Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance

Deep learning-based segmentation of left ventricular myocardium on dynamic contrast-enhanced MRI: a comprehensive evaluation across temporal frames

PURPOSE

Cardiac perfusion MRI is vital for disease diagnosis, treatment planning, and risk stratification, with anomalies serving as markers of underlying ischemic pathologies. AI-assisted methods and tools enable accurate and efficient left ventricular (LV) myocardium segmentation on all DCE-MRI timeframes, offering a solution to the challenges posed by the multidimensional nature of the data. This study aims to develop and assess an automated method for LV myocardial segmentation on DCE-MRI data of a local hospital.

METHODS

The study consists of retrospective DCE-MRI data from 55 subjects acquired at the local hospital using a 1.5 T MRI scanner. The dataset included subjects with and without cardiac abnormalities. The timepoint for the reference frame (post-contrast LV myocardium) was identified using standard deviation across the temporal sequences. Iterative image registration of other temporal images with respect to this reference image was performed using Maxwell's demons algorithm. The registered stack was fed to the model built using the U-Net framework for predicting the LV myocardium at all timeframes of DCE-MRI.

RESULTS

The mean and standard deviation of the dice similarity coefficient (DSC) for myocardial segmentation using pre-trained network Net_cine is 0.78 ± 0.04, and for the fine-tuned network Net_dyn which predicts mask on all timeframes individually, it is 0.78 ± 0.03. The DSC for Net_dyn ranged from 0.71 to 0.93. The average DSC achieved for the reference frame is 0.82 ± 0.06.

CONCLUSION

The study proposed a fast and fully automated AI-assisted method to segment LV myocardium on all timeframes of DCE-MRI data. The method is robust, and its performance is independent of the intra-temporal sequence registration and can easily accommodate timeframes with potential registration errors.

Clinical implementation of a fully automated quantitative perfusion cardiovascular magnetic resonance imaging workflow with a simplified dual-bolus contrast administration scheme

This study clinically implemented a ready-to-use quantitative perfusion (QP) cardiovascular magnetic resonance (QP CMR) workflow, encompassing a simplified dual-bolus gadolinium-based contrast agent (GBCA) administration scheme and fully automated QP image post-processing. Twenty-five patients with suspected obstructive coronary artery disease (CAD) underwent both adenosine stress perfusion CMR and an invasive coronary angiography or coronary computed tomography angiography. The dual-bolus protocol consisted of a pre-bolus (0.0075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) and a main bolus (0.075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) at an infusion rate of 3 ml/s. The arterial input function curves showed excellent quality. Stress MBF ≤ 1.84 ml/g/min accurately detected obstructive CAD (area under the curve 0.79; 95% Confidence Interval: 0.66 to 0.89). Combined visual assessment of color pixel QP maps and conventional perfusion images yielded a diagnostic accuracy of 84%, sensitivity of 70% and specificity of 93%. The proposed easy-to-use dual-bolus QP CMR workflow provides good image quality and holds promise for high accuracy in diagnosis of obstructive CAD. Implementation of this approach has the potential to serve as an alternative to current methods thus increasing the accessibility to offer high-quality QP CMR imaging by a wide range of CMR laboratories.

Coronary Microvascular Dysfunction and Diffuse Myocardial Fibrosis in Patients With Type 2 Diabetes Using Quantitative Perfusion MRI

BACKGROUND

Imaging techniques that quantitatively and automatically measure changes in the myocardial microcirculation in patients with diabetes are lacking.

PURPOSE

To detect diabetic myocardial microvascular complications using a novel automatic quantitative perfusion MRI technique, and to explore the relationship between myocardial microcirculation dysfunction and fibrosis.

STUDY TYPE

Prospective.

SUBJECTS

101 patients with type 2 diabetes mellitus (T2DM) (53 without and 48 with complications), 20 healthy volunteers.

FIELD STRENGTH/SEQUENCE

3.0T; modified Look-Locker inversion-recovery sequence; saturation recovery sequence and dual-bolus technique; segmented fast low-angle shot sequence.

ASSESSMENT

All participants underwent MRI to determine the rest myocardial blood flow (MBF), stress MBF, myocardial perfusion reserve (MPR), and extracellular volume (ECV), which represents the extent of myocardial fibrosis.

STATISTICAL TESTS

Kolmogorov-Smirnov test, Shapiro-Wilk test, Kruskal-Wallis H test, Mann-Whitney U test, chi-square test, Spearman correlation coefficient, multivariable linear regression analysis. P < 0.05 was considered statistically significant.

