Analysis of spatiotemporal fidelity in quantitative 3D first-pass perfusion cardiovascular magnetic resonance
© The Author(s). 2017
Received: 2 September 2016
Accepted: 11 January 2017
Published: 27 January 2017
Whole-heart first-pass perfusion cardiovascular magnetic resonance (CMR) relies on highly accelerated image acquisition. The influence of undersampling on myocardial blood flow (MBF) quantification has not been systematically investigated yet. In the present work, the effect of spatiotemporal scan acceleration on image reconstruction accuracy and MBF error was studied using a numerical phantom and validated in-vivo.
Up to 10-fold scan acceleration using k-t PCA and k-t SPARSE-SENSE was simulated using the MRXCAT CMR numerical phantom framework. Image reconstruction results were compared to ground truth data in the k-f domain by means of modulation transfer function (MTF) analysis. In the x-t domain, errors pertaining to specific features of signal intensity-time curves and MBF values derived using Fermi model deconvolution were analysed. In-vivo first-pass CMR data were acquired in ten healthy volunteers using a dual-sequence approach assessing the arterial input function (AIF) and myocardial enhancement. 10x accelerated 3D k-t PCA and k-t SPARSE-SENSE were compared and related to non-accelerated 2D reference images.
MTF analysis revealed good recovery of data upon k-t PCA reconstruction at 10x undersampling with some attenuation of higher temporal frequencies. For 10x k-t SPARSE-SENSE the MTF was found to decrease to zero at high spatial frequencies for all temporal frequencies indicating a loss in spatial resolution. Signal intensity-time curve errors were most prominent in AIFs from 10x k-t PCA, thereby emphasizing the need for separate AIF acquisition using a dual-sequence approach. These findings were confirmed by MBF estimation based on AIFs from fully sampled and undersampled simulations. Average in-vivo MBF estimates were in good agreement between both accelerated and the fully sampled methods. Intra-volunteer MBF variation for fully sampled 2D scans was lower compared to 10x k-t PCA and k-t SPARSE-SENSE data.
Quantification of highly undersampled 3D first-pass perfusion CMR yields accurate MBF estimates provided the AIF is obtained using fully sampled or moderately undersampled scans as part of a dual-sequence approach. However, relative to fully sampled 2D perfusion imaging, intra-volunteer variation is increased using 3D approaches prompting for further developments.
KeywordsFirst-pass myocardial perfusion Myocardial blood flow Modulation transfer function k-t PCA k-t SPARSE-SENSE 3D-MTF Whole-heart perfusion
Diagnosis of ischemia in patients with known or suspected coronary artery disease (CAD) is increasingly being performed using cardiovascular magnetic resonance (CMR) first-pass perfusion imaging. Perfusion CMR outperforms other imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in terms of spatial resolution and operates without ionizing radiation. Compared to coronary angiography and the assessment of fractional flow reserve, perfusion CMR is non-invasive and has been proven suitable for patients with intermediate probability of significant CAD . Numerous authors have compared the diagnostic performance of perfusion CMR to SPECT [2–6] and PET [7–10], and found that CMR performs at least comparably to these methods . Studies comparing perfusion CMR to stress echocardiography and perfusion computed tomography report similar results [12–14].
Detection of small ischemic regions such as sub-endocardial perfusion defects is enabled by the high spatial resolution offered by CMR . In addition to sufficient spatial resolution, whole-heart coverage is desired to accurately assess the size and extent of perfusion deficits . These demands have triggered the development of three-dimensional (3D) scanning techniques employing advanced undersampling strategies for efficient data acquisition [17–19]. The importance of whole-heart imaging has further been stressed by authors evaluating the volumetric ischemic burden as a marker of significant CAD [20–22]. Optimization of scanning efficiency has also been made in the temporal domain. Multiple authors have proposed perfusion CMR throughout the cardiac cycle to assess differences in perfusion between heart phases and to combine cine and perfusion imaging into a single scan [23–25]. Alternatively, interleaved acquisition at different heart phases may be used to separately capture blood pool and myocardial enhancement for improved perfusion quantification [26, 27] or frame-by-frame T1 mapping .
