GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: a preliminary evaluation in pediatric patients with congenital heart disease

Huili Yang; KyungPyo Hong; Justin Baraboo; Lexiaozi Fan; Andrine Larsen; Michael Markl; Joshua D Robinson; Cynthia K Rigsby; Daniel Kim

doi:10.1002/mrm.29974

. Author manuscript; available in PMC: 2025 May 1.

Published in final edited form as: Magn Reson Med. 2023 Dec 12;91(5):1965–1977. doi: 10.1002/mrm.29974

GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: a preliminary evaluation in pediatric patients with congenital heart disease

Huili Yang ^1,², KyungPyo Hong ¹, Justin Baraboo ^1,², Lexiaozi Fan ^1,², Andrine Larsen ³, Michael Markl ^1,², Joshua D Robinson ^1,^4,⁵, Cynthia K Rigsby ^1,^4,⁶, Daniel Kim ^1,²

PMCID: PMC10950531 NIHMSID: NIHMS1948240 PMID: 38084397

Abstract

Purpose:

To develop a highly-accelerated, real-time phase contrast (rtPC) MRI pulse sequence with 40 fps frame rate (25 ms effective temporal resolution).

Methods:

Highly-accelerated Golden-Angle Radial Sparse Parallel (GRASP) with over regularization may result in temporal blurring, which in turn causes underestimation of peak velocity. Thus, we amplified GRASP performance by synergistically combining view-sharing (VS) and k-space weighted image contrast (KWIC) filtering. In 17 pediatric patients with congenital heart disease (CHD), the conventional GRASP and the proposed GRASP amplified by VS and KWIC (or GRASP+VS+KWIC) reconstruction for rtPC MRI were compared with respect to clinical standard PC MRI in measuring hemodynamic parameters (peak velocity, forward volume, backward volume, regurgitant fraction) at four locations (aortic valve, pulmonary valve, left and right pulmonary arteries).

Results:

The proposed reconstruction method (GRASP+VS+KWIC) achieved better effective spatial resolution (i.e., image sharpness) compared with conventional GRASP, ultimately reducing the underestimation of peak velocity from 17.4% to 6.4%. The hemodynamic metrics (peak velocity, volumes) were not significantly (P > 0.99) different between GRASP+VS+KWIC and clinical PC, whereas peak velocity was significantly (P < 0.007) lower for conventional GRASP. RtPC with GRASP+VS+KWIC also showed the ability to assess beat-to-beat variation and detect the highest peak among peaks.

Conclusion:

The synergistic combination of GRASP, VS, and KWIC achieves 25 ms effective temporal resolution (40 fps frame rate), while minimizing the underestimation of peak velocity compared with conventional GRASP.

Keywords: Real-time, phase contrast, radial MRI, compressed sensing, pediatric, flow

Introduction

Phase contrast (PC) MRI is used in routine clinical practice for quantitative assessment of blood flow in patients with a variety of cardiovascular diseases (1–3). Clinical standard 2D ECG-triggered PC MRI with segmented k-space imaging is performed either during breath-holding or free-breathing with averaging. In both scenarios, patients with irregular heart rhythm will generate ghosting imaging artifacts; in breath-held imaging, patients with limited breath-hold capacity will generate motion-induced artifacts; in free-breathing imaging with averaging, the boundaries of vascular wall are typically blurred due to averaging of data from multiple respiratory states. Finally, in young pediatric patients, 2D PC MRI is often performed under general anesthesia, because of their inability to stay still inside the MRI bore and perform breathing instructions. Thus, there is a need to establish technical solutions to address such challenges.

One approach to overcome the limitations of 2D PC MRI is performing real-time 2D PC (rtPC) MRI (4–13), which has several advantages. Firstly, rtPC is relatively insensitive to irregular heart rhythm and motion, enabling rapid scanning during free-breathing, and potentially reducing the need for general anesthesia in young pediatric patients (14). Secondly, as a real-time pulse sequence, it permits a beat-to-beat evaluation of hemodynamics, which may be useful for exercise stress testing, studying the impact of respiration on hemodynamics, and diagnosis of conditions such as cardiac tamponade (15). Thirdly, compared with standard 2D PC, which has a scan time of approximately 20 sec for breath-held scanning and 2 min for free-breathing scanning with averaging (9), rtPC can be as short as 2 heartbeats, where the first heartbeat is necessary for playing a dummy scan to achieve a steady-state of magnetization. Conventional rtPC, however, produces relatively low spatial and temporal resolution compared with clinical standard PC MRI, which may result in quantification error, particularly the peak velocity derived from a single voxel.