RESULTS

The rest MBF was not significantly different between the T2DM without complications group (1.1, IQR: 0.9-1.3) and the control group (1.1, 1.0-1.3) (P = 1.000), but it was significantly lower in the T2DM with complications group (0.8, 0.6-1.0) than in both other groups. The stress MBF and MPR were significantly lower in the T2DM without complications group (1.9, 1.5-2.3, and 1.7, 1.4-2.1, respectively) than in the control group (3.0, 2.6-3.5, and 2.7, 2.4-3.1, respectively), and were also significantly lower in the T2DM with complications group (1.1, 0.9-1.4, and 1.4, 1.2-1.8, respectively) than in the T2DM without complications group. A decrease in MBF and MPR were significantly associated with an increase in the ECV.

DATA CONCLUSION

Quantitative perfusion MRI can evaluate myocardial microcirculation dysfunction. In T2DM, there was a significant decrease in both MBF and MPR compared to healthy controls, with the decrease being significantly different between T2DM with and without complications groups. The decrease of MBF was significantly associated with the development of myocardial fibrosis, as determined by ECV.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance

Aims

One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.

Methods and results

A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann-Whitney U test and Bland-Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland-Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of -0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments.

Conclusion

Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.

Abdominal motion tracking with free-breathing XD-GRASP acquisitions using spatio-temporal geodesic trajectories

Free-breathing external beam radiotherapy remains challenging due to the complex elastic or irregular motion of abdominal organs, as imaging moving organs leads to the creation of motion blurring artifacts. In this paper, we propose a radial-based MRI reconstruction method from 3D free-breathing abdominal data using spatio-temporal geodesic trajectories, to quantify motion during radiotherapy. The prospective study was approved by the institutional review board and consent was obtained from all participants. A total of 25 healthy volunteers, 12 women and 13 men (38 years ± 12 [standard deviation]), and 11 liver cancer patients underwent imaging using a 3.0 T clinical MRI system. The radial acquisition based on golden-angle sparse sampling was performed using a 3D stack-of-stars gradient-echo sequence and reconstructed using a discretized piecewise spatio-temporal trajectory defined in a low-dimensional embedding, which tracks the inhale and exhale phases, allowing the separation between distinct motion phases. Liver displacement between phases as measured with the proposed radial approach based on the deformation vector fields was compared to a navigator-based approach. Images reconstructed with the proposed technique with 20 motion states and registered with the multiscale B-spline approach received on average the highest Likert scores for the overall image quality and visual SNR score 3.2 ± 0.3 (mean ± standard deviation), with liver displacement errors varying between 0.1 and 2.0 mm (mean 0.8 ± 0.6 mm). When compared to navigator-based approaches, the proposed method yields similar deformation vector field magnitudes and angle distributions, and with improved reconstruction accuracy based on mean squared errors. Schematic illustration of the proposed 4D-MRI reconstruction method based on radial golden-angle acquisitions and a respiration motion model from a manifold embedding used for motion tracking. First, data is extracted from the center of k-space using golden-angle sampling, which is then mapped onto a low-dimensional embedding, describing the relationship between neighboring samples in the breathing cycle. The trained model is then used to extract the respiratory motion signal for slice re-ordering. The process then improves the image quality through deformable image registration. Using a reference volume, the deformation vector field (DVF) of sequential motion states are extracted, followed by deformable registrations. The output is a 4DMRI which allows to visualize and quantify motion during free-breathing.

Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation

In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Background

Advances in sub-Nyquist–sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility.

Purpose

To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma.

Materials and Methods

Two methods for sub-Nyquist–sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M₀ maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (K^trans) on five healthy tissue types.

Results

The images from 13 participants (mean age ± standard deviation, 61 years ± 10; nine women) with high-grade glioma were evaluated. In healthy tissues, the protocol achieved a coefficient of variation less than 57% for median K^trans, if K^trans was estimated consecutively. The maximum reproducibility coefficient for median K^trans was estimated to be at 0.06 min^–1 for large or low-enhancing tissues and to be as high as 0.48 min^–1 in smaller or strongly enhancing tissues.

