Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping

Deep learning for accelerated and robust MRI reconstruction

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

The role of artificial intelligence in cardiovascular magnetic resonance imaging

Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation

BACKGROUND

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

METHODS

Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis.

RESULTS

These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.

CONCLUSIONS

Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.

Correction of phase offset errors and quantification of background noise, signal-to-noise ratio, and encoded-displacement uncertainty on DENSE MRI for kinematics of the descending thoracic and abdominal aorta

Displacement encoding with stimulated echoes (DENSE) MRI is a phase contrast technique that allows the encoding of tissue displacement into the phase of the magnetic resonance signal. Recent developments in this technique allow the imaging of relatively thin structures such as the aortic wall. Quantifying background noise associated to DENSE MRI is required to assess the uncertainty of derived displacement measurements and for the design and implementation of adequate noise-reduction techniques. Although noise and error management of cardiac DENSE MRI has been previously studied, developments for aortic applications are scarce. Herein, we evaluate the noise and uncertainty of DENSE MRI scans at three different locations along the descending aorta: the distal aortic arch (DAA), the descending thoracic aorta (DTA), and infrarenal abdominal aorta (IAA). Additionally, we analyze three datasets from in vitro validation experiments with polyvinyl alcohol phantoms. We implement and evaluate the effectiveness of an offset-error correction algorithm and noise filtering techniques on DENSE MRI for aortic motion applications. Our results show that the phase signal of pixels composing the static background was normally distributed, centered on average at 0.003 ± 0.02 rad and - 0.02 ± 0.024 rad for each phase directions, suggesting that background noise is random, isotropic, and DENSE MRI has little offset errors. However, background signal noise significantly increased with elapsed time of the cardiac cycle; and was spatially heterogeneous consistently increased towards the anterior space. Background noise showed no significant differences between the 3 aortic locations and the in vitro experiments. However, SNR depended on the displacement of the region of interest, in consequence it was found significantly larger at DAA (16.7 ± 8.5, p = 0.003) and DTA (15.4 ± 7.6, p = 0.008) than at the IAA (8.0 ± 4.1), but not significantly different than the SNR of in vitro experiments (8.0 ± 3.7), and had an overall average of 13 ± 7. The applied methods significantly reduced the offset error and effect of noise on the estimation of encoded displacements. Finally, this analysis suggests that the implemented DENSE MRI protocol is adequate to assess the motion of healthy human aortas. However, the relative effect of noise increased considerably on the analysis of an ageing or diseased aortas with impaired mobility, calling for further analyses on pathologically stiffened aortas.

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data

OBJECTIVE

The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction.

METHODS

A total of 401 participants (311 patients and 90 age-matched volunteers) were recruited for this prospective study. The two-dimensional UNet segmentation model of the LV and classification model for identifying paradoxical pulsation were established using the DCNN model. Features of 2- and 3-chamber images were extracted with 2-dimensional (2D) and 3D ResNets with masks generated by a segmentation model. Next, the accuracy of the segmentation model was evaluated using the Dice score and classification model by receiver operating characteristic (ROC) curve and confusion matrix. The areas under the ROC curve (AUCs) of the physicians in training and DCNN models were compared using the DeLong method.

RESULTS

The DCNN model showed that the AUCs for the detection of paradoxical pulsation were 0.97, 0.91, and 0.83 in the training, internal, and external testing cohorts, respectively (p < 0.001). The 2.5-dimensional model established using the end-systolic and end-diastolic images combined with 2-chamber and 3-chamber images was more efficient than the 3D model. The discrimination performance of the DCNN model was better than that of physicians in training (p < 0.05).

CONCLUSIONS

Compared to the model trained by 2-chamber or 3-chamber images alone or 3D multiview, our 2.5D multiview model can combine the information of 2-chamber and 3-chamber more efficiently and obtain the highest diagnostic sensitivity.