Absolute quantification of perfusion CMR has gained significant attention in the past decade since clinical advantages have been pointed out [29, 30]. Several technical aspects of myocardial blood flow (MBF) estimation from reconstructed images have been investigated mostly using single or multi-slice 2D imaging. Special focus has been put on the development of mathematical models for MBF estimation [31–34] and comparison between them [35, 36]. Zarinabad et al. have compared voxel-wise vs. spatially averaged (sector-wise) estimation of MBF  and highlighted the importance of accurate bolus arrival time estimation . Most recently, the feasibility of 3D CMR perfusion quantification has been demonstrated [25, 27].
Perceived image quality and deviation from a fully sampled reference image have traditionally been used as direct measures to validate spatiotemporal scan acceleration methodology [38–40]. A more general approach is the use of the modulation transfer function (MTF) concept to characterize the ability of a MR system to correctly capture spatial and temporal frequencies . A perturbation of the system combined with linear regression is used yielding the MTF derived from the slope, and an artefact map based on the ratio of slope and intercept of the linear fit. This method is well suited for linear reconstruction methods, but application to non-linear reconstruction techniques such as compressed sensing is also feasible if linearization about a suitable expansion point is used. Consequently, the MTF approach can be employed to compare spatiotemporal performance of linear and non-linear reconstruction algorithms, such as k-t PCA and k-t SPARSE-SENSE [42, 43].
The present study introduces a linearized MTF approach to evaluate k-t PCA and k-t SPARSE-SENSE in the context of highly accelerated, fully quantitative 3D myocardial perfusion imaging. MTF maps derived from numerical phantoms and in-vivo data are used to investigate changes of spatiotemporal fidelity introduced by undersampling. Furthermore, errors in signal intensity-time curves are analysed and their influence on MBF estimation is highlighted. MRXCAT simulation of a sub-endocardial lesion reveals the ability of the proposed methodology to identify small ischemic territories. Finally, simulation results are validated in-vivo comparing 3D k-t PCA, 3D k-t SPARSE-SENSE and fully sampled 2D imaging.
k-t PCA and k-t SPARSE-SENSE
k-t PCA and k-t SPARSE-SENSE are reconstruction methods based on differing principles both suited for highly accelerated MRI.
followed by Fourier transformation to the x-t domain.
with the encoding matrix E as above, the data d expressed as a vectorised form of D, and i the image to be reconstructed. Φ represents a sparsifying transform and λ is the regularization parameter. In k-t SPARSE-SENSE, the reconstruction equation is minimized using a POCS-like algorithm alternating between data consistency and soft-thresholding [45, 46] leaving the acquired data unchanged, or non-linear conjugate gradient optimization . Common choices for Φ include the temporal Fourier transform (FT), temporal PCA or a mixture of both starting with the temporal FT for the first iterations, followed by PCA for the remaining iterations .
Spatiotemporal modulation transfer functions
While the formalism is directly valid for the linear k-t PCA in eq. (3), for k-t SPARSE-SENSE (eq. (5)) an approximately linear relationship between fully sampled and accelerated imaging is assumed based on linearization about a suitable expansion point. This expansion point corresponds to the magnitude of the unperturbed object at each position in k y -k z -f space.
In the original interpretation of the MTF formalism a true object and its imaged version are compared. The natural upper bound for the MTF is 1, indicating that a certain voxel in k-f space perfectly reproduces the corresponding object part. Lower values of the MTF indicate image degradation by the imaging system. Note that this strict physical constraint not necessarily applies to scan acceleration. Especially at the k-f-space edges, where the signal-to-noise ratio (SNR) is low, the effect of undersampling and subsequent reconstruction might also increase k-f space magnitudes, resulting in MTF values above 1. Therefore, only the central k-f-space parts of the MTF should be evaluated.
Myocardial blood flow quantification
In this equation, F is the MBF estimate, and α, β are further fitting parameters. Note that the units of measurement for c AIF(t) and c MYO(t) are mmol/mL, while the amount of contrast agent in the myocardium measured by indicator dilution theory is in units of mmol/g of tissue. This discrepancy is implicitly corrected by scaling F by the myocardial tissue density of 1.05 g/mL .
In-vivo CMR experiments were performed in 10 healthy volunteers (4 males) on a Philips Achieva 1.5 T scanner (Philips Healthcare, Best, The Netherlands) using a 5-channel cardiac coil array. Volunteers had an average age of 26.2 ± 4.7 years and underwent CMR upon written informed consent in accordance with ethics regulations approved by the local ethics committee. Dynamic contrast enhanced CMR was conducted twice per volunteer and at least 20 min apart. Gadobutrol (Gadovist, Bayer Schering Pharma, Germany) at 0.075 mmol/kg b.w. dose was injected as contrast agent, followed by a 30 mL saline flush at 4 mL/s. Volunteers were measured during instructed breath-holding.