Conclusion

A fully automated, sparsely sampled DCE MRI reconstruction with patient-specific vascular input function offered high spatial and temporal resolution and whole-brain coverage; in healthy tissues, the protocol estimated median volume transfer constant with maximum reproducibility coefficient of 0.06 min^–1 in large, low-enhancing tissue regions and maximum reproducibility coefficient of less than 0.48 min^–1 in smaller or more strongly enhancing tissue regions.

Published under a CC BY 4.0 license.

Online supplemental material is available for this article.

See also the editorial by Lenkinski in this issue.

Direct Comparison of Bayesian and Fermi Deconvolution Approaches for Myocardial Blood Flow Quantification: In silico and Clinical Validations

Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al., 2014). In addition to providing accurate perfusion measurements, this probabilistic approach appears more robust than previous approaches, particularly due to its insensitivity to bolus arrival delays. We assessed the performance of this approach against a well-known and commonly deployed model-independent method based on the Fermi function for cardiac magnetic resonance myocardial perfusion imaging. The methods were first evaluated for accuracy and precision using a digital phantom to test them against the ground truth; next, they were applied in a group of coronary artery disease patients. The Bayesian method can be considered an appropriate model-independent method with which to estimate myocardial blood flow and delays. The digital phantom comprised a set of synthetic time-concentration curve combinations generated with a 2-compartment exchange model and a realistic combination of perfusion indexes, arterial input dynamics, noise and delays collected from the clinical dataset. The myocardial blood flow values estimated with the two methods showed an excellent correlation coefficient (r² > 0.9) under all noise and delay conditions. The Bayesian approach showed excellent robustness to bolus arrival delays, with a similar performance to Fermi modeling when delays were considered. Delays were better estimated with the Bayesian approach than with Fermi modeling. An in vivo analysis of coronary artery disease patients revealed that the Bayesian approach had an excellent ability to distinguish between abnormal and normal myocardium. The Bayesian approach was able to discriminate not only flows but also delays with increased sensitivity by offering a clearly enlarged range of distribution for the physiologic parameters.

Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance

First pass gadolinium-enhanced cardiovascular magnetic resonance (CMR) perfusion imaging allows fully quantitative pixel-wise myocardial blood flow (MBF) assessment, with proven diagnostic value for coronary artery disease. Segmental analysis requires manual segmentation of the myocardium. This work presents a fully automatic method of segmenting the left ventricular myocardium from MBF pixel maps, validated on a retrospective dataset of 247 clinical CMR perfusion studies, each including rest and stress images of three slice locations, performed on a 1.5T scanner. Pixel-wise MBF maps were segmented using an automated pipeline including region growing, edge detection, principal component analysis, and active contours to segment the myocardium, detect key landmarks, and divide the myocardium into sectors appropriate for analysis. Automated segmentation results were compared against a manually defined reference standard using three quantitative metrics: Dice coefficient, Cohen Kappa and myocardial border distance. Sector-wise average MBF and myocardial perfusion reserve (MPR) were compared using Pearson's correlation coefficient and Bland-Altman Plots. The proposed method segmented stress and rest MBF maps of 243 studies automatically. Automated and manual myocardial segmentation had an average (± standard deviation) Dice coefficient of 0.86 ± 0.06, Cohen Kappa of 0.86 ± 0.06, and Euclidian distances of 1.47 ± 0.73 mm and 1.02 ± 0.51 mm for the epicardial and endocardial border, respectively. Automated and manual sector-wise MBF and MPR values correlated with Pearson's coefficient of 0.97 and 0.92, respectively, while Bland-Altman analysis showed bias of 0.01 and 0.07 ml/g/min. The validated method has been integrated with our fully automated MBF pixel mapping pipeline to aid quantitative assessment of myocardial perfusion CMR.

Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network

Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.

Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence

This paper presents a prospective study evaluating the impact on image quality and quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters when varying the number of respiratory motion states when using an eXtra-Dimensional Golden-Angle Radial Sparse Parallel (XD-GRASP) MRI sequence. DCE acquisition was performed using a 3D stack-of-stars gradient-echo golden-angle radial acquisition in free-breathing with 100 spokes per motion state and temporal resolution of 6 s/volume, and using a non-rigid motion compensation to align different motion states. Parametric analysis was conducted using a dual-input single-compartment model. Nonparametric analysis was performed on the time-intensity curves. A total of 22 hepatocellular carcinomas (size: 11-52 mm) were evaluated. XD-GRASP reconstructed with increasing number of spokes for each motion state increased the signal-to-noise ratio (SNR) (p < 0.05) but decreased temporal resolution (0.04 volume/s vs 0.17 volume/s for one motion state) (p < 0.05). A visual scoring by an experienced radiologist show no change between increasing number of motion states with same number of spokes using the Likert score. The normalized maximum intensity time ratio, peak enhancement ratio and tumor arterial fraction increased with decreasing number of motion states (p < 0.05) while the transfer constant from the portal venous plasma to the surrounding tissue significantly decreased (p < 0.05). These same perfusion parameters show a significant difference in case of tumor displacement more than 1 cm (p < 0.05) whereas in the opposite case there was no significant variation. While a higher number of motion states and higher number of spokes improves SNR, the resulting lower temporal resolution can influence quantitative parameters that capture rapid signal changes. Finally, fewer displacement compensation is advantageous with lower number of motion state due to the higher temporal resolution. XD-GRASP can be used to perform quantitative perfusion measures in the liver, but the number of motion states may significantly alter some quantitative parameters.

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

BACKGROUND AND OBJECTIVE

Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation.

METHODS

A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset.

RESULTS

The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P < 0.001).

CONCLUSIONS

Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts.

Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia

Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.

Hierarchical Bayesian myocardial perfusion quantification

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Dynamic contrast-enhanced MRI to assess hepatocellular carcinoma response to Transarterial chemoembolization using LI-RADS criteria: A pilot study

PURPOSE

To identify quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters indicating tumor response of hepatocellular carcinoma (HCC) to transarterial chemoembolization (TACE).

MATERIALS AND METHODS

This prospective pilot study was approved by our institutional review board; written and informed consent was obtained for each participant. Patients underwent DCE-MRI examinations before and after TACE. A variable flip-angle unenhanced 3D mDixon sequence was performed for T1 mapping. A dynamic 4D mDixon sequence was performed after contrast injection for assessing dynamic signal enhancement. Nonparametric analysis was conducted on the time-intensity curves. Parametric analysis was performed on the time-concentration curves using a dual-input single-compartment model. Treatment response according to Liver Reporting and Data System (LI-RADS) v2018 was used as the reference standard. The comparisons within groups (before vs. after treatment) and between groups (nonviable vs. equivocal or viable tumor) were performed using nonparametric bootstrap taking into account the clustering effect of lesions in patients.

RESULTS

Twenty-eight patients with 52 HCCs (size: 10-104 mm) were evaluated. For nonviable tumors (n = 27), time to peak increased from 62.5 ± 18.2 s before to 83.3 ± 12.8 s after treatment (P< 0.01). For equivocal or viable tumors (n = 25), time to peak and mean transit time significantly increased (from 54.4 ± 24.1 s to 69.5 ± 18.9 s, P < 0.01 and from 14.2 ± 11.8 s to 33.9 ± 36.8 s, P= 0.01, respectively) and the transfer constant from the extracellular and extravascular space to the central vein significantly decreased from 14.8 ± 14.1 to 8.1 ± 9.1 s^-1 after treatment (P= 0.01).

CONCLUSION

This prospective pilot DCE-MRI study showed that time to peak significantly changed after TACE treatment for both groups (nonviable tumors and equivocal or viable tumors). In our cohort, several perfusion parameters may provide an objective marker for differentiation of treatment response after TACE in HCC patients.

Automated Quantitative Stress Perfusion in a Clinical Routine

Cardiovascular magnetic resonance (CMR) perfusion imaging is a robust noninvasive technique to evaluate ischemia in patients with coronary artery disease (CAD). Although qualitative and semiquantitative methods have shown that CMR has high accuracy in diagnosing flow-obstructing lesions in CAD, quantitative ischemic burden is an important variable used in clinical practice for treatment decisions. Quantitative CMR perfusion techniques have evolved significantly, with accuracy comparable with both PET and microsphere evaluation. Routine clinical use of these quantitative techniques has been facilitated by the introduction of automated methods that accelerate the work flow and rapidly generate pixel-based myocardial blood flow maps.