There are several techniques for accelerating rtPC, including echo-planar imaging (7,8), non-Cartesian sampling (9–12), parallel imaging (16,17), low-rank reconstruction (13), and Golden-Angle Radial Sparse Parallel (GRASP)(18), with each technique having advantages and disadvantages. It is very challenging for rtPC to match the 25 ms temporal resolution (40 fps frame rate) of transthoracic echocardiography (TTE), which is the first-line investigation for assessing blood flow (3). To realize 25 ms temporal resolution, we sought to achieve high acceleration by synergistically combining GRASP, view-sharing (VS)(19,20), and k-space weighted image contrast (KWIC) filtering (21). Our approach is similar to the vastly undersampled isotropic projection reconstruction (VIPR) technique (22) but amplified using GRASP and made more flexible with retrospective rebinning using golden angles (23).

The aims of this study were to develop a highly accelerated rtPC to achieve 25 ms effective temporal resolution (40 fps frame rate) and test our methods in phantom experiments and a cohort of pediatric patients with congenital heart disease (CHD).

Methods

Study population

This study was conducted in accordance with protocols approved by our institutional review board and was compliant with the Health Insurance Portability and Accountability Act (HIPAA). We retrospectively identified raw k-space data of 17 pediatric patients with congenital heart disease (10 males and 7 females, mean age = 11.1 ± 3.2 years). All subjects and/or guardians consented in writing to participate in a study comparing clinical standard cardiovascular MRI and rapid research MRI. Table 1 summarizes the baseline characteristics of the patients.

Table 1.

Demographics of pediatric CHD patients (N = 17). LVEF: left ventricular ejection fraction, RVEF: right ventricular ejection fraction, BAV: bicuspid aortic valve, s/p: status post.

Characteristic
Age	11.1 ± 3.2 years
Females	7/17 (41.2%)
Resting heart rate	80.3 ± 9.6 bpm
LVEF	55.8 ± 5.4%
RVEF	50.1 ± 8.7%
Tetralogy of Fallot s/p repair	5 (29.4%)
Aortopulmonary window s/p repair	1 (5.9%)
Mitral stenosis s/p valvuloplasty	1 (5.9%)
Tetralogy of Fallot and pulmonary atresia s/p repair	1 (5.9%)
Transposition of the great arteries and coarctation s/p arterial switch and coarctation repair	1 (5.9%)
Ehlers–Danlos	1 (5.9%)
Partial anomalous pulmonary venous return	2 (11.8%)
Ebstein anomaly	1 (5.9%)
Supravalvar pulmonary stenosis	1 (5.9%)
Pulmonary valve stenosis	1 (5.9%)
Transposition of the great arteries s/p arterial switch	1 (5.9%)
BAV and coarctation	1 (5.9%)

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For each patient, 2D PC MRI (clinical standard vs. real-time) was performed in up to four planes (aortic valve, pulmonary valve, left pulmonary artery, right pulmonary artery). In 10/17 (59%) patients who did not require general anesthesia, clinical 2D PC MRI was performed during free breathing for several minutes with averaging (either 2 or 3). In seven (41%) younger patients who required general anesthesia, clinical 2D PC MRI was performed during breath-holding by having the respirator suspended at end-expiration. In all patients, rtPC MRI was performed during free-breathing. As part of clinical routine, 12 (71%) patients received 0.15 mmol/kg of gadobutrol (Gadavist, Bayer HealthCare Pharmaceuticals, Whippany, New Jersey); three (18%) patients received 2 mg/kg of ferumoxytol (Feraheme, AMAG Pharmaceuticals, Waltham, Massachusetts); two (12%) patients did not receive any contrast agent. In all contrast-enhanced examinations, both clinical PC and research rtPC were performed after administration of contrast agent, and research rtPC was performed immediately after clinical PC.

Phantom experiment

A flow phantom (a U-shaped PVC pipe with inner size 21 mm representing a simplified aorta) was scanned using both clinical PC and rtPC to estimate the effective temporal resolution. A pneumatically driven ventricular assist device (VAD) controlled by a pressure pump control unit (MEDOS, Germany)(24) was attached to the flow phantom and generated pulsatile flow through the phantom at a frequency of 90 beats per minute. Water doped with gadolinium-based contrast agent was used as fluid in the experiment. ECG-gating was performed using a synchronized trigger signal generated by the pump control unit. The same spatial resolution (1.5 × 1.5 × 6 mm³) and similar temporal resolutions (29.16 ms for clinical PC and 25.5 ms for rtPC) were used in this experiment.

