Introduction
Cardiac magnetic resonance (CMR) imaging is a routine part of the diagnostic work-up in heart disease. An advantage of CMR is the variety of physiological and dynamic imaging methods available. However, many of the methods are still sensitive to respiratory motion, which is often resolved by performing the acquisition during breath holding. Breath holding can be difficult for patients to sustain and often leads to degraded image quality and motion blurring [
1] or misalignment between stacks of two-dimensional (2D) image slices acquired during separate breath-holds [
2,
3]. Furthermore, an intrinsic part of breath holding is that only one respiratory phase is imaged. As an alternative solution, free-breathing CMR techniques have been developed that allow acquisitions to be long enough for three-dimensional (3D) imaging. Cardiac imaging during free breathing also enables reconstruction of respiratory resolved images [
4‐
6]. Bridging the gap between research in healthy individuals and clinical feasibility is an important step that often proves difficult. Patients have a higher variability in respiratory breathing patterns, heart rates, and ability to lie still during a long examination, thus making successful imaging challenging [
7].
Patients who would benefit from respiratory-resolved evaluation of LV volume are those with stiffness changes in the myocardium or pericardium [
8]. Filling of the right ventricle is affected by the pressure changes in the chest during respiration in healthy individuals [
9]. When the pressure is high during expiration, the venous return to the right atrium becomes restricted, resulting in a shift of the septum to the right, thus allowing an increase in LV filling. During inspiration, the lower chest pressure increases the venous return to the right side of the heart and thereby shifts the septum to the left, which decreases the filling of the left ventricle. The left ventricular filling pattern during respiration in patients with stiffness changes can become either increased or decreased compared to normal [
10,
11]. Distinguishing between diseases that present with similar symptoms, but have different etiologies and treatments, such as restrictive cardiomyopathy and constrictive pericarditis, could potentially be performed by measuring the respiratory variation in LV volumes.
Measurements of variation in LV volumes during respiration by 3D imaging has been attempted in healthy volunteers [
12], but has to our knowledge never been tested in patients. Respiratory variation in LV function during free breathing has been studied in patients by measuring septal excursion with 2D imaging [
10,
13]. However, free breathing 2D imaging is sensitive to through-plane motion and provides only a regional assessment of the respiratory-induced variation.
A method for respiratory-resolved free breathing 3D acquisition has previously been developed [
12,
14], evaluated in healthy individuals, and successfully illustrated the respiratory variation in LV end-diastolic volumes (EDV) [
14]. Two challenges were identified in the method; reduced image quality caused by eddy current effects, and sensitivity to changes in respiratory patterns with the respiratory self-gating method. The eddy current artifacts were caused by rapid movement in
k-space from repetition time to repetition time when using the golden-angle trajectory. The respiratory self-gating did not take the amplitude of respiration into account, and could lead to situations where data, acquired in cycles with higher amplitudes or different shape, were sorted into the wrong respiratory phase bins. Furthermore, the
k-space-based self-gating required a precise positioning of a band-pass filter, which might not be able to account for greatly varying frequencies in patients. In this work, the acquisition was modified to address these previously identified challenges. Specifically, the golden-angle trajectory was modified to decrease the mean angle between consecutive spokes from 92° of the original golden-angle trajectory to 7° to reduce image artifacts from eddy currents, and a new projection-based respiratory self-gating strategy was implemented. LVEDV and LVED diameters from both self-gating strategies were compared and tested in patients consecutively referred for clinical evaluation.
Materials and methods
Clinical cardiac patients
Data were acquired from ten patients (48 ± 16 years, 6 males) referred for a clinical CMR scan for cardiac evaluation. The study was approved by the regional human subject research ethics review board, and all patients provided written informed consent. Data acquisition for this study was added to the standard clinical protocol and was performed prior to administration of contrast agent.
Data acquisition
Images were acquired using a clinical 1.5-T MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) during free breathing and with a spine and a chest multi-channel surface coil with 30 active channels in total. The acquisition was undertaken using a free-running double golden-angle 3D radial trajectory [
15] incorporated in a prototype-balanced steady-state free precession sequence. The double golden-angle trajectory was given by an azimuthal angle
α and a polar angle
β calculated from two golden means
ϕ1 and
ϕ2 as:
$$\begin{aligned} \alpha & = 2\pi \,\left\{ {m\phi_{2} } \right\} \\ \beta & = \cos^{ - 1} \left\{ {m\phi_{1} } \right\} , \\ \end{aligned}$$
(1)
where
m was the spoke number and {} denoted the modulo one operation. Conventionally, the golden means used are
ϕ1 = 0.4656 and
ϕ2 = 0.6823. In this work,
ϕ1 = 0.0219 and
ϕ2 = 0.0102 were used to reduce the angle between consecutively acquired radial spokes from 92 ± 13° to 7 ± 8° (mean ± standard deviation) to reduce image degradation from eddy current-related artifacts [
16]. A superior–inferior radial spoke for respiratory self-gating was incorporated into the sequence, and was acquired every 25th repetition time (every 64 ms) to detect the motion of the diaphragm during respiration. With the superior–inferior spoke, the corresponding mean angles are 90 ± 17° and 11 ± 17° for the original and the reduced angular step, respectively.