Journal of Cardiovascular Magnetic Resonance 2017

There were 106 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2017, including 92 original research papers, 3 reviews, 9 technical notes, and 1 Position paper, 1 erratum and 1 correction. The volume was similar to 2016 despite an increase in manuscript submissions to 405 and thus reflects a slight decrease in the acceptance rate to 26.7%. The quality of the submissions continues to be high. The 2017 JCMR Impact Factor (which is published in June 2018) was minimally lower at 5.46 (vs. 5.71 for 2016; as published in June 2017), which is the second highest impact factor ever recorded for JCMR. The 2017 impact factor means that an average, each JCMR paper that were published in 2015 and 2016 was cited 5.46 times in 2017.In accordance with Open-Access publishing of Biomed Central, the JCMR articles are published on-line in continuus fashion and in the chronologic order of acceptance, with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful to annually summarize the publications into broad areas of interest or theme, so that readers can view areas of interest in a single article in relation to each other and other contemporary JCMR articles. In this publication, the manuscripts are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought within the journal. In addition, I have elected to use this format to convey information regarding the editorial process to the readership.I hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your very best, high quality manuscripts to JCMR for consideration. I thank our very dedicated Associate Editors, Guest Editors, and Reviewers for their efforts to ensure that the review process occurs in a timely and responsible manner and that the JCMR continues to be recognized as the forefront journal of our field. And finally, I thank you for entrusting me with the editorship of the JCMR as I begin my 3rd year as your editor-in-chief. It has been a tremendous learning experience for me and the opportunity to review manuscripts that reflect the best in our field remains a great joy and highlight of my week!

A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging

PURPOSE

Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.

METHODS

Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R² ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.

RESULTS

Our multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R² and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R² and NRMSE are 0.91 and 0.31, respectively.

CONCLUSIONS

The proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.

Sampling arterial input function (AIF) from peripheral arteries: Comparison of a temporospatial-feature based method against conventional manual method

It is often difficult to accurately localize small arteries in images of peripheral organs, and even more so with vascular abnormality vasculatures, including collateral arteries, in peripheral artery disease (PAD). This poses a challenge for manually sampling arterial input function (AIF) in quantifying dynamic contrast-enhanced (DCE) MRI data of peripheral organs. In this study, we designed a multi-step screening approach that utilizes both the temporal and spatial information of the dynamic images, and is presumably suitable for localizing small and unpredictable peripheral arteries. In 41 DCE MRI datasets acquired from human calf muscles, the proposed method took <5 s on average for sampling AIF for each case, much more efficient than the manual sampling method; AIFs by the two methods were comparable, with Pearson's correlation coefficient of 0.983 ± 0.004 (p-value < 0.01) and relative difference of 2.4% ± 2.6%. In conclusion, the proposed temporospatial-feature based method enables efficient and accurate sampling of AIF from peripheral arteries, and would improve measurement precision and inter-observer consistency for quantitative DCE MRI of peripheral tissues.

Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance

BACKGROUND

Clinical evaluation of stress perfusion cardiovascular magnetic resonance (CMR) is currently based on visual assessment and has shown high diagnostic accuracy in previous clinical trials, when performed by expert readers or core laboratories. However, these results may not be generalizable to clinical practice, particularly when less experienced readers are concerned. Other factors, such as the level of training, the extent of ischemia, and image quality could affect the diagnostic accuracy. Moreover, the role of rest images has not been clarified. The aim of this study was to assess the diagnostic accuracy of visual assessment for operators with different levels of training and the additional value of rest perfusion imaging, and to compare visual assessment and automated quantitative analysis in the assessment of coronary artery disease (CAD).

METHODS

We evaluated 53 patients with known or suspected CAD referred for stress-perfusion CMR. Nine operators (equally divided in 3 levels of competency) blindly reviewed each case twice with a 2-week interval, in a randomised order, with and without rest images. Semi-automated Fermi deconvolution was used for quantitative analysis and estimation of myocardial perfusion reserve as the ratio of stress to rest perfusion estimates.

RESULTS

Level-3 operators correctly identified significant CAD in 83.6% of the cases. This percentage dropped to 65.7% for Level-2 operators and to 55.7% for Level-1 operators (p < 0.001). Quantitative analysis correctly identified CAD in 86.3% of the cases and was non-inferior to expert readers (p = 0.56). When rest images were available, a significantly higher level of confidence was reported (p = 0.022), but no significant differences in diagnostic accuracy were measured (p = 0.34).

CONCLUSIONS

Our study demonstrates that the level of training is the main determinant of the diagnostic accuracy in the identification of CAD. Level-3 operators performed at levels comparable with the results from clinical trials. Rest images did not significantly improve diagnostic accuracy, but contributed to higher confidence in the results. Automated quantitative analysis performed similarly to level-3 operators. This is of increasing relevance as recent technical advances in image reconstruction and analysis techniques are likely to permit the clinical translation of robust and fully automated quantitative analysis into routine clinical practice.