CLINICAL RELEVANCE STATEMENT

A deep convolutional neural network model that integrates 2-chamber and 3-chamber CMR images can identify LV paradoxical pulsation which correlates with LV thrombosis, heart failure, ventricular tachycardia after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction.

KEY POINTS

• The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.

Measuring Cardiac Dyssynchrony with DENSE (Displacement Encoding with Stimulated Echoes)-A Systematic Review

Background

In this review, we introduce the displacement encoding with stimulated echoes (DENSE) method for measuring myocardial dyssynchrony using cardiovascular magnetic resonance (CMR) imaging. We provide an overview of research findings related to DENSE from the past two decades and discuss other techniques used for dyssynchrony evaluation. Additionally, the review discusses the potential uses of DENSE in clinical practice.

Methods

A search was conducted to identify relevant articles published from January 2000 through January 2023 using the Scopus, Web of Science, PubMed and Cochrane databases. The following search term was used: (DENSE OR 'displacement encoding with stimulated echoes' OR CURE) AND (dyssynchrony* OR asynchron* OR synchron*) AND (MRI OR 'magnetic resonance' OR CMR).

Results

After removing duplicates, researchers screened a total of 174 papers. Papers that were not related to the topic, reviews, general overview articles and case reports were excluded, leaving 35 articles for further analysis. Of these, 14 studies focused on cardiac dyssynchrony estimation with DENSE, while the remaining 21 studies served as background material. The studies used various methods for presenting synchronicity, such as circumferential uniformity ratio estimate (CURE), CURE-singular value decomposition (SVD), radial uniformity ratio estimate (RURE), longitudinal uniformity ratio estimate (LURE), time to onset of shortening (TOS) and dyssynchrony index (DI). Most of the dyssynchrony studies concentrated on human heart failure, but congenital heart diseases and obesity were also evaluated. The researchers found that DENSE demonstrated high reproducibility and was found useful for detecting cardiac resynchronisation therapy (CRT) responders, optimising CRT device settings and assessing right ventricle synchronicity. In addition, studies showed a correlation between cardiac fibrosis and mechanical dyssynchrony in humans, as well as a decrease in the synchrony of contraction in the left ventricle in obese mice.

Conclusions

DENSE shows promise as a tool for quantifying myocardial function and dyssynchrony, with advantages over other cardiac dyssynchrony evaluation methods. However, there remain challenges related to DENSE due to the relatively time-consuming imaging and analysis process. Improvements in imaging and analysing technology, as well as possible artificial intelligence solutions, may help overcome these challenges and lead to more widespread clinical use of DENSE.

A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root

Recent clinical studies have suggested that introducing 3D patient-specific aortic root models into the pre-operative assessment procedure of transcatheter aortic valve replacement (TAVR) would reduce the incident rate of peri-operative complications. Tradition manual segmentation is labor-intensive and low-efficient, which cannot meet the clinical demands of processing large data volumes. Recent developments in machine learning provided a viable way for accurate and efficient medical image segmentation for 3D patient-specific models automatically. This study quantitively evaluated the auto segmentation quality and efficiency of the four popular segmentation-dedicated three-dimensional (3D) convolutional neural network (CNN) architectures, including 3D UNet, VNet, 3D Res-UNet and SegResNet. All the CNNs were implemented in PyTorch platform, and low-dose CTA image sets of 98 anonymized patients were retrospectively selected from the database for training and testing of the CNNs. The results showed that despite all four 3D CNNs having similar recall, Dice similarity coefficient (DSC), and Jaccard index on the segmentation of the aortic root, the Hausdorff distance (HD) of the segmentation results from 3D Res-UNet is 8.56 ± 2.28, which is only 9.8% higher than that of VNet, but 25.5% and 86.4% lower than that of 3D UNet and SegResNet, respectively. In addition, 3D Res-UNet and VNet also performed better in the 3D deviation location of interest analysis focusing on the aortic valve and the bottom of the aortic root. Although 3D Res-UNet and VNet are evenly matched in the aspect of classical segmentation quality evaluation metrics and 3D deviation location of interest analysis, 3D Res-UNet is the most efficient CNN architecture with an average segmentation time of 0.10 ± 0.04 s, which is 91.2%, 95.3% and 64.3% faster than 3D UNet, VNet and SegResNet, respectively. The results from this study suggested that 3D Res-UNet is a suitable candidate for accurate and fast automatic aortic root segmentation for pre-operative assessment of TAVR.

Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI

Purpose

To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.

Materials and Methods

In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.

Results

Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.

Conclusion

The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE

Purpose

To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.

Materials and Methods

In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.

Results

The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.

Conclusion

StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning

PURPOSE

To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order respiratory motion compensation for self-navigated free-breathing cine DENSE of the heart.

METHODS

The motion model includes conventional position shifts of magnetization and further describes the phase shift of the stimulated echo due to breathing. These image-domain effects correspond to linear and constant phase errors, respectively, in k-space. The model was validated using phantom experiments and Bloch-equation simulations and was used along with the simulation of respiratory motion to generate synthetic images with phase-shift artifacts to train a U-Net, DENSE-RESP-NET, to perform motion correction. DENSE-RESP-NET-corrected self-navigated free-breathing DENSE was evaluated in human subjects through comparisons with signal averaging, uncorrected self-navigated free-breathing DENSE, and breath-hold DENSE.

RESULTS

Phantom experiments and Bloch-equation simulations showed that breathing-induced constant phase errors in segmented DENSE leads to signal loss in magnitude images and phase corruption in phase images of the stimulated echo, and that these artifacts can be corrected using the known respiratory motion and the model. For self-navigated free-breathing DENSE where the respiratory motion is not known, DENSE-RESP-NET corrected the signal loss and phase-corruption artifacts and provided reliable strain measurements for systolic and diastolic parameters.

CONCLUSION

DENSE-RESP-NET is an effective method to correct for breathing-associated constant phase errors. DENSE-RESP-NET used in concert with self-navigation methods provides reliable free-breathing DENSE myocardial strain measurement.

Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks

BACKGROUND

Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images.

METHODS

2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods.

RESULTS

Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies.

CONCLUSION

Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

Improved computation of Lagrangian tissue displacement and strain for cine DENSE MRI using a regularized spatiotemporal least squares method

Introduction

In displacement encoding with stimulated echoes (DENSE), tissue displacement is encoded in the signal phase such that the phase of each pixel in space and time provides an independent measurement of absolute tissue displacement. Previously for DENSE, estimation of Lagrangian displacement used two steps: first a spatial interpolation and, second, least squares fitting through time to a Fourier or polynomial model. However, there is no strong rationale for such a through-time model,

Methods

To compute the Lagrangian displacement field from DENSE phase data, a minimization problem is introduced to enforce fidelity with the acquired Eulerian displacement data while simultaneously providing model-independent regularization in space and time, enforcing only spatiotemporal smoothness. A regularized spatiotemporal least squares (RSTLS) method is used to solve the minimization problem, and RSTLS was tested using two-dimensional DENSE data from 71 healthy volunteers.

Results

The mean absolute percent error (MAPE) between the Lagrangian displacements and the corresponding Eulerian displacements was significantly lower for the RSTLS method vs. the two-step method for both x- and y-directions (0.73±0.59 vs 0.83 ±0.1, p &lt; 0.05) and (0.75±0.66 vs 0.82 ±0.1, p &lt; 0.05), respectively. Also, peak early diastolic strain rate (PEDSR) was higher (1.81±0.58 (s-1) vs. 1.56±0. 63 (s-1), p&lt;0.05) and the strain rate during diastasis was lower (0.14±0.18 (s-1) vs 0.35±0.2 (s-1), p &lt; 0.05) for the RSTLS vs. the two-step method, with the former suggesting that the two-step method was over-regularized.