A saturation-recovery dual-sequence spoiled gradient echo sequence with ECG-triggering was used to acquire one image pair per heartbeat. The interleaved acquisitions  consisted of a 2D aortic scan for arterial input function (AIF) assessment and an end-systolic left-ventricular scan to capture myocardial enhancement, as proposed earlier . Myocardial enhancement was assessed using 3D imaging accelerated by k-t PCA (N = 7 measurements), k-t SPARSE-SENSE (N = 7), and fully sampled single-slice 2D imaging (N = 6) for comparison. To limit the amount of contrast agent administered and the examination time per volunteer, only two injections per volunteer were carried out. This resulted in three groups of volunteers, allowing comparison of k-t PCA or k-t SPARSE-SENSE with fully sampled 2D imaging (N = 3 for both), and direct inter-comparison between the accelerated sequences (N = 4).
2D AIF imaging was planned orthogonally to the ascending aorta in transverse view, with a separate WET saturation preparation pulse. An ultrashort T SAT of 3.7 ms was enabled using a central-out profile order, i.e. acquisition started at the k-space centre, continued outwards and concluded at the most distant point from the centre. Further 2D scan parameters were: 3x k-t PCA acceleration, 11 training profiles, spatial resolution: 3.5×3.5 mm2, slice thickness: 10 mm, field-of-view: 260×300 mm2, flip angle: 15°, acquisition window: 40–48 ms, T R : 1.67 ms, T E : 0.58 ms.
In addition to contrast-enhanced imaging, baseline T 1 values were measured in all volunteers using modified Look-Locker inversion recovery (MOLLI) imaging . MOLLI acquisitions were done before the first and second contrast administration. Population average pre-contrast myocardial and left-ventricular T 1 values for the first and second injection were determined from these MOLLI T 1 maps. These average T 1 values were subsequently used for signal intensity to contrast agent concentration conversion, as outlined below.
k-t PCA and k-t SPARSE-SENSE reconstructions were implemented in ReconFrame (Gyrotools LLC, Zurich, Switzerland) and Matlab R2014a (MathWorks, Natick MA, USA). Sensitivity maps were derived from a separately acquired reference scan. The k-t SPARSE-SENSE implementation comprised soft thresholding and a combination of temporal FT (10 iterations) and PCA (iteration 11 onwards) as sparsifying transforms . Reconstruction voxel sizes of 2×2 mm2 and 1.25×1.25×5 mm3 were achieved using zero-filling of the 2D AIF image and the accelerated 3D scan, respectively. All reconstructed in-vivo images were manually segmented to yield regional signal intensity-time curves.
Modulation transfer function analysis
Numerical simulations were performed to compare images reconstructed from undersampled data with fully sampled references using MTFs. A fully sampled 3D numerical phantom was created using the MRXCAT simulation framework . Phantom parameters were: spatial resolution: 2.3×2.3 mm2, slice thickness: 5 mm, 10 slices, T R /T E : 2.0/1.0 ms, flip angle: 15°, contrast agent dose: 0.075 mmol/kg b.w., 5 receive coils, myocardial blood flow (MBF): 1 mL/g/min. 64 noise realizations with equal noise statistics were performed, each comprising 11 different perturbations for 4 different acceleration factors (cf. below). In each realization, 11 identical datasets were generated, which were individually perturbed by multiplication with factors 0.95–1.05 in steps of 0.01, and subsequent degradation by noise (SNR = 20). Scaling was done to ensure that a certain signal intensity range was covered for linear regression analysis. Compared to completely random perturbations without scaling, this approach ensured a spread of signal values at every k-space position. This resulted in a drastically reduced number of iterations required to probe linearity at all spatiotemporal frequency positions.
Timing constraints prohibit acquisition of a fully sampled 3D dataset during the first-pass of the contrast agent in-vivo. Hence, reference single slice 2D data were used for in-vivo MTF analysis. MTF calculations were performed using the same undersampling factors and procedure as for the MRXCAT phantom. In contrast to the MRXCAT case, training consisted of 11 profiles in k y only, resulting in different net acceleration factors (1.9, 3.8, and 5.9), and the S2A threshold was set to 2.5.