Myocardial perfusion quantification using simultaneously acquired 13 NH3 -ammonia PET and dynamic contrast-enhanced MRI in patients at rest and stress

PURPOSE

Systematic differences with respect to myocardial perfusion quantification exist between DCE-MRI and PET. Using the potential of integrated PET/MRI, this study was conceived to compare perfusion quantification on the basis of simultaneously acquired ¹³ NH₃ -ammonia PET and DCE-MRI data in patients at rest and stress.

METHODS

Twenty-nine patients were examined on a 3T PET/MRI scanner. DCE-MRI was implemented in dual-sequence design and additional T₁ mapping for signal normalization. Four different deconvolution methods including a modified version of the Fermi technique were compared against ¹³ NH₃ -ammonia results.

RESULTS

Cohort-average flow comparison yielded higher resting flows for DCE-MRI than for PET and, therefore, significantly lower DCE-MRI perfusion ratios under the common assumption of equal arterial and tissue hematocrit. Absolute flow values were strongly correlated in both slice-average (R²  = 0.82) and regional (R²  = 0.7) evaluations. Different DCE-MRI deconvolution methods yielded similar flow result with exception of an unconstrained Fermi method exhibiting outliers at high flows when compared with PET.

CONCLUSION

Thresholds for Ischemia classification may not be directly tradable between PET and MRI flow values. Differences in perfusion ratios between PET and DCE-MRI may be lifted by using stress/rest-specific hematocrit conversion. Proper physiological constraints are advised in model-constrained deconvolution.

Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance

OBJECTIVES

The authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients.

BACKGROUND

Fully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming.

METHODS

CMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as ≥70% stenosis. Nonsignificant CAD was defined by: 1) QCA as <70% stenosis; or 2) coronary computed tomography angiography as <30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR.

RESULTS

The correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good.

CONCLUSIONS

Fully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170)

Review of Journal of Cardiovascular Magnetic Resonance (JCMR) 2015-2016 and transition of the JCMR office to Boston

The Journal of Cardiovascular Magnetic Resonance (JCMR) is the official publication of the Society for Cardiovascular Magnetic Resonance (SCMR). In 2016, the JCMR published 93 manuscripts, including 80 research papers, 6 reviews, 5 technical notes, 1 protocol, and 1 case report. The number of manuscripts published was similar to 2015 though with a 12% increase in manuscript submissions to an all-time high of 369. This reflects a decrease in the overall acceptance rate to <25% (excluding solicited reviews). The quality of submissions to JCMR continues to be high. The 2016 JCMR Impact Factor (which is published in June 2016 by Thomson Reuters) was steady at 5.601 (vs. 5.71 for 2015; as published in June 2016), which is the second highest impact factor ever recorded for JCMR. The 2016 impact factor means that the JCMR papers that were published in 2014 and 2015 were on-average cited 5.71 times in 2016.In accordance with Open-Access publishing of Biomed Central, the JCMR articles are published on-line in the order that they are accepted with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful to annually summarize the publications into broad areas of interest or themes, so that readers can view areas of interest in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes with previously published JCMR papers to guide continuity of thought in the journal. In addition, I have elected to open this publication with information for the readership regarding the transition of the JCMR editorial office to the Beth Israel Deaconess Medical Center, Boston and the editorial process.Though there is an author publication charge (APC) associated with open-access to cover the publisher's expenses, this format provides a much wider distribution/availability of the author's work and greater manuscript citation. For SCMR members, there is a substantial discount in the APC. I hope that you will continue to send your high quality manuscripts to JCMR for consideration. Importantly, I also ask that you consider referencing recent JCMR publications in your submissions to the JCMR and elsewhere as these contribute to our impact factor. I also thank our dedicated Associate Editors, Guest Editors, and reviewers for their many efforts to ensure that the review process occurs in a timely and responsible manner and that the JCMR continues to be recognized as the leading publication in our field.

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance

OBJECTIVES

This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies.

BACKGROUND

There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis.

METHODS

Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium.

RESULTS

After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model).

CONCLUSIONS

This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and established risk factors, potentially representing an important step forward in the translation of quantitative CMR perfusion analysis to the clinical setting.