Imaging parameters were: matrix size 176 × 176 × 176, isotropic voxel size 2 × 2 × 2 mm3, repetition time/echo time 2.5/1.3 ms, flip angle 60°, 250,000 radial spokes, including 10,000 superior–inferior spokes for projection-based self-gating, which were not used for image reconstruction. The 3D image volume was manually positioned over the heart, but not angled, and total scan time was 10 min. ECG and bellows signals were simultaneously acquired.
Phantom imaging was performed in a bottle containing a nickel sulfate solution (NiSO4), once with the original golden-angle trajectory and once with the modified angle increment. Imaging parameters for the phantom experiment were: matrix size 176 × 176 × 176, isotropic voxels 1.4 × 1.4 × 1.4 mm, TR/TE 2.9/1.4 ms, flip angle 60°, 48,657 radial spokes, corresponding to a fully sampled radial k-space. Images were reconstructed from all data of the free-running acquisition. To evaluate the robustness to physiological binning, a healthy volunteer (age 25 years, male) was also imaged with both methods. The parameters used for this experiment were: matrix size 176 × 176 × 176, isotropic voxels 2 × 2 × 2 mm, TR/TE 2.5/1.3 ms, flip angle 60°, 250,000 radial spokes, of which 10,000 were superior–inferior self-navigation spokes.
k-Space-based respiratory self-gating
The
k-space-based respiratory self-gating has been described in detail previously [
12,
14]. In short, the
k-space-based self-gating signal was extracted from a matrix consisting of the
k-space center sample in each spoke for all coil elements. First, a one-dimensional signal was found by combining all coil elements, and second, the signal was band-pass filtered to include only respiratory frequencies. The coils were combined using a reference vector consisting of the
k-space center sample from one radial spoke for all coil elements. The one-dimensional signal was then obtained from the scalar product of the reference vector and all other vectors of center samples over coils. Respiratory phase was extracted from an analytical signal and the phase wraps of the analytical signal were used to distinguish between respiratory cycles.
Only respiratory phase information was contained in the
k-space-based self-gating signal. The position of mid-inspiration was determined as 28% of the respiratory cycle relative to end-expiration, and mid-expiration as 88% of the respiratory cycle relative to end-expiration, corresponding to phase bins 1 and 6 in our previous work [
14].
Projection-based respiratory self-gating
For projection-based respiratory self-gating, the respiratory signals were extracted from the superior–inferior spokes, which were acquired with a frequency of 15.7 Hz. Projection-based respiratory self-gating has been described previously [
17,
18] and the respiratory self-gating signals were extracted in five steps adapted from [
19]. First, the projection in image space of all the superior–inferior spokes was calculated using a one-dimensional Fourier transformation along the spoke direction, providing spatial information about the structures along the direction perpendicular to the diaphragm over time. Second, principal component analysis was performed coil-wise for all projections. Third, locally weighted scatterplot smoothing was performed with a span of six points. Fourth, spectral clustering of the two dominant principal components from each coil element was performed to estimate a single respiratory self-gating signal. Fifth, slowly varying trends in the self-gating signal were removed with a second-, third- or fourth-order polynomial fit, depending on the degree of signal variation that was caused by other factors than the respiratory motion.
To divide individual respiratory cycles, an analytical signal of the projection-based respiratory self-gating signal was extracted using the Hilbert transform. Cycle starting points were extracted from the phase angle of the analytical signal at 2π wrapping points. A mid-inspiration and a mid-expiration point were determined within each respiratory cycle using the maximum amplitude of the cycle and the minimum amplitudes in the beginning and end of the cycle. The mid-inspiration point was positioned where the self-gating signal amplitude was exactly halfway between the minimum in the beginning of the cycle and the maximum of the cycle. The mid-expiration point was positioned at the amplitude half of the maximum of the cycle and the minimum at the end of the cycle.
Data binning of respiratory pressure extremes
Each radial spoke was assigned a combination of respiratory and cardiac phase. Data from two bins representing end-diastole in mid-inspiration and mid-expiration, corresponding to the minimum and maximum intra-thoracic pressure, were reconstructed. The reconstructed diastolic window has a width of 12.5% and starts at 70% of the RR interval. The width of the respiratory bins was defined as 15% of the respiratory cycle duration and centered on the points of mid-inspiration and mid-expiration found from the self-gating signals, as identified using both the projection-based and the k-space-based methods.