A T1MES phantom was scanned using both clinical PC and rtPC to estimate the effective spatial resolution (25). For more details on analysis, see “Image sharpness assessment” subsection.

MRI hardware

MRI was performed on one 1.5 T whole-body MRI scanner (MAGNETOM Aera, Siemens Healthineers, Erlangen, Germany) equipped with a gradient system capable of achieving a maximum gradient strength of 45 mT m⁻¹ and a maximum slew rate of 200 T m⁻¹ s⁻¹. A body coil was used for RF excitation, and standard body flex and spine coil arrays (30 elements) were used for signal reception.

Pulse sequence

Relevant imaging parameters for in-vivo clinical 2D PC MRI included: image acquisition matrix size varying from 176 × 132 (phase-encoding) to 256 × 208, field of view (FOV) varying from 180 mm × 135 mm (phase-encoding) to 300 mm × 224 mm (depending on patient size), spatial resolution varying from 1.1 × 1.1 mm² to 1.4 × 1.4 mm², slice thickness = 5 mm, receiver bandwidth = 445 Hz pixel⁻¹, flip angle = 20°, TE/TR = 2.45/4.8 ms, temporal resolution = 19.2–28.8 ms, Cartesian k-space sampling with GRAPPA (26) acceleration factor ( $R$ ) = 1.8, velocity encoding = 150–400 cm s⁻¹ (depending on valve/vessel type), k-space lines acquired per heartbeat ranged from 2 to 3.

Relevant imaging parameters for rtPC included: image acquisition matrix size = 192 × 192, FOV = 288 mm × 288 mm, spatial resolution = 1.5 × 1.5 mm², slice thickness = 6 mm, receiver bandwidth = 745 Hz pixel⁻¹, flip angle = 12°, TE/TR = 1.8/4.17 ms, temporal resolution = 25.02 ms, 3 native radial spokes per frame, free-breathing scan time = 2.75 s (835 ms of dummy scan + 1.92 s), velocity encoding = 150–400 cm s⁻¹. While ECG triggering was unnecessary for rtPC acquisition, we used prospective ECG triggering for accurate determination of the start and end of each cardiac cycle during analysis, and to save cardiac cine specific information such as trigger time in the Digital Imaging and Communications in Medicine (DICOM) header.

View-sharing and KWIC filtering

Figure 1 shows radial spokes used for reconstructing each time frame and corresponding k-space trajectories in the conventional GRASP reconstruction and the proposed GRASP with VS and KWIC filtering (or GRASP+VS+KWIC). In GRASP, only three native spokes are used to reconstruct each time frame, leaving a large part of k-space not sampled; this results in low spatial resolution. In the proposed GRASP+VS+KWIC, we used a symmetric VS scheme along time to borrow k-space samples from neighboring time frames, specifically the edges of k-space defining spatial resolution. A previous study reported that velocity-induced phase shifts are primarily encoded in the center of k-space (19). Conventional VS in radial k-space sampling causes “averaging” hemodynamics between the native spokes and shared spokes. We used KWIC filtering to minimize the influence of shared k-space lines from contributing to the center of k-space, which dominates the hemodynamic information. We applied a trapezoid-shaped KWIC filter to remove the central part of the shared spokes. We elected to use a trapezoid-shaped KWIC filter with the narrow base of three-point width, in order to account for gradient delays (i.e., imperfect centering of radial spokes). For additional details describing the preliminary experiments and results for optimizing the parameters for VS and KWIC, see Appendix and Supporting Information Figure S1 in Supplementary Materials.

Image reconstruction

The custom-made GRASP image reconstruction code was implemented in MATLAB (R2020b, MathWorks, Natick, Massachusetts) running on a Linux operating system (Ubuntu20.04 LTS) of a workstation (32 Xeon E5-2620 v4 384 GB memory, Intel, Santa Clara, CA, USA) equipped with two GPU cards (Tesla V100 GPU with 32 GB memory, NVIDIA, Santa Clara, California).

We performed and compared the conventional GRASP reconstruction and the proposed GRASP+VS+KWIC reconstruction on the same raw k-space data. In GRASP+VS+KWIC, VS and KWIC filtering were implemented as pre-processing steps. Otherwise, the remaining reconstruction pipeline was identical to GRASP. Velocity-compensated and velocity-encoded data were reconstructed separately.