Dark-blood late gadolinium enhancement without additional magnetization preparation

BACKGROUND

This study evaluates a novel dark-blood late gadolinium enhancement (LGE) cardiovascular magnetic resonance imaging (CMR) method, without using additional magnetization preparation, and compares it to conventional bright-blood LGE, for the detection of ischaemic myocardial scar. LGE is able to clearly depict myocardial infarction and macroscopic scarring from viable myocardium. However, due to the bright signal of adjacent left ventricular blood, the apparent volume of scar tissue can be significantly reduced, or even completely obscured. In addition, blood pool signal can mimic scar tissue and lead to false positive observations. Simply nulling the blood magnetization by choosing shorter inversion times, leads to a negative viable myocardium signal that appears equally as bright as scar due to the magnitude image reconstruction. However, by combining blood magnetization nulling with the extended grayscale range of phase-sensitive inversion-recovery (PSIR), a darker blood signal can be achieved whilst a dark myocardium and bright scar signal is preserved.

METHODS

LGE was performed in nine male patients (63 ± 11y) using a PSIR pulse sequence, with both conventional viable myocardium nulling and left ventricular blood nulling, in a randomized order. Regions of interest were drawn in the left ventricular blood, viable myocardium, and scar tissue, to assess contrast-to-noise ratios. Maximum scar transmurality, scar size, circumferential scar angle, and a confidence score for scar detection and maximum transmurality were also assessed. Bloch simulations were performed to simulate the magnetization levels of the left ventricular blood, viable myocardium, and scar tissue.

RESULTS

Average scar-to-blood contrast was significantly (p < 0.001) increased by 99% when nulling left ventricular blood instead of viable myocardium, while scar-to-myocardium contrast was maintained. Nulling left ventricular blood also led to significantly (p = 0.038) higher expert confidence in scar detection and maximum transmurality. No significant changes were found in scar transmurality (p = 0.317), normalized scar size (p = 0.054), and circumferential scar angle (p = 0.117).

CONCLUSIONS

Nulling left ventricular blood magnetization for PSIR LGE leads to improved scar-to-blood contrast and increased expert confidence in scar detection and scar transmurality. As no additional magnetization preparation is used, clinical application on current MR systems is readily available without the need for extensive optimizations, software modifications, and/or additional training.

A novel myocardium segmentation approach based on neutrosophic active contour model

BACKGROUND AND OBJECTIVES

Automatic delineation of the myocardium in echocardiography can assist radiologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise. The purpose of this study is to automatically detect myocardium region in left ventricle myocardial contrast echocardiography (LVMCE) images to help radiologists' diagnosis and further measurement on infarction size.

METHODS

The LVMCE image is firstly mapped into neutrosophic similarity (NS) domain using the intensity and homogeneity features. Then, a neutrosophic active contour model (NACM) is proposed and the energy function is defined by the NS values. Finally, the ventricle is detected using the curve evolving results. The ventricle's boundary is identified as the endocardium. To speed up the evolution procedure and increase the detection accuracy, a clustering algorithm is employed to obtain the initial ventricle region. The curve evolution procedure in NACM is utilized again to obtain the epicardium, where the initial contour uses the detected endocardium and the anatomy knowledge on the thickness of the myocardium.

RESULTS

Echocardiographic studies are performed on 10 male Sprague-Dawley rats using a Vivid 7 system including 5 normal cases and 5 rats with myocardial infarction. The myocardium boundaries manually outlined by an experienced radiologist are used as the reference standard for the performance evaluation. Two metrics, Hdist and AvgDist, are employed to evaluate the detection results. The NACM method was compared with those from the eliminated particle swarm optimization (EPSO) and active contour model without edges (ACMWE) methods. The mean and standard deviation of the Hdist and AvgDist on endocardium are 6.83 ± 1.12mm and 0.79 ± 0.28mm using EPSO method, 7.12 ± 0.98mm and 0.82 ± 0.32mm using ACMWE method, and 4.55 ± 0.9mm and 0.58 ± 0.18mm using NACM method, respectively. The improvement on epicardium is much more significant, and two metrics are decreased from 7.45 ± 1.24mm, and 1.47 ± 0.34mm using EPSO method, and 8.21±0.43mm, and 1.73±0.47mm using ACMWE method, to 4.94 ± 0.82mm, and 0.84 ± 0.22mm using NACM method, respectively.

CONCLUSIONS

The proposed method can automatically detect myocardium accurately, and is helpful for clinical therapeutics to measure myocardial perfusion and infarct size.