: Coronary artery disease (CAD) remains the number one cause of mortality worldwide; improving diagnosis and treatment is a priority. Multi- parametric cardiovascular magnetic resonance (CMR) offers quantitative assessment of the cardiovascular system with a variety of techniques allowing assessment of anatomy, function, myocardial composition and perfusion during a single scan. Aims: To assess 1.) diagnostic accuracy of visual and quantitative perfusion CMR to single-photon emission computed tomography (MPS-SPECT) in patients with left main stem CAD. 2.) the hypothesis that patients with ischaemic (ICM) and non-ischaemic cardiomyopathy (NICM) have different torsion and strain parameters 3.) development and validation of a contemporary multivariable risk model of CAD from a large population undergoing X-ray angiography. 4.) a rapid 3D mDIXON pulse sequence for image quality and quantitation of MI. 5.) T1 rho prepared (T1ρ) dark blood sequence and compare to blood nulled PSIR (BN) and standard myocardium nulled PSIR (MN) for detection and quantification of scar. Methods: Patients were recruited between 2008 and 2017. Patients in chapters 3,4,6,7 underwent multi-parametric CMR including late gadolinium enhancement (LGE) imaging at 1.5 or 3.0T. Patients in chapter 5 underwent angiography. Results: 1.) CMR demonstrated significantly higher area under the curve for detection of LMS or equivalent disease over MPS-SPECT(P=0.0001). 2.) Despite no difference in LV dimensions, EF and strain between ICM and NICM, NICM patients had significantly lower LV twist(P=0.023) and torsion(P=0.017) compared to ICM. 3.) The developed model discriminated well and was well-calibrated. Diamond and Forrester and Duke scores substantially over-predicted CAD risk, whilst CAD Consortium risk models slightly under-estimated risk. 4.) Image quality was comparable between 3D and 2D LGE(P=0.162). Time for 3D image acquisition was only 5% of the time required for a standard 2D acquisition. 5.) CNRscar-blood was significantly increased for BN and T1ρ compared to MN LGE. BN LGE demonstrated significantly higher reader confidence scores