Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI

Weine, Jonathan; Gorkum, Robbert J. H. van; Stoeck, Christian T.; Vishnevskiy, Valery; Kozerke, Sebastian

doi:10.1016/j.compmedimag.2022.102075

Cited by 4 publications

(5 citation statements)

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“…The trajectory of the refined mesh nodes for all snapshots between 150 and 350 ms is captured by the PODTrajectory module. To evaluate the diffusion weighting operator, spatially coherent random diffusion tensors were assigned to all LV mesh nodes according to the sampling procedure described in Weine et al 22 A slab of 30‐mm thickness was extracted from the LV as well as the background phantom surrounding the target FOV with a margin of 20 mm. Breathing motion was incorporated by assuming a periodic global translation of the slab and the LV using the SimpleBreathingMotion module.…”

Section: Methodsmentioning

confidence: 99%

“…Simulation frameworks may target specific applications with assumptions on physiology, anatomy, and MR physics, 2–8 or may take a more general approach, seeking to accommodate a range of possible applications 9–18 . Additionally, the widespread deployment of machine learning algorithms makes simulations increasingly relevant, such as to optimize sampling strategies and/or sequences, 19–21 to facilitate access to simulated ground‐truth information for postprocessing algorithms, and to train and test models on synthetic data 22,23 …”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11][12][13][14][15][16][17][18] Additionally, the widespread deployment of machine learning algorithms makes simulations increasingly relevant, such as to optimize sampling strategies and/or sequences, [19][20][21] to facilitate access to simulated ground-truth information for postprocessing algorithms, and to train and test models on synthetic data. 22,23 Requirements such as simulation time and availability of computational resources play a major role in determining what level of simulation detail can be considered. The most general approach, which has previously been implemented in multiple available software projects, [9][10][11][12][13][14][15][16][17][18] is based on the numerical solution of the Bloch equations for a selected sequence (defining gradient and RF waveforms as well as sampling events).…”

Section: Introductionmentioning

confidence: 99%

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CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Weine,

McGrath,

Dirix

et al. 2024

Magnetic Resonance in Med

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PurposeTo present an open‐source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction.MethodsCMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase‐contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided.ResultsThe Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk‐motion phase sensitivity took approximately 10 s per diffusion‐weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain.ConclusionCMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open‐source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Weine,

McGrath,

Dirix

et al. 2024

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

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“…Even Weine et al [ 51 ] attempted to improve the acquisition of cardiac diffusion tensor imaging (cDTI), which can provide information about myocardial microstructure. They hypothesized that the robustness of diffusion tensor estimation can be improved by incorporating spatial information and physiologically plausible priors into the fitting algorithm.…”

Section: Ai In Cardiac Magnetic Resonancementioning

confidence: 99%

Artificial Intelligence in Cardiovascular CT and MR Imaging

Lanzafame

Bucolo

Muscogiuri

et al. 2023

Life

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The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists’ workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

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“…In recent years, a few studies have been performed in which synthetic data has been generated for the training of neural networks which are then used on clinical data. For example, synthetic images were generated to train neural networks to track cardiac motion and calculate cardiac strain ( Loecher et al, 2021 ), estimate tensors from free-breathing cardiac diffusion tensor imaging ( Weine et al, 2022 ), and predict end-diastole volume, end-systole volume, and ejection fraction ( Gheorghita et al, 2022 ). Furthermore, synthetic photoplethysmography (PPG) signals were generated to detect bradycardia and tachycardia ( Sološenko et al, 2022 ), and synthetic electrocardiogram (ECG) signals were generated to detect r-waves during different physical activities and atrial fibrillation ( Kaisti et al, 2023 ), and to predict the ventricular origin in outflow tract ventricular arrhythmias ( Doste et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Plappert,

Engström,

Platonov

et al. 2024

Front. Physiol.

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Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay.Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis.Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation.Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, aresp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of aresp.

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Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI

Cited by 4 publications

References 30 publications

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Artificial Intelligence in Cardiovascular CT and MR Imaging

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

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