Image-time domain analysis
In addition to MTF analysis in k-f space, signal intensity vs. time curves extracted from MRXCAT images were investigated. Direct comparison of accelerated scanning simulations with fully sampled reference data allows for estimation of data fidelity upon undersampling during contrast enhancement. Furthermore, specific features of the signal intensity-time curve such as the pre-contrast baseline, peak enhancement and upslope can be compared. Errors in these features will directly propagate into the estimated myocardial blood flow upon signal-to-concentration conversion or deconvolution fitting.
Myocardial blood flow quantification
where T 1,0 is the baseline T 1 in the absence of contrast agent, and r the material-specific relaxivity of the contrast agent. Resolving eq. (14) yields the concentration c of the contrast agent.
Baseline ranges for signal-to-concentration conversion were set to time frames 1–5 for the AIF and 1–10 for the myocardial curves in all MRXCAT simulations. Since baseline length, timing of acquisition and contrast agent injection vary in-vivo, baseline range selection was done manually in each volunteer dataset. In-vivo population average pre-contrast T 1,0 values derived from MOLLI imaging were: 1590 ms for the left ventricle and 1020 ms for the myocardium at the first contrast agent injection. T 1,0 before the second injection were 640 ms and 680 ms, respectively.
In a second step, the concentration vs. time curves c AIF and c MYO from the blood pool and the myocardium, respectively, were related to estimate the MBF using Fermi model deconvolution as detailed in eqs. (11) and (12), and reference .
Sub-endocardial Ischemic lesion simulation
The ability of the proposed 3D methods to reveal small ischemic defects was probed by MRXCAT simulation of sub-endocardial ischemia. Ischemia was introduced in a single slice of the MRXCAT phantom with a healthy rest MBF of 1 mL/g/min. The ischemic region in a mid-ventricular slice covered a circumferential lateral sector spanning 60°, and a transmural sub-endocardial layer of 1–2 voxels. In this ischemic territory, contrast enhancement was suppressed such that the signal intensities remained around the baseline level during all time frames. Ischemic MRXCAT data were reconstructed without undersampling and at 10x scan acceleration using both k-t PCA and k-t SPARSE-SENSE. Subsequently, MBF quantification was performed.
Figure 3a displays a k y -f slice of the MRXCAT MTF map at k z = 0 for k-t PCA and k-t SPARSE-SENSE at nominal acceleration factors of 2, 5, and 10. A k z -f slice at k y = 0 of the MRXCAT MTFs is shown in Fig. 3b. For both reconstruction methods at all acceleration factors, the non-zero MTF values lie around the main axes, i.e. along the different direct current (DC) regions. In the temporal DC region, data at most spatial frequencies k y and k z are partially restored upon undersampling. Similarly, at spatial DC, all temporal frequency components are restored to a certain degree. MTF values decrease with increasing distance from the DC axes. For k-t PCA at different undersampling factors, the shape of the MTF remains similar with slight narrowing of the non-zero regions near the DC axes. At R = 10, the MTF is noisier than at lower acceleration indicating noise amplification at certain spatiotemporal frequencies. Compared to k-t SPARSE-SENSE, k-t PCA restores off-DC temporal frequencies on a relatively narrow range. As a consequence, MTFs from k-t SPARSE-SENSE have a larger non-zero area, but exhibit larger changes when increasing R. MTF values >1 away from the DC axes signify deviation from linear behaviour due to the non-linearity of the reconstruction algorithm. A number of spatial frequency components along k y is not restored using 10x k-t SPARSE-SENSE. This leads to a loss of in-plane spatial resolution in the reconstructed image.
MTF results derived from 2D in-vivo data are illustrated in Fig. 3c, revealing similar patterns as for the 3D simulation along the DC axes. In contrast to 3D, 2D results exhibit lower signal-to-artefact ratios, yielding smaller non-zero MTF areas despite the slightly reduced signal-to-artefact threshold. As in the 3D simulation at maximum undersampling rate R = 10, k-t SPARSE-SENSE exhibits a loss of spatial resolution in phase-encoding direction.
The feasibility of MBF estimation from highly undersampled first-pass myocardial perfusion MRI has been investigated and presented in this work. Effects were examined by means of k-f space based MTFs, image-time domain analysis of signal intensity, and by deconvolution using Fermi function modelling for MBF estimation. The MRXCAT framework  was employed for simulation, and complemented by in-vivo assessment of perfusion using accelerated 3D k-t PCA, 3D k-t SPARSE-SENSE and fully sampled 2D reference data.
