Olivier Jaubert

The value of exercise cardiovascular magnetic resonance (CMR) has been shown in many clinical scenarios. We have developed a novel MR‐compatible exercise device and aim to validate it against the reference standard MR‐conventional ergometer. The novel device consisted of two half‐pipes fixed to a wooden base, with participants wearing knee‐length socks with a 0.5 kg weight in each sock. Increased workload was achieved by increasing the rate of alternating leg flexion and extension in time with a bleep sound of increasing frequency. Twenty subjects (10 healthy volunteers, 10 patients with pulmonary arterial hypertension) performed two CMR‐augmented cardiopulmonary exercise tests (CMR/CPET) using the novel exercise device and a conventional ergometer in a randomised order. Comparing peak metrics elicited on both exercise devices, there was a moderate correlation in peak oxygen consumption (VO ₂ , r = 0.86, p < 0.001), cardiac output (CO, r = 0.73, p < 0.001), stroke volume (SV, r = 0.90, p < 0.001), peak heart rate (HR, r = 0.65, p = 0.002) and peak arteriovenous oxygen content gradient (ΔavO ₂ , r = 0.71, p < 0.001). However, all metrics were significantly lower with the novel device. Both devices were able to elicit statistically significant differences in VO ₂ , HR and right ventricular ejection fraction (RVEF) between patients and healthy subjects ( p ≤ 0.04). We have created a simple, easy to use and affordable exercise device for the CMR environment. This may encourage greater dissemination of exercise CMR in clinical and research practice.

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.

PURPOSE: Interactive cardiac magnetic resonance imaging 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 to 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 NRMSE, pSNR, SSIM and LAPE were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (SSIM: 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 to radial and uniform spiral trajectories.

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.

Purpose: Real-time monitoring of cardiac output (CO) requires low latency reconstruction and segmentation of real-time phase contrast MR (PCMR), which has previously been difficult to perform. Here we propose a deep learning framework for 'Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring' (FReSCO). Methods: Deep artifact suppression and segmentation U-Nets were independently trained. Breath hold spiral PCMR data (n=516) was 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 PCMR 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. CO obtained via FReSCO was compared to a reference rest CO and rest and exercise Compressed Sensing (CS) CO. Results: FReSCO was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume and CO could be visualized with a mean latency of 622ms. No significant differences were noted when compared to reference at rest (Bias = -0.21+-0.50 L/min, p=0.246) or CS at peak exercise (Bias=0.12+-0.48 L/min, p=0.458). Conclusion: FReSCO 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.

Purpose: Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan.Methods: Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques.Results: The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters.Conclusion: The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.

Purpose: To develop a novel simultaneous co‐registered T₁, T₂, , T_1ρ, and fat fraction abdominal MR fingerprinting (MRF) approach for fully comprehensive liver‐tissue characterization in a single breath‐hold scan.Methods: A gradient‐echo liver MRF sequence with low fixed flip angle, multi‐echo radial readout, and varying magnetization preparation pulses for multiparametric encoding is performed at 1.5 T. The and fat fraction are estimated from a graph/cut water/fat separation method using a six‐peak fat model. Water/fat singular images obtained are then matched to an MRF dictionary, estimating water‐specific T₁, T₂, and T_1ρ. The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional sequences.Results: For the phantom studies, linear fits show excellent coefficients of determination (r² > 0.9) for every parametric map. For in vivo studies, the average values measured within regions of interest drawn on liver, spleen, muscle, and fat are statistically different from the reference scans (p < 0.05) for T₁, T₂, and T_1⍴ but not for and fat fraction, whereas correlation between MRF and reference scans is excellent for each parameter (r² > 0.92 for every parameter).Conclusion: The proposed multi‐echo inversion‐recovery, T₂, and T_1⍴ prepared liver MRF sequence presented in this work allows for quantitative T₁, T₂, , T_1⍴, and fat fraction liver‐tissue characterization in a single breath‐hold scan of 18 seconds. The approach showed good agreement and correlation with respect to reference clinical maps.

Purpose: Develop a novel low‐rank motion‐corrected (LRMC) reconstruction for nonrigid motion‐corrected MR fingerprinting (MRF).Methods: Generalized motion‐corrected (MC) reconstructions have been developed for steady‐state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low‐rank dictionary‐based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T₁ and T₂) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free‐breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram‐triggered/breath‐hold MOLLI and T₂ gradient‐and–spin echo conventional maps (in vivo 2D and 3D myocardial MRF).Results: Phantom results were in general agreement with reference spin‐echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T₁ and short T₂ (1 values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T₂ values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T₂ gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T₁, and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T₂. For 3D liver, LRMC MRF measured liver T₁ at 565 ± 44 ms and liver T₂ at 35.4 ± 2.4 ms.Conclusion: The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.

Purpose: Real‐time low latency MRI is performed to guide various cardiac interventions. Real‐time acquisitions often require iterative image reconstruction strategies, which lead to long reconstruction times. In this study, we aim to reconstruct highly undersampled radial real‐time data with low latency using deep learning.Methods: A 2D U‐Net with convolutional long short‐term memory layers is proposed to exploit spatial and preceding temporal information to reconstruct highly accelerated tiny golden radial data with low latency. The network was trained using a dataset of breath‐hold CINE data (including 770 time series from 7 different orientations). Synthetic paired data were created by retrospectively undersampling the magnitude images, and the network was trained to recover the target images. In the spirit of interventional imaging, the network was trained and tested for varying acceleration rates and orientations. Data were prospectively acquired and reconstructed in real time in 1 healthy subject interactively and in 3 patients who underwent catheterization. Images were visually compared to sliding window and compressed sensing reconstructions and a conventional Cartesian real‐time sequence.Results: The proposed network generalized well to different acceleration rates and unseen orientations for all considered metrics in simulated data (less than 4% reduction in structural similarity index compared to similar acceleration and orientation‐specific networks).The proposed reconstruction was demonstrated interactively, successfully depicting catheters in vivo with low latency (39 ms, including 19 ms for deep artifact suppression) and an image quality comparing favorably to other reconstructions.Conclusion: Deep artifact suppression was successfully demonstrated in the time‐critical application of non‐Cartesian real‐time interventional cardiac MR.