Image reconstruction
Images from the data bins were reconstructed using conjugate gradient SENSE [
20] with four iterations. The number of iterations was chosen by visual inspection, considering the trade-off between improved image quality and noise amplification with increasing number of iterations. Adaptive calculation of coil sensitivity maps was done from the first 10,000 consecutive radial spokes in each acquisition as previously described [
21].
For every reconstructed data bin, the image volume was rotated using multi-planar reformatting to obtain short-axis, 2-, 3- and 4-chamber views. To avoid degradation from the additional interpolations associated with multi-planar reformatting in the final image, the calculated angles were used to transform the k-space coordinates prior to the gridding step. Image slices of 8-mm thickness were calculated by summing four complex-valued 2-mm slices in each of the four cardiac views to limit the number of slices for segmentation and improve the signal-to-noise ratio.
Left ventricular volume segmentation
Manual segmentation of the left ventricular endocardial border was performed using the freely available software Segment 2.1 R5874 (Medviso, Lund, Sweden) [
22]. Segmentation was performed for each patient in mid-inspiration and mid-expiration in end-diastole, for both of the self-gating methods. All segmentations were performed by a single observer (KH), and were reviewed by an experienced physician.
Respiratory-induced change in left ventricular diameter
Segmentation of the LV endocardial border was used to measure the LVED volumes and diameters. The diameter was measured in the septal–lateral direction and the anterior–inferior direction. One mid-ventricular short-axis image during mid-inspiration and mid-expiration from each patient was used for these measurements.
LV segmentation was used for automatic identification of the centroid point in each mid-ventricular image slice. An anterior–inferior line was thereafter manually angled to intersect the centroid and the anterior–inferior diameter was measured as the distance between the two points on the segmented endocardial border intersecting this line. The septal–lateral diameter was measured in the same way from a line perpendicular to the anterior–inferior direction while intersecting the centroid.
Image quality assessment
The difference between the original and the modified angle increment was evaluated by calculating a normalized average absolute deviation (NAAD) of the signal intensity in the phantom images, defined as:
$${\text{NAAD}} = 1 - \frac{{\mathop \sum \nolimits_{i = 1}^{N} \left| {s_{i} - } \mu\right|}}{\mu N} ,$$
(2)
where
s is the signal intensity in a voxel,
μ is the mean within the volume and
N is the number of voxels within the volume. To minimize noise contributions to the deviation, the images were convolved by a 9-point cosine filter kernel prior to the calculation. Only voxels with at least 25% of the maximum intensity were considered.
To evaluate potential differences in image quality between the two self-gating methods, two blinded observers were presented with one short-axis stack at a time and were asked to rate the image quality on a scale from 1 to 5, where 1 = non-diagnostic, 2 = poor, 3 = adequate, 4 = good and 5 = excellent. The image stacks were presented in a randomized order.
Statistics
Continuous data are presented as mean ± standard error of the mean (SEM) and discrete data as mean ± standard deviation (SD). Volume difference between self-gating methods and observer scores was tested with the Wilcoxon signed rank test using MATLAB (version R2016b; Mathworks, Natick, MA, USA). Interobserver agreement was assessed using Spearman’s rank correlation coefficient (ρ). P < 0.05 was considered statistically significant.
Discussion
Golden-angle radial acquisitions in 3D and during free breathing with projection-based self-gating allows for measurement of respiratory-induced variation in LVEDV and LVED diameters in cardiac patients.
Phantom experiments suggest that eddy current-induced artifacts seem to be reduced using the smaller angular increment. Notably, the inclusion of a superior–inferior self-navigation spoke did not visibly impact the eddy current characteristics. An in vivo experiment in a healthy volunteer shows a more homogenous fat signal across the field of view.
Projection-based respiratory self-gating from superior–inferior spokes was able to detect the respiratory motion in terms of both respiratory phase and amplitude when visually comparing to the measured bellows signal. A phase shift between the bellows signal and the projection-based self-gating signal was present for all patients, likely caused by indirect coupling between the abdominal circumference and the diaphragmatic position during respiration. This illustrates the importance of an internal, and more direct, measurement of respiration other than respiratory bellows [
23,
24]. Using amplitude detection within each respiratory cycle, adaptation to varying respiratory patterns is intrinsic to the method. In contrast, the
k-space-based self-gating method relies on the analytic phase varying appropriately over the different cycles. The complex amplitude of the
k-space center is not by necessity correlated with the amplitude of the motion, but rather a measure of the global signal changes over time. However, considering the spatial encoding provided by the surface coil elements, it might be possible to extract a self-gating signal related to the respiratory amplitude.