During preprocessing, we first rearranged the radial spokes according to VS. We then applied self-calibrated gradient delay correction for all spokes using the radial intersections (RING) method (27). GPU-based non-uniform fast Fourier transform (gpuNUFFT) (28) was used to convert radial k-space data to image data in Cartesian coordinates. GpuNUFFT regridding for time-average images was performed with geometrically-derived density compensation (29). Time-average images were used to derive auto-calibrated coil sensitivity profiles using the method described by Walsh et al. (26). No density compensation was used in gpuNUFFT regridding for producing time-resolved, coil-combined, zero-filled images, which was the initialization for GRASP reconstruction (30). The KWIC filter was applied as a radial k-space sampling mask, which was another input to the GRASP pipeline. Other inputs were the multi-coil radial k-space data and coil sensitivity maps. Our GRASP algorithm used temporal total variation (TTV) as the sparsifying transform and nonlinear conjugate gradient with back-tracking line search as the optimization algorithm with 50 iterations. In the reconstruction, we solved for

\hat{x} = a r g m i n_{x} {‖F S x - y‖}_{2}^{2} + λ {‖T x‖}_{1},

where $F$ is the NUFFT operator, $S$ is the coil sensitivities, $x$ is the image series to be reconstructed, $y$ is the acquired k-space data, $T$ is the TTV operator, and $λ$ is the normalized regularization weight that balances the tradeoff between data consistency and sparsity terms. With a KWIC filter, $\nabla f (x)$ in the conjugate gradient method for solving GRASP reconstruction (30) was modified to $\nabla f (x) = 2 F^{*} (K W I C \times (F x - y)) + λ \nabla {‖T x‖}_{1}$ .

The normalized regularization weight $λ$ for TTV was selected separately for GRASP and GRASP+VS+KWIC, since the effective acceleration factor differs between them. We conducted a preliminary experiment on three training datasets to determine optimal regularization weights by sweeping over a range from 0.0005 to 0.01 to identify several regularization weights that achieve a good balance between visual assessment of aliasing artifacts (e.g., low variation in static tissue phase, as illustrated in Figure 2) and temporal fidelity of blood flow (see Figure 2). The static tissue phase variation is important for maintaining high velocity-to-noise ratio, particularly for slow flow regions. As shown in Figure 2, we determined that $λ = 10 e^{- 4} a n d λ = 5 e^{- 4}$ for GRASP and GRASP+VS+KWIC, respectively, produces a good balance between peak velocity accuracy and background aliasing artifact suppression.

Figure 2. — Empirical determination of optimal TTV weight ( $λ$ ) for GRASP alone (top half) and GRASP+VS+KWIC (bottom half). We swept through multiple $λ$ to identify an optimal point that achieves a good balance between peak velocity accuracy and background phase stability. Because the overall effective acceleration factor differs between GRASP alone and GRASP+VS+KWIC, the optimal $λ$ was determined separately. The images show one representative training case, whereas the curves show averaged results across three training cases. RT: real-time. TTV: temporal total variation. STD: standard deviation.

Image sharpness assessment

From the T1MES images, fifteen contiguous intensity profiles were measured across one sharp edge of a tube and then averaged. The edge width, defined as the distance between the 25^th and 75^th percentiles of the maximum intensity value, was measured. To improve the precision, we interpolated each intensity profile by a factor of 150 using linear interpolation (see Figure 3).

Figure 3. — The stationary T1MES phantom experiment to estimate the effective spatial resolution of clinical PC, GRASP alone, and GRASP+VS+KWIC. All three images had identical nominal spatial resolution. Fifteen contiguous intensity profiles (see red on clinical image) were averaged to calculate the edge width, as shown. The measured edge width from clinical PC was 1.20 mm, from GRASP was 3.47 mm, and from GRASP+VS+KWIC was 1.22 mm.

For in-vivo assessment, we calculated the blur metric (0–1: sharp-blur)(31) to quantify the overall image sharpness of GRASP and GRASP+VS+KWIC reconstruction in magnitude images from one heartbeat for each plane.

Phase unwrapping and background phase correction

We applied the ROMEO technique (32) to unwrap phase aliasing as needed. For all clinical PC and rtPC images, background phase was corrected using the method described by Walker et al. (33). We used first-order fitting for clinical 2D PC sampled with Cartesian k-space sampling, whereas second-order fitting for rtPC to account for the nonlinear phase offsets induced by non-Cartesian k-space sampling.

Velocity and volume quantification

For analysis of velocity and volume from rtPC data, we used the ECG time stamp information to extract one full cardiac cycle. For both clinical PC and rtPC, ROIs were contoured manually with custom software written in MATLAB. For fair comparisons, the same set of ROI masks was used for GRASP and GRASP+VS+KWIC.

Peak velocities at peak systole, forward and backward volumes through one full cardiac cycle, and regurgitation fraction were measured for both clinical PC and rtPC. The peak velocity was defined as the 95-percentile, instead of the maximum, of all velocities within the ROI at peak systole to avoid inaccuracies caused by spurious voxels.

