Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis.

BACKGROUND: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms and prognosis of patients with heart failure. In patients with depressed LV systolic function, the E/A ratio, the ratio between the peak early (E) and the peak late (A) transmitral flow velocity, is the first step to defining the grade of diastolic dysfunction. Doppler echocardiography (echo) is the preferred imaging technique for diastolic function assessment, while cardiovascular magnetic resonance (CMR) is less established as a method. Previous four-dimensional (4D) Flow-based studies have looked at the E/A ratio proximal to the mitral valve, requiring manual interaction. In this study, we compare an automated, deep learning-based and two semi-automated approaches for 4D Flow CMR-based E/A ratio assessment to conventional, gold-standard echo-based methods. METHODS: Ninety-seven subjects with chronic ischemic heart disease underwent a cardiac echo followed by CMR investigation. 4D Flow-based E/A ratio values were computed using three different approaches; two semi-automated, assessing the E/A ratio by measuring the inflow velocity (MVvel) and the inflow volume (MVflow) at the mitral valve plane, and one fully automated, creating a full LV segmentation using a deep learning-based method with which the E/A ratio could be assessed without constraint to the mitral plane (LVvel). RESULTS: MVvel, MVflow, and LVvel E/A ratios were strongly associated with echocardiographically derived E/A ratio (R2 = 0.60, 0.58, 0.72). LVvel peak E and A showed moderate association to Echo peak E and A, while MVvel values were weakly associated. MVvel and MVflow EA ratios were very strongly associated with LVvel (R2 = 0.84, 0.86). MVvel peak E was moderately associated with LVvel, while peak A showed a strong association (R2 = 0.26, 0.57). CONCLUSION: Peak E, peak A, and E/A ratio are integral to the assessment of diastolic dysfunction and may expand the utility of CMR studies in patients with cardiovascular disease. While underestimation of absolute peak E and A velocities was noted, the E/A ratio measured with all three 4D Flow methods was strongly associated with the gold standard Doppler echocardiography. The automatic, deep learning-based method performed best, with the most favorable runtime of ∼40 seconds. As both semi-automatic methods associated very strongly to LVvel, they could be employed as an alternative for estimation of E/A ratio.

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI-based personalized cardiovascular mathematical model.

Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. KEY POINTS: The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.

Whole body MRI with Diffusion Weighted Imaging versus 18F-fluorodeoxyglucose-PET/CT in the staging of lymphomas.

PURPOSE: To assess the diagnostic performance of Whole Body (WB)-MRI in comparison with 18F-Fluorodeoxyglucose-PET/CT (18F-FDG-PET/CT) in lymphoma staging and to assess whether quantitative metabolic parameters from 18F-FDG-PET/CT and Apparent Diffusion Coefficient (ADC) values are related. MATERIALS AND METHODS: We prospectively enrolled patients with a histologically proven primary nodal lymphoma to  undergo 18F-FDG-PET/CT and WB-MRI, both performed within 15 days one from the other, either before starting treatment (baseline) or during treatment (interim). Positive and negative predictive values of WB-MRI for the identification of nodal and extra-nodal disease were measured. The agreement between WB-MRI and 18F-FDG-PET/CT for the identification of lesions and staging was assessed through Cohen's coefficient k and observed agreement. Quantitative parameters of nodal lesions derived from 18F-FDG-PET/CT and WB-MRI (ADC) were measured and the Pearson or Spearman correlation coefficient was used to assess the correlation between them. The specified level of significance was p ≤ 0.05. RESULTS: Among the 91 identified patients, 8 refused to participate and 22 met exclusion criteria, thus images from 61 patients (37 men, mean age 30.7 years) were evaluated. The agreement between 18F-FDG-PET/CT and WB-MRI for the identification of nodal and extra-nodal lesions was 0.95 (95% CI 0.92 to 0.98) and 1.00 (95% CI NA), respectively; for staging it was 1.00 (95% CI NA). A strong negative correlation was found between ADCmean and SUVmean of nodal lesions in patients evaluated at baseline (Spearman coefficient rs = - 0.61, p = 0.001). CONCLUSION: WB-MRI has a good diagnostic performance for staging of patients with lymphoma in comparison with 18F-FDG-PET/CT and is a promising technique for the quantitative assessment of disease burden in these patients.

Impact of dobutamine stress on diastolic energetic efficiency of healthy left ventricle: an in vivo kinetic energy analysis.