Analysis of the k-space-based respiratory self-gating signals shows that the k-space-based method is not consistently capturing the respiratory phase, which could explain why the k-space-based self-gating could not observe any respiratory-induced variation in LVEDV and LV septal–lateral diameters.
Images from representative patients show that the contrast between blood and myocardium was higher with the projection-based self-gating compared to the k-space-based self-gating. Especially the septal and anterior LV walls were more clearly defined with the projection-based method. Also, the heart–liver interface was more clearly defined with the projection-based self-gating. The interfaces were likely blurry when using k-space-based self-gating because the two respiratory phases were not identified correctly using that technique.
With the k-space-based self-gating, the blood–myocardial borders are poorly defined, especially in mid-expiration. Segmentation of the LV endocardial border indicates a larger difference in size, location and shape of the left ventricle, and the septal wall in particular, between mid-inspiration and mid-expiration with the projection-based self-gating compared to k-space-based self-gating. There is almost no difference in the segmentation with the k-space-based self-gating.
Absolute LVEDV measurements support the observations that projection-based self-gating was able to distinguish between mid-inspiration and mid-expiration and measured an 11-ml difference between the two phases, whereas the k-space-based self-gating was not able to detect any difference between the two phases. Also, the respiratory-induced variation in LVEDV was higher with the projection-based self-gating. The mid-expiratory LVEDV was higher with the projection-based self-gating compared to the k-space-based self-gating. This could be caused by the lower myocardium-to-blood contrast with the k-space-based self-gating making manual segmentation more difficult.
A previous study showed that the
k-space-based self-gating was able to separate the mid-inspiration and mid-expiration in healthy volunteers and measured relative differences in mid-inspiratory LVEDV compared to mid-expiratory LVEDV of 5–6% [
14]. In the current study, the
k-space-based self-gating could not detect any difference between mid-inspiration and mid-expiration. Healthy volunteers likely have more stable breathing patterns in both amplitude and frequency than patients. The use of a fixed-frequency band-pass filter could potentially explain the bad performance in patients, if they had more varying respiratory frequencies. On the other hand, the difference in LVEDV between mid-inspiration and mid-expiration with the projection-based self-gating was 8%, which was even higher than measured in healthy individuals. This could potentially indicate that the
k-space-based measurements in the healthy volunteers were indeed underestimations too, but less so than observed in patients. If the respiratory phases are not identified correctly, it is more probable that it leads to underestimation rather than overestimation of the volume or diameter differences. This has to be verified further by comparing the two respiratory self-gating methods in healthy volunteers before a conclusion can be made.
Measurements of LVED diameters corroborate the findings in volume measurements. Only the septal–lateral diameter measured from the projection-based self-gating detected a difference between mid-inspiration and mid-expiration. No respiratory-induced variation was found in anterior–inferior diameter with either of the self-gating methods indicating that the main contribution to the variation in LVEDV comes from the septal–lateral direction. This is supported by previous findings that the septum plays the main role in respiratory-induced variation in cardiac mechanics [
9]. Septal excursion in healthy individuals has previously been found to be 6.6 ± 2.6 mm [
10]. In the current study, the septal excursion was 4.4 ± 0.5 mm in patients with the projection-based self-gating.
A limitation of this study is that only two respiratory phases were reconstructed. Another limitation is the relatively small number of patients, which warrants future validation studies with power analysis based on the results in this initial study. Furthermore, the introduction of the self-gating projection spoke in the pulse sequence could potentially cause eddy current-induced oscillations, and it should be verified that this does not cause disturbing image artifacts. In the current implementation, the drift correction of the projection-based self-gating signal was performed manually in each patient, which is a limitation from a reproducibility standpoint. The images were reconstructed with the CG-SENSE method. With the imaging parameters used, the blinded observers deemed the image quality to be adequate to poor, on average. Potential improvement of image quality could be obtained by employing a more advanced reconstruction method such as Alternating Direction Method of Multipliers or Low-Rank and Sparse.
Projection-based respiratory self-gating enables 10-min acquisition of free-running 3D double golden-angle data in patients and is able to measure the variation in LVEDV and LV septal–lateral diameters between mid-inspiration and mid-expiration.
Acknowledgements
This research was in part funded by the Swedish Foundation for Strategic Research [SSF ICA-120063], the Stockholm County Council [ALF LS 1411-1372] and the Swedish Heart Lung Foundation, Stockholm, Sweden. The authors thank Jannike Nickander, MD, for assistance in image segmentation, and technologists Jenny Rasck, Sofie Olsson and Márcia Guerra Tomé Ferreira as well as Peder Sörensson, MD for data acquisition. We acknowledge Siemens Healthineers for providing the scanner-programming environment and Dr. Frederik Testud, Siemens Healthcare AB, Sweden, for automatic extraction of physiological data from the pulse sequence.