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

Auger, Daniel A.; Ghadimi, Sona; Cai, Xiaoying; Reagan, Claire E; Sun, Changyu; Abdi, Mohamad; Cao, Jie J; Cheng, Joshua; Ngai, Nora; Scott, Andrew D.; Ferreira, Pedro; Oshinski, John N.; Emamifar, Nick; Ennis, Daniel B.; Loecher, Michael; Liu, Zhan-Qiu; Croisille, Pierre; Viallon, Magalie; Bilchick, Kenneth C.; Epstein, Frederick H.

doi:10.1186/s12968-022-00851-7

Cited by 17 publications

(22 citation statements)

References 39 publications

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“…The greatest existing hurdle to widespread adoption of strain CMR is repeatability. Although high reproducibility of global strain measurements has been demonstrated for different types of strain CMR approaches ( 74 – 76 ), reproducibility for more difficult segmental strain analysis is just emerging ( 77 ). If vendors can come to a consensus and provide standardized, reproducible strain CMR platforms, multiple diagnostic opportunities will emerge.…”

Section: Discussionmentioning

confidence: 99%

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

Cheng

2022

Front. Cardiovasc. Med.

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Recent advances in cardiac MRI (CMR) capabilities have truly transformed its potential for deep phenotyping of the diseased heart. Long known for its unparalleled soft tissue contrast and excellent depiction of three-dimensional (3D) structure, CMR now boasts a range of unique capabilities for probing disease at the tissue and molecular level. We can look beyond coronary vessel blockages and detect vessel disease not visible on a structural level. We can assess if early fibrotic tissue is being laid down in between viable cardiac muscle cells. We can measure deformation of the heart wall to determine early presentation of stiffening. We can even assess how cardiomyocytes are utilizing energy, where abnormalities are often precursors to overt structural and functional deficits. Finally, with artificial intelligence gaining traction due to the high computing power available today, deep learning has proven itself a viable contender with traditional acceleration techniques for real-time CMR. In this review, we will survey five key emerging MRI techniques that have the potential to transform the CMR clinic and permit early detection and intervention. The emerging areas are: (1) imaging microvascular dysfunction, (2) imaging fibrosis, (3) imaging strain, (4) imaging early metabolic changes, and (5) deep learning for acceleration. Through a concerted effort to develop and translate these areas into the CMR clinic, we are committing ourselves to actualizing early diagnostics for the most intractable heart disease phenotypes.

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Section: Discussionmentioning

confidence: 99%

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

Cheng

2022

Front. Cardiovasc. Med.

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“…There are two types of respiratory motion: (1) that which changes the heart's position before displacement encoding, Δr R 1 , and that which occurs between displacement encoding (DE) and readout (RO), Δr R 2 . Δr R 1 leads to a position shift of the magnetization amplitude, whereas Δr R 2 gets encoded into the phase of the stimulated echo in addition to leading to a position shift of the magnetization amplitude By invoking Fourier transform properties applied to Equation (2), Equation (3) shows that respiratory motion leads to linear and constant phase errors in the k-space domain given by…”

Section: Theorymentioning

confidence: 99%

“…The simulated motion-corrupted data were corrected for linear and constant phase errors (following Equations [3] and [4]) using the known motion, and the resulting k-space data were reconstructed using the nonuniform fast Fourier transform. 18 The motion-corrupted and motion-compensated images were compared with reference images without motion.…”

Section: Bloch-equation Dense Simulations Incorporating Cardiac and R...mentioning

confidence: 99%

“…A spiral cine DENSE sequence 16 with prospective cardiac gating was used for breath-hold scans using a previously described protocol. 3 Four spiral interleaves per image were acquired corresponding to full sampling of k-space, where two interleaves were acquired during each heartbeat. The uniform-density spirals used 𝜋∕2 uniform rotations, and each interleaf performed 2.5 turns to reach the required extent of k-space.…”

Section: Acquisition Of Training Data For the Convolutional Neural Ne...mentioning

confidence: 99%

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

Abdi

Bilchick

Epstein

2023

Magnetic Resonance in Med

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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.

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“…The heart failure data came from patient studies supported by the American Heart Association, "Personalized Approach to Cardiac Resynchronization Therapy Using High Dimensional Immunophenotyping, " as well as the NIH/NHLBI, "MRI of Mechanical Activation and Scar for Optimal Cardiac Resynchronization Therapy Implementation. " The primary researchers for these studies have reported their results and described their data in the literature [36][37][38].…”

Section: Datamentioning

confidence: 99%

Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data

et al. 2022

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Background Cardiopulmonary exercise testing (CPET) provides a reliable and reproducible approach to measuring fitness in patients and diagnosing their health problems. However, the data from CPET consist of multiple time series that require training to interpret. Part of this training teaches the use of flow charts or nested decision trees to interpret the CPET results. This paper investigates the use of two machine learning techniques using neural networks to predict patient health conditions with CPET data in contrast to flow charts. The data for this investigation comes from a small sample of patients with known health problems and who had CPET results. The small size of the sample data also allows us to investigate the use and performance of deep learning neural networks on health care problems with limited amounts of labeled training and testing data. Methods This paper compares the current standard for interpreting and classifying CPET data, flowcharts, to neural network techniques, autoencoders and convolutional neural networks (CNN). The study also investigated the performance of principal component analysis (PCA) with logistic regression to provide an additional baseline of comparison to the neural network techniques. Results The patients in the sample had two primary diagnoses: heart failure and metabolic syndrome. All model-based testing was done with 5-fold cross-validation and metrics of precision, recall, F1 score, and accuracy. As a baseline for comparison to our models, the highest performing flow chart method achieved an accuracy of 77%. Both PCA regression and CNN achieved an average accuracy of 90% and outperformed the flow chart methods on all metrics. The autoencoder with logistic regression performed the best on each of the metrics and had an average accuracy of 94%. Conclusions This study suggests that machine learning and neural network techniques, in particular, can provide higher levels of accuracy with CPET data than traditional flowchart methods. Further, the CNN performed well with a small data set showing that these techniques can be designed to perform well on small data problems that are often found in health care and the life sciences. Further testing with larger data sets is needed to continue evaluating the use of machine learning to interpret CPET data.

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

Cited by 17 publications

References 39 publications

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

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

Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data

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