The concept of the MTF describing the relationship between an imaged object and its image has been adapted to portray undersampled first-pass perfusion CMR. Thereby, the MTF represents the relationship in k-f space between the fully sampled and the undersampled data upon image reconstruction. Implementation in MRXCAT allowed for quantification of errors relating the accelerated imaging simulation to the corresponding fully sampled reference. The reduction in MTF area with increasing acceleration factor and the appearance of noise therein provide insights into the performance of the undersampling and reconstruction strategy.
For k-t PCA, the k y -f portion with MTF close to 1 remains almost unchanged from R = 2 up to R = 10, suggesting adequate performance of image reconstruction at all examined R. The increased noise-like patterns in 10x k-t PCA MTFs indicate that this acceleration factor is close to the maximum achievable R without major loss of data fidelity. On the other hand, k-t SPARSE-SENSE MTFs exhibit larger non-zero areas for all temporal frequencies further away from the spatial DC. MTF shapes at R = 2 and R = 5 are similar, but a sudden drop-off at high k y is observed at R = 10, indicating that data at higher spatial frequencies are not properly restored. This yields a loss in effective spatial resolution, which can be observed in-vivo comparing k-t PCA and k-t SPARSE-SENSE images in Fig. 9.
Starting from the original definition of the MTF as a relationship between the object and its image, the MTF may assume values between 0 and 1 because information about the object is only lost and never gained with bandlimited, linear imaging methods. However, when applied to the characterization of undersampling, MTF > 1 is possible indicating noise amplification at the corresponding k-f position by regularized reconstruction. This phenomenon is most prominent at high undersampling rates, e.g. in the k y -f MTF map for 10x k-t PCA. Around spatial DC, some temporal frequencies exceed 1 resulting in the noise-like MTF appearance. In contrast, MTF values vastly exceeding 1, as observed only in k-t SPARSE-SENSE MTF maps at higher k y , may not be explained by noise amplification alone. Presumably, these errors stem from the treatment of non-linear compressed sensing reconstruction with the linear MTF formalism. The assumption of linearity between images from fully sampled and undersampled acquisitions is violated at higher frequencies, which is in line with previous statements . The discrepancy between linear and non-linear reconstruction algorithms treated with the MTF formalism was corrected for using masking with a fixed threshold in the signal-to-artefact map.
Simulated signal intensity-time curves from the blood pool and the myocardium were examined at all acceleration factors R. The AIFs for different R agree well when upslopes and peak signal are compared, as well as for the baseline up to R = 5. Underestimation of the baseline at R = 10 is most prominent in k-t PCA with almost 20% error. The myocardial curves up to R = 5 agree well with the reference, but exhibit deviations from ground truth at the beginning (k-t PCA) or at the end of the baseline (k-t SPARSE-SENSE). These errors are reflected in the myocardial baseline error, which can be reduced if the time frames selected for baseline averaging are optimally chosen. Based on these findings, the first time frames might be excluded when determining the baseline in k-t PCA. Accordingly, for k-t SPARSE-SENSE, the last time points before contrast agent arrival should be discarded. The septum was chosen for myocardial signal-time analysis due to its strategic position between the right and left ventricle. Aliasing of components from left and right ventricles and the myocardium is expected in the septum upon undersampling, as these three compartments are aligned along the fold-over direction. Resolving the aliased data at this location should be more challenging than anywhere else in the myocardium .
The percentage errors upon MBF quantification using AIFs extracted from the undersampled image and from a fully sampled reference were compared. Global MBF underestimation up to 43% was observed at R = 10 with the AIF from undersampled data, an error not present when using the AIF from reference image. This finding indicates that the AIF baseline error may be the main source of inaccuracy. A remedy to address this issue in-vivo is interleaved AIF acquisition at small acceleration factors using dual-sequence imaging, thereby markedly reducing the AIF baseline error. Exact knowledge of sequence parameters included in the corresponding signal model was assumed in this simulation, alongside with perfect saturation efficiency. As previously shown, errors in parameter estimation as well as inefficient saturation may additionally distort the estimated MBF . In addition, signal intensity to concentration non-linearity effects may further degrade quantification accuracy for single-sequence acquisition schemes.