The total kinetic energy (KE) of blood can be decomposed into mean KE (MKE) and turbulent KE (TKE), which are associated with the phase-averaged fluid velocity field and the instantaneous velocity fluctuations, respectively. The aim of this study was to explore the effects of pharmacologically induced stress on MKE and TKE in the left ventricle (LV) in a cohort of healthy volunteers. 4D Flow MRI data were acquired in eleven subjects at rest and after dobutamine infusion, at a heart rate that was ∼60% higher than the one in rest conditions. MKE and TKE were computed as volume integrals over the whole LV and as data mapped to functional LV flow components, i.e., direct flow, retained inflow, delayed ejection flow and residual volume. Diastolic MKE and TKE increased under stress, in particular at peak early filling and peak atrial contraction. Augmented LV inotropy and cardiac frequency also caused an increase in direct flow and retained inflow MKE and TKE. However, the TKE/KE ratio remained comparable between rest and stress conditions, suggesting that LV intracavitary fluid dynamics can adapt to stress conditions without altering the TKE to KE balance of the normal left ventricle at rest.

Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.

BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue. PURPOSE: To develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI. STUDY TYPE: Retrospective. SUBJECTS: A total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets. FIELD STRENGTH/SEQUENCE: A 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI). ASSESSMENT: A 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations. STATISTICAL TESTS: Dice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters. RESULTS: The following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively. DATA CONCLUSION: The addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Assessment of Blood Flow Transport in the Left Ventricle Using Ultrasound. Validation Against 4-D Flow Cardiac Magnetic Resonance.

Four-dimensional flow cardiac magnetic resonance (CMR) is the reference technique for analyzing blood transport in the left ventricle (LV), but similar information can be obtained from ultrasound. We aimed to validate ultrasound-derived transport in a head-to-head comparison against 4D flow CMR. In five patients and two healthy volunteers, we obtained 2D + t and 3D + t (4D) flow fields in the LV using transthoracic echocardiography and CMR, respectively. We compartmentalized intraventricular blood flow into four fractions of end-diastolic volume: direct flow (DF), retained inflow (RI), delayed ejection flow (DEF) and residual volume (RV). Using ultrasound we also computed the properties of LV filling waves (percentage of LV penetration and percentage of LV volume carried by E/A waves) to determine their relationships with CMR transport. Agreement between both techniques for quantifying transport fractions was good for DF and RV (Ric [95% confidence interval]: 0.82 [0.33, 0.97] and 0.85 [0.41, 0.97], respectively) and moderate for RI and DEF (Ric= 0.47 [-0.29, 0.88] and 0.55 [-0.20, 0.90], respectively). Agreement between techniques to measure kinetic energy was variable. The amount of blood carried by the E-wave correlated with DF and RV (R = 0.75 and R = 0.63, respectively). Therefore, ultrasound is a suitable method for expanding the analysis of intraventricular flow transport in the clinical setting.

A Relapsing Meningeal Acute Myeloid Leukaemia FLT3-ITD+ Responding to Gilteritinib.

A patient with a therapy-related acute myeloid leukaemia (AML), NPM1mut, and FLT3-ITD+ was treated with induction and consolidation with CPX-351, obtaining a complete response (CR) but minimal residual disease persisted positive. Later, she complained progressive burning leg pain, weakening of the right hand and leg muscles, associated with absence of osteotendinous leg reflexes. Examination of cerebrospinal fluid (CSF) showed a meningeal relapse of AML. Moreover, a magnetic resonance imaging (MRI) showed 2 right meningeal implants of myeloid sarcoma and bone marrow revealed haematologic relapse of disease. She was treated with medicated lumbar punctures (LPs) followed by an FLA-Ida scheme, and she achieved a 2nd CR. Unfortunately, the patient developed hyperleucocytosis and reappearance of meningeal myeloid sarcoma at MRI. For this reason, a monotherapy with gilteritinib (an FLT3 inhibitor) was started: after 3 months of therapy, central nervous system (CNS)-disease shrunken and then faded, while AML in the bone marrow achieved only a partial response. This is the 1st report of a positive biological effect of gilteritinib on CNS (meningeal) myeloid sarcoma. There are no studies of gilteritinib concentration into CSF and penetration of gilteritinib into the blood-brain barrier should be further studied, given the paucity of drugs active on CNS relapse of AML. In patients receiving CPX-351 only, diagnostic LP should be considered after induction.

Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI.

