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Aims Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. Methods and results A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R 2 and more significant P- values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES). Conclusion Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline. [ABSTRACT FROM AUTHOR]

Purpose: MRI‐guidance of cardiac catheterization is currently performed using one or multiple 2D imaging planes, which may be suboptimal for catheter navigation, especially in patients with complex anatomies. The purpose of the work was to develop a robust real‐time 3D catheter tracking method and 3D visualization strategy for improved MRI‐guidance of cardiac catheterization procedures. Methods: A fast 3D tracking technique was developed using continuous acquisition of two orthogonal 2D‐projection images. Each projection corresponds to a gradient echo stack of slices with only the central k‐space lines being collected for each slice. To enhance catheter contrast, a saturation pulse is added ahead of the projection pair. An offline image processing algorithm was developed to identify the 2D coordinates of the balloon in each projection image and to estimate its corresponding 3D coordinates. Post‐processing includes background signal suppression using an atlas of background 2D‐projection images. 3D visualization of the catheter and anatomy is proposed using three live sagittal, coronal, and axial (MPR) views and 3D rendering. The technique was tested in a subset of a catheterization step in three patients undergoing MRI‐guided cardiac catheterization using a passive balloon catheter. Results: The extraction of the catheter balloon 3D coordinates was successful in all patients and for the majority of time‐points (accuracy >96%). This tracking method enabled a novel 3D visualization strategy for passive balloon catheter, providing enhanced anatomical context during catheter navigation. Conclusion: The proposed tracking strategy shows promise for robust tracking of passive balloon catheter and may enable enhanced visualization during MRI‐guided cardiac catheterization. [ABSTRACT FROM AUTHOR]

Purpose: To develop a framework for simultaneous three‐dimensional (3D) mapping of T1$$ {\mathrm{T}}_1 $$, T2$$ {\mathrm{T}}_2 $$, and fat signal fraction in the liver at 0.55 T. Methods: The proposed sequence acquires four interleaved 3D volumes with a two‐echo Dixon readout. T1$$ {\mathrm{T}}_1 $$ and T2$$ {\mathrm{T}}_2 $$ are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation of T1$$ {\mathrm{T}}_1 $$, T2$$ {\mathrm{T}}_2 $$, and M0$$ {M}_0 $$ for water and fat separately. 2D image navigators permit respiratory binning, and motion fields from nonrigid registration between bins are used in a nonrigid respiratory‐motion‐corrected reconstruction, enabling 100% scan efficiency from a free‐breathing acquisition. The integrated nature of the framework ensures the resulting maps are always co‐registered. Results: T1$$ {\mathrm{T}}_1 $$, T2$$ {\mathrm{T}}_2 $$, and fat‐signal‐fraction measurements in phantoms correlated strongly (adjusted r2>0.98$$ {r}^2>0.98 $$) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were 427±22$$ 427\pm 22 $$, 47.7±3.3 ms$$ 47.7\pm 3.3\;\mathrm{ms} $$, and 7±2%$$ 7\pm 2\% $$ for T1$$ {\mathrm{T}}_1 $$, T2$$ {\mathrm{T}}_2 $$, and fat signal fraction, giving biases of 71$$ 71 $$, −30.0 ms$$ -30.0\;\mathrm{ms} $$, and −5$$ -5 $$ percentage points, respectively, when compared to conventional methods. Conclusion: A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co‐registered 3D T1$$ {\mathrm{T}}_1 $$, T2$$ {\mathrm{T}}_2 $$, and fat‐signal‐fraction maps with full coverage of the liver, from a single nine‐and‐a‐half‐minute free‐breathing scan. Further development is needed to achieve accurate proton‐density fat fraction (PDFF) estimation in vivo. [ABSTRACT FROM AUTHOR]

Purpose: Myocardial T1ρ mapping techniques commonly acquire multiple images in one breathhold to calculate a single‐slice T1ρ map. Recently, non‐selective adiabatic pulses have been used for robust spin‐lock preparation (T1ρ,adiab). The objective of this study was to develop a fast multi‐slice myocardial T1ρ,adiab mapping approach. Methods: The proposed‐sequence reduces the number of breathholds required for whole‐heart 2D T1ρ,adiab mapping by acquiring multiple interleaved slices in each breathhold using slice‐selective T1ρ,adiab preparation pulses. The proposed‐sequence was implemented with two interleaved slices per breathhold scan and was quantitatively evaluated in phantom experiments and 10 healthy‐volunteers against a single‐slice T1ρ,adiab mapping sequence. The sequence was demonstrated in two patients with myocardial scar. Results: The phantom experiments showed the proposed‐sequence had slice‐to‐slice variation of 1.62% ± 1.05% and precision of 4.51 ± 0.68 ms. The healthy volunteer cohort subject‐wise mean relaxation time was lower for the proposed‐sequence than the single‐slice sequence (137.7 ± 5.3 ms vs. 148.4 ± 8.3 ms, p < 0.001), and spatial‐standard‐deviation was better (18.7 ± 1.8 ms vs. 21.8 ± 3.4 ms, p < 0.018). The mean within‐subject, coefficient of variation was 5.93% ± 1.57% for the proposed‐sequence and 6.31% ± 1.92% for the single‐slice sequence (p = 0.35) and the effect of slice variation (0.81 ± 4.87 ms) was not significantly different to zero (p = 0.61). In both patient examples increased T1ρ,adiab (maximum American Heart Association‐segment mean = 174 and 197 ms) was measured within the myocardial scar. Conclusion: The proposed sequence provides a twofold acceleration for myocardial T1ρ,adiab mapping using a multi‐slice approach. It has no significant difference in within‐subject variability, and significantly better precision, compared to a 2D T1ρ,adiab mapping sequence based on non‐selective adiabatic spin‐lock preparations. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. Methods: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. Results: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correctionmethods, with referencelessmethods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 smm-2. It isworth noting that as the strength of the diffusion encoding increases, the performance gap between the referencelessmethod and the navigator-based method diminishes. Conclusion: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans. [ABSTRACT FROM AUTHOR]

Purpose: Simultaneous PET‐MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis‐registration between free‐breathing 3D PET and 2D breath‐held MR images. We propose a free‐breathing non‐rigid motion‐compensated 3D T2‐mapping sequence enabling whole‐heart myocardial tissue characterization in a hybrid 3T PET‐MR system and provides non‐rigid respiratory motion fields to correct also simultaneously acquired PET data. Methods: Free‐breathing 3D whole‐heart T2‐mapping was implemented on a hybrid 3T PET‐MRI system. Three datasets were acquired with different T2‐preparation modules (0, 28, 55 ms) using 3‐fold undersampled variable‐density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi‐contrast non‐rigid motion‐corrected MR reconstruction. T2‐maps were computed using dictionary‐matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET‐MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2‐mapping (in‐vivo). Results: Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In‐vivo 3D T2‐mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2‐mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland–Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of −2.4‐6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of −1.9 to 4.6 ms for patients. Conclusion: Validated efficient 3D whole‐heart T2‐mapping at hybrid 3T PET‐MRI provides myocardial inflammation characterization and non‐rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR‐based motion correction. [ABSTRACT FROM AUTHOR]