Identification of sub-endocardial ischemia is a key criterion for the clinical utility of novel myocardial perfusion scan and post-processing methodology. MRXCAT simulations of fully sampled and 10x accelerated imaging including a small ischemic lesion were performed to investigate this question. Quantification of 10x k-t PCA data yielded MBF values in good agreement with the fully sampled reference both in healthy and ischemic regions. In contrast, MBF values derived from the 10x k-t SPARSE-SENSE differed from the reference in healthy segments, with increased MBF variation. In the ischemic territory MBF reduction due to ischemia was less pronounced than in the reference. This latter effect may be related to the loss of effective spatial resolution observed in 10x k-t SPARSE-SENSE MTF analysis.
In-vivo data were measured using a dual-sequence acquisition framework enabling separate images mapping blood pool and myocardial enhancement . For 3x k-t PCA the AIF baseline error remained below 2% as confirmed by our simulations up to R = 5. In addition, dual-sequence imaging enabled separately optimized saturation delays for the interleaved scans, thereby eliminating the signal vs. concentration non-linearity concerns.
The range of average MBF values found in-vivo at rest was in line with previous findings. Variations of MBF across different volunteers are expected based on physiological differences. The change in mean MBF between different acquisition techniques is lower than the intra-volunteer MBF variation, and standard deviations in MBF around 20% compare well to previous work. This variation represents a persistent limitation of MBF quantification in part caused by the ill-posed nature of deconvolution fitting . The increased intra-volunteer variation observed in highly accelerated vs. fully sampled reference data can be explained in part by the loss in data fidelity and SNR caused by undersampling. To enhance MBF estimation precision, increasing the contrast-to-noise ratio by high dose first-pass imaging is an option . Furthermore, parallel imaging with up to 32 receive channels has been demonstrated to enhance image quality . Moreover, in accelerated first-pass perfusion CMR accurate segmentation of the myocardium is crucial. For instance, the sector-wise myocardial signal intensity-time curve in the septum may be severely distorted if a single voxel from the right ventricle or multiple voxels affected by partial volume effects are included in the segmentation. These challenges need to be addressed in order to adopt fully quantitative perfusion CMR in clinical routine.
In addition to solving the aforementioned implementation challenges, further validation is needed before clinical introduction of the proposed methods. Future studies could include patients with sub-endocardial ischemia to investigate the ability to detect small, localized lesions. In addition, patients with triple vessel disease or microvascular disease potentially benefit from quantitative methods and may be included in clinical studies. In these pathologies, healthy remote myocardium may be absent as a reference for qualitative or semi-quantitative approaches.
Combined modulation transfer function and signal-to-artefact ratio analysis is a useful means of studying the performance of accelerated 3D first-pass perfusion CMR acquisition in a linearized regime, correctly predicting losses in spatial and temporal resolution. Highly accelerated perfusion CMR enables estimation of myocardial blood flow provided an unbiased arterial input function is acquired, e.g. using dual-sequence acquisition. The accuracy of blood flow quantification from undersampled imaging is maintained compared to fully sampled reference images, whereas the precision measured by intra-volunteer variation is reduced prompting for further improvements of whole-heart 3D perfusion imaging approaches.
Arterial input function
Coronary artery disease
Cardiovascular magnetic resonance
Myocardial blood flow
Modulation transfer function
Principal component analysis
Positron emission tomography
Single photon emission computed tomography
This project was funded by the Swiss National Science Foundation, grant #CR3213_132671/1. Research support from Philips Healthcare, Best, The Netherlands is gratefully acknowledged.
Availability of data and materials
The MRXCAT framework used for numerical simulations contained in this study is available for download in source code format under www.biomed.ee.ethz.ch/mrxcat. In-vivo data used in this study are available from the corresponding author on request.
LW: Study design; realization of simulations and in-vivo CMR; volunteer recruiting and preparation; implementation and processing of image reconstruction, segmentation, quantification; authoring and revision of the manuscript. AG, SH: preparation and information of volunteers; CMR scanning. CS: Implementation and support for k-t SPARSE-SENSE reconstruction. KCT: Study design and planning; CMR scanning; manuscript revision. RM: in-vivo CMR supervision; responsibility for ethics regulations. SK: Study design and supervision; advice on post-processing; manuscript revision. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Consent to publish data from individual volunteers was obtained from all participants.
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the Canton of Zurich (KEK); reference number EK-1294. All volunteers gave written informed consent for study participation.
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