BACKGROUND: Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications. PURPOSE: To develop and evaluate a deep learning architecture to generate high blood-tissue contrast in noncontrast 4D flow MRI by emulating the use of an external contrast agent. STUDY TYPE: Retrospective. SUBJECTS: Of 222 data sets, 141 were used for neural network (NN) training (69 with and 72 without contrast agent). Evaluation was performed on the remaining 81 noncontrast data sets. FIELD STRENGTH/SEQUENCES: Gradient echo or echo-planar 4D flow MRI at 1.5 T and 3 T. ASSESSMENT: A cyclic generative adversarial NN was trained to perform image translation between noncontrast and contrast data. Evaluation was performed quantitatively using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), structural similarity index (SSIM), mean squared error (MSE) of edges, and Dice coefficient of segmentations. Three observers performed a qualitative assessment of blood-tissue contrast, noise, presence of artifacts, and image structure visualization. STATISTICAL TESTS: The Wilcoxon rank-sum test evaluated statistical significance. Kendall's concordance coefficient assessed interobserver agreement. RESULTS: Contrast in the regions of interest (ROIs) in the NN enhanced images increased by 88%, CNR increased by 63%, and SNR improved by 48% (all P < 0.001). The SSIM was 0.82 ± 0.01, and the MSE of edges was 0.09 ± 0.01 (range [0,1]). Segmentations based on the generated images resulted in a Dice similarity increase of 15.25%. The observers managed to differentiate between contrast MR images and our results; however, they preferred the NN enhanced images in 76.7% of cases. This percentage increased to 93.3% for phase-contrast MR angiograms created from the NN enhanced data. Visual grading scores were blood-tissue contrast = 4.30 ± 0.74, noise = 3.12 ± 0.98, and presence of artifacts = 3.63 ± 0.76. Image structures within and without the ROIs resulted in scores of 3.42 ± 0.59 and 3.07 ± 0.71, respectively (P < 0.001). DATA CONCLUSION: The proposed approach improves blood-tissue contrast in MR images and could be used to improve data quality, visualization, and postprocessing of cardiovascular 4D flow data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Non-invasive estimation of relative pressure for intracardiac flows using virtual work-energy.

Intracardiac blood flow is driven by differences in relative pressure, and assessing these is critical in understanding cardiac disease. Non-invasive image-based methods exist to assess relative pressure, however, the complex flow and dynamically moving fluid domain of the intracardiac space limits assessment. Recently, we proposed a method, νWERP, utilizing an auxiliary virtual field to probe relative pressure through complex, and previously inaccessible flow domains. Here we present an extension of νWERP for intracardiac flow assessments, solving the virtual field over sub-domains to effectively handle the dynamically shifting flow domain. The extended νWERP is validated in an in-silico benchmark problem, as well as in a patient-specific simulation model of the left heart, proving accurate over ranges of realistic image resolutions and noise levels, as well as superior to alternative approaches. Lastly, the extended νWERP is applied on clinically acquired 4D Flow MRI data, exhibiting realistic ventricular relative pressure patterns, as well as indicating signs of diastolic dysfunction in an exemplifying patient case. Summarized, the extended νWERP approach represents a directly applicable implementation for intracardiac flow assessments.

Quantification of epicardial fat using 3D cine Dixon MRI.

BACKGROUND: There is an increased interest in quantifying and characterizing epicardial fat which has been linked to various cardiovascular diseases such as coronary artery disease and atrial fibrillation. Recently, three-dimensional single-phase Dixon techniques have been used to depict the heart and to quantify the surrounding fat. The purpose of this study was to investigate the merits of a new high-resolution cine 3D Dixon technique for quantification of epicardial adipose tissue and compare it to single-phase 3D Dixon in patients with cardiovascular disease. METHODS: Fifteen patients referred for clinical CMR examination of known or suspected heart disease were scanned on a 1.5 T scanner using single-phase Dixon and cine Dixon. Epicardial fat was segmented by three readers and intra- and inter-observer variability was calculated per slice. Cine Dixon segmentation was performed in the same cardiac phase as single-phase Dixon. Subjective image quality assessment of water and fat images were performed by three readers using a 4-point Likert scale (1 = severe; 2 = significant; 3 = mild; 4 = no blurring of cardiac structures). RESULTS: Intra-observer variability was excellent for cine Dixon images (ICC = 0.96), and higher than single-phase Dixon (ICC = 0.92). Inter-observer variability was good for cine Dixon (ICC = 0.76) and moderate for single-phase Dixon (ICC = 0.63). The intra-observer measurement error (mean ± standard deviation) per slice for cine was - 0.02 ± 0.51 ml (- 0.08 ± 0.4%), and for single-phase 0.39 ± 0.72 ml (0.18 ± 0.41%). Inter-observer measurement error for cine was 0.46 ± 0.98 ml (0.11 ± 0.46%) and for single-phase 0.42 ± 1.53 ml (0.17 ± 0.47%). Visual scoring of the water image yielded median of 2 (interquartile range = [Q3-Q1] 2-2) for cine and median of 3 (interquartile range = 3-2) for single-phase (P < 0.05) while no significant difference was found for the fat images, both techniques yielding a median of 3 and interquartile range of 3-2. CONCLUSION: Cine Dixon can be used to quantify epicardial fat with lower intra- and inter-observer variability compared to standard single-phase Dixon. The time-resolved information provided by the cine acquisition appears to support the delineation of the epicardial adipose tissue depot.

