Training deep learning based dynamic MR image reconstruction using open-source natural videos

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination

PURPOSE

Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression).

METHODS

A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05).

RESULTS

In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging.

CONCLUSION

HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

Automatic segmentation of the great arteries for computational hemodynamic assessment

BACKGROUND

Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies.

METHODS

90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors.

RESULTS

The network's Dice score (ML vs GT) was 0.945 (interquartile range: 0.929-0.955) for the aorta and 0.885 (0.851-0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5-15.7%) and 4.1% (3.1-6.9%), respectively, and for the pulmonary arteries 14.6% (11.5-23.2%) and 6.3% (4.3-7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2).

CONCLUSIONS

ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation

PURPOSE

Real-time monitoring of cardiac output (CO) requires low-latency reconstruction and segmentation of real-time phase-contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for "FReSCO" (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring).

METHODS

Deep artifact suppression and segmentation U-Nets were independently trained. Breath-hold spiral phase-contrast MR data (N = 516) were synthetically undersampled using a variable-density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U-net. A subset of the data (N = 96) was segmented and used to train the segmentation U-net. Real-time spiral phase-contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed-sensing CO.

RESULTS

The FReSCO framework was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = -0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458).

CONCLUSIONS

The FReSCO framework was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.

Real-Time Magnetic Resonance Imaging

Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

Deep artifact suppression for spiral real-time phase contrast cardiac magnetic resonance imaging in congenital heart disease

PURPOSE

Real-time spiral phase contrast MR (PCMR) enables rapid free-breathing assessment of flow. Target spatial and temporal resolutions require high acceleration rates often leading to long reconstruction times. Here we propose a deep artifact suppression framework for fast and accurate flow quantification.

METHODS

U-Nets were trained for deep artifact suppression using 520 breath-hold gated spiral PCMR aortic datasets collected in congenital heart disease patients. Two spiral trajectories (uniform and perturbed) and two losses (Mean Absolute Error -MAE- and average structural similarity index measurement -SSIM-) were compared in synthetic data in terms of MAE, peak SNR (PSNR) and SSIM. Perturbed spiral PCMR was prospectively acquired in 20 patients. Stroke Volume (SV), peak mean velocity and edge sharpness measurements were compared to Compressed Sensing (CS) and Cartesian reference.

RESULTS

In synthetic data, perturbed spiral consistently outperformed uniform spiral for the different image metrics. U-Net MAE showed better MAE and PSNR while U-Net SSIM showed higher SSIM based metrics. In-vivo, there were no significant differences in SV between any of the real-time reconstructions and the reference standard Cartesian data. However, U-Net SSIM had better image sharpness and lower biases for peak velocity when compared to U-Net MAE. Reconstruction of 96 frames took ~59 s for CS and 3.9 s for U-Nets.

CONCLUSION

Deep artifact suppression of complex valued images using an SSIM based loss was successfully demonstrated in a cohort of congenital heart disease patients for fast and accurate flow quantification.

Comprehensive mechanical & metabolic imaging of abdominal aortic aneurysm with 4D flow/ FDG PET on an integrated PETMRI: a feasibility study

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): NIHR Biomedical Research Centre, University College London Hospitals.

Background

A number of non-invasive imaging derived parameters have been implicated in the development and progression of abdominal aortic aneurysm, although the mechanism, and relationships of many of these are yet to be precisely determined.  Mechanical parameters can now be studied using 4D phase contrast magnetic resonance (PCMR), and inflammatory cellular activity can be detected with FDG PET.

Purpose

It may be postulated that inflammation of the aortic wall may be the intermediary at the tissue level linking mechanical wall shear stress (WSS) to aneurysm progression. It may be feasible to study 4D PCMR and FDG PET at the same patient visit on a PETMRI platform, with the potential to enhance temporal and spatial co-registration and improving the understanding of any relationship between these two parameters.  Our study aims to assess feasibility of studying these on an integrated PETMRI system.

Methods

7 patients with known aortic aneurysm were recruited in a vascular ultrasound screening follow up clinic.  During a single visit following 6 hours fasting, all patients underwent FDG injection and 60 minutes uptake period.  With quiet breathing, list mode PET acquisition and concurrent 4D PCMR was acquired using stacks of spiral acquisition, with ECG trace information for retrospective gating.  Images from the 4D PCMR and FDG PET were assessed qualitatively for image quality and visual matching.

Results

All 7 patients completed the study.  Overall image quality was adequate to good.  There is qualitatively a good concordance with impression of positive correlation between wall shear stress and inflammatory signal (see attached image).

Conclusion

We have demonstrated feasibility of combined assessment of mechanical and metabolic imaging parameters using an integrated PETMRI system.  Initial findings show there to be a broad concordance of wall shear stress and inflammatory signal in the abdominal aneurysm.

Rapid 3D whole-heart cine imaging using golden ratio stack of spirals

Three-dimensional cine imaging provides a wealth of information about cardiac anatomy and function, but its use in the clinical environment is limited because data acquisition is very time consuming. In this work, a free-breathing 3D whole-heart cine imaging framework was developed using a time-efficient stack of spirals trajectory and accelerated reconstruction. Two suitable view ordering methods are considered with different spacing between k-space readouts in the partition dimension: uniform and tiny golden ratio based. A simulation study suggested the latter did not present any benefits in terms of similarity to the true image. The proposed method was subsequently tested on 10 prospective subjects and compared with conventional multi-slice breath-hold imaging. Image quality was evaluated using objective and subjective scores and ventricular measurements were compared to assess clinical accuracy. Image quality was lower in the proposed technique than in breath-hold images but good agreement was found in clinically relevant ventricular measurements. In addition, the proposed method was fast to acquire, required minimal planning and provided full anatomical coverage with isotropic resolution.