Discussion

The proposed RSTLS method provides more realistic measurements of Lagrangian displacement and strain from DENSE images without imposing arbitrary motion models.

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

BACKGROUND AND OBJECTIVES

Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.

METHODS

DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.

RESULTS

The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction.

CONCLUSIONS

Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.

Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis

BACKGROUND

Myocardial feature tracking (FT) provides a comprehensive analysis of myocardial deformation from cine balanced steady-state free-precession images (bSSFP). However, FT remains time-consuming, precluding its clinical adoption.

PURPOSE

To compare left-ventricular global radial strain (GRS) and global circumferential strain (GCS) values measured using automated DeepStrain analysis of short-axis cine images to those calculated using manual commercially available FT analysis.

STUDY TYPE

Retrospective, single-center.

POPULATION

A total of 30 healthy subjects and 120 patients with cardiac disease for DeepStrain development. For evaluation, 47 healthy subjects (36 male, 53 ± 5 years) and 533 patients who had undergone a clinical cardiac MRI (373 male, 59 ± 14 years). FIELD STRENGTH/SEQUENCE: bSSFP sequence at 1.5 T (Phillips) and 3 T (Siemens).

ASSESSMENT

Automated DeepStrain measurements of GRS and GCS were compared to commercially available FT (Circle, cvi42) measures obtained by readers with 1 year and 3 years of experience. Comparisons were performed overall and stratified by scanner manufacturer.

STATISTICAL TESTS

Paired t-test, linear regression slope, Pearson correlation coefficient (r).

RESULTS

Overall, FT and DeepStrain measurements of GCS were not significantly different (P = 0.207), but measures of GRS were significantly different. Measurements of GRS from Philips (slope = 1.06 [1.03 1.08], r = 0.85) and Siemens (slope = 1.04 [0.99 1.09], r = 0.83) data showed a very strong correlation and agreement between techniques. Measurements of GCS from Philips (slope = 0.98 [0.98 1.01], r = 0.91) and Siemens (slope = 1.0 [0.96 1.03], r = 0.88) data similarly showed a very strong correlation. The average analysis time per subject was 4.1 ± 1.2 minutes for FT and 34.7 ± 3.3 seconds for DeepStrain, representing a 7-fold reduction in analysis time.

DATA CONCLUSION

This study demonstrated high correlation of myocardial GCS and GRS measurements between freely available fully automated DeepStrain and commercially available manual FT software, with substantial time-saving in the analysis.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease

BACKGROUND

While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (E_cc), longitudinal (E_ll) and radial (E_rr) strain, torsion, and segmental E_cc at multiple centers.

METHODS

Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global E_cc, E_ll and E_rr, torsion, and segmental E_cc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4.

RESULTS

Based on CV and ICC, segmental E_cc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice E_cc and global E_ll provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice E_rr, CV was in the fair-good range, and ICC was in the good-excellent range.

CONCLUSIONS

Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental E_cc, global E_ll, and torsion measurements in healthy subjects and heart disease patients.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease

BACKGROUND

While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers.

METHODS

Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4.

RESULTS

Based on CV and ICC, segmental Ecc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice Ecc and global Ell provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice Err, CV was in the fair-good range, and ICC was in the good-excellent range.

CONCLUSIONS

Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental Ecc, global Ell, and torsion measurements in healthy subjects and heart disease patients.

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid-structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.

Suppression of artifact-generating echoes in cine DENSE using deep learning

PURPOSE

To use deep learning for suppression of the artifact-generating T1 -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.

METHODS

A U-Net was trained to suppress the artifact-generating T1 -relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T1 -relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain.

RESULTS

The DAS-Net method effectively suppressed the T1 -relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE.

CONCLUSION

The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T1 -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60, and 61-80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were - 0.3 ± 10.1 mL for EDV, - 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and - 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, - 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.