To evaluate the ability of rtPC for assessing beat-to-beat variations, we used the semi-automatic tools in Circle cvi42 (v5.14, Circle Cardiovascular Imaging, Calgary, Canada) to contour the ROIs in the time frames corresponding to peak systole for all cardiac cycles and all cases. Variations in peak velocity at peak systole across different cardiac cycles were analyzed. In one case, ROIs were contoured in all time frames with cvi42 to visualize the change of peak velocity with time.

Statistical analysis

The statistical analyses were conducted by one investigator (HY). We tested for variable normality using the Shapiro-Wilk test. Bland-Altman and linear regression analyses were performed on peak velocity, forward volume, backward volume, and regurgitation fraction values to assess the level of agreement and correlation between clinical PC and each reconstruction method for rtPC. One-way analysis of variation (Kruskal-Wallis if not normally distributed) with Bonferroni correction was conducted to detect any significant differences among results from clinical PC and reconstruction methods of rtPC. A P-value < 0.05 was considered statistically significant for all tests performed.

Results

The dataset from one patient was excluded due to a mismatch in imaging plane between rtPC and clinical PC. The total number of planes used in this study was $N = 58$ .

According to the Shapiro-Wilk test, the blur metric values were normally distributed (P > 0.51) while all hemodynamic parameters for clinical PC and rtPC were not normally distributed (P < 0.04). Thus, we used parametric statistical tests (one-way analysis of variation) for blur metric and non-parametric statistical tests (Kruskal-Wallis) for hemodynamic parameters.

The mean reconstruction time was 16.6 sec/frame for GRASP and 22.7 sec/frame for GRASP+VS+KWIC.

Image sharpness

Figure 3 shows images of the T1MES phantom tubes, the average normalized intensity profiles, and the measured edge widths from clinical PC, GRASP, and GRASP+VS+KWIC rtPC. The regularization weights were the same as in-vivo conditions. GRASP+VS+KWIC produced better image sharpness than GRASP. The edge width was 1.20, 1.22 (1.7% higher than clinical PC), and 3.47 mm (189.2% higher than clinical PC) for clinical PC, GRASP+VS+KWIC, and GRASP, respectively.

Figure 4 and Figure 5 show representative rtPC magnitude and phase images from the flow phantom experiment and from in-vivo data, respectively. As shown in Figure 4, GRASP reconstruction resulted in phantom structures to appear larger than the true size due to blurring, whereas GRASP+VS+KWIC reconstruction produced similar size as the clinical PC. As shown in Figure 5, GRASP+VS+KWIC provides shaper in-vivo images compared with GRASP. For dynamic display of images in Figure 4 and Figure 5, see Supporting Information Video S1–3.

For in-vivo magnitude images, the mean blur metric (0 [best] to 1 [worst]) was significantly (P < 0.001) lower (i.e., better) for GRASP+VS+KWIC (0.314 ± 0.045) than GRASP (0.450 ± 0.056), which was consistent with the qualitative assessment.

Velocity and volume quantification

In the flow phantom experiment (see Figure 4), where the overall sparsity is considerably higher than in vivo condition and the spatial resolution is matched between clinical PC and rtPC, the underestimation in peak velocity was relatively low for both GRASP (5.4%) and GRASP+VS+KWIC (8.1%). In contrast, in patients (see Figure 5), where the overall sparsity is considerably lower than flow phantoms and the spatial resolution is higher for clinical PC than rtPC, the underestimation in peak velocity was lower for GRASP+VS+KWIC than GRASP. Both GRASP and GRASP+VS+KWIC achieved good agreement with clinical PC in flow measurements, which are less influenced by spatial resolution.

Figure 6 shows scatter plots illustrating the linear regression analysis on hemodynamic parameters. For peak velocity, GRASP+VS+KWIC was strongly correlated with clinical PC (the coefficient of determination [ $R^{2}$ ] = 0.71), whereas GRASP had moderate correlation with clinical PC ( $R^{2}$ = 0.55). For forward volume, backward volume, and regurgitant fraction, GRASP+VS+KWIC and GRASP had similarly strong correlation with clinical PC ( $R^{2}$ ≥ 0.88).

Figure 7 shows scatter plots illustrating the Bland-Altman analysis on hemodynamic parameters. For peak velocity, GRASP+VS+KWIC achieved better agreement with clinical PC (mean difference = −7.8 cm sec⁻¹ [−6.4% relative to mean]; limit of agreement (LOA) = 86.5