Publisher Correction: Bridging the gap between measurements and modelling: a cardiovascular functional avatar.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Data Quality and Optimal Background Correction Order of Respiratory-Gated k-Space Segmented Spoiled Gradient Echo (SGRE) and Echo Planar Imaging (EPI)-Based 4D Flow MRI.

BACKGROUND: A reduction in scan time of 4D Flow MRI would facilitate clinical application. A recent study indicates that echo-planar imaging (EPI) 4D Flow MRI allows for a reduction in scan time and better data quality than the recommended k-space segmented spoiled gradient echo (SGRE) sequence. It was argued that the poor data quality of SGRE was related to the nonrecommended absence of respiratory motion compensation. However, data quality can also be affected by the background offset compensation. PURPOSE: To compare the data quality of respiratory motion-compensated SGRE and EPI 4D Flow MRI and their dependence on background correction (BC) order. STUDY TYPE: Retrospective. SUBJECTS: Eighteen healthy subjects (eight female, mean age 32 ± 5 years). FIELD STRENGTH AND SEQUENCE: 1.5 T. [Correction added on July 26, 2019, after first online publication: The preceding field strength was corrected.] SGRE and EPI-based 4D Flow MRI. ASSESSMENT: Data quality was investigated visually and by comparing flows through the cardiac valves and aorta. Measurements were obtained from transvalvular flow and pathline analysis. STATISTICAL TESTS: Linear regression and Bland-Altman analysis were used. Wilcoxon test was used for comparison of visual scoring. Student's t-test was used for comparison of flow volumes. RESULTS: No significant difference was found by visual inspection (P = 0.08). Left ventricular (LV) flows were strongly and very strongly associated with SGRE and EPI, respectively (R2 = 0.86-0.94 SGRE; 0.71-0.79 EPI, BC0-4). LV and right ventricular (RV) outflows and LV pathline flows were very strongly associated (R2 = 0.93-0.95 SGRE; 0.88-0.91 EPI, R2 = 0.91-0.95 SGRE; 0.91-0.93 EPI, BC1-4). EPI LV outflow was lower than the short-axis-based stroke volume. EPI RV outflow and proximal descending aortic flow were lower than SGREs. DATA CONCLUSION: Both sequences yielded good internal data consistency when an adequate background correction was applied. Second and first BC order were considered sufficient for transvalvular flow analysis in SGRE and EPI, respectively. Higher BC orders were preferred for particle tracing. Level of Evidence 4 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2020;51:885-896.

Non-invasive Assessment of Systolic and Diastolic Cardiac Function During Rest and Stress Conditions Using an Integrated Image-Modeling Approach.

Background: The possibility of non-invasively assessing load-independent parameters characterizing cardiac function is of high clinical value. Typically, these parameters are assessed during resting conditions. However, for diagnostic purposes, the parameter behavior across a physiologically relevant range of heart rate and loads is more relevant than the isolated measurements performed at rest. This study sought to evaluate changes in non-invasive estimations of load-independent parameters of left-ventricular contraction and relaxation patterns at rest and during dobutamine stress. Methods: We applied a previously developed approach that combines non-invasive measurements with a physiologically-based, reduced-order model of the cardiovascular system to provide subject-specific estimates of parameters characterizing left ventricular function. In this model, the contractile state of the heart at each time point along the cardiac cycle is modeled using a time-varying elastance curve. Non-invasive data, including four-dimensional magnetic resonance imaging (4D Flow MRI) measurements, were acquired in nine subjects without a known heart disease at rest and during dobutamine stress. For each of the study subjects, we constructed two personalized models corresponding to the resting and the stress state. Results: Applying the modeling framework, we identified significant increases in the left ventricular contraction rate constant [from 1.5 ± 0.3 to 2 ± 0.5 (p = 0.038)] and relaxation constant [from 37.2 ± 6.9 to 46.1 ± 12 (p = 0.028)]. In addition, we found a significant decrease in the elastance diastolic time constant from 0.4 ± 0.04 s to 0.3 ± 0.03 s (p = 0.008). Conclusions: The integrated image-modeling approach allows the assessment of cardiovascular function given as model-based parameters. The agreement between the estimated parameter values and previously reported effects of dobutamine demonstrates the potential of the approach to assess advanced metrics of pathophysiology that are otherwise difficult to obtain non-invasively in clinical practice.

Bridging the gap between measurements and modelling: a cardiovascular functional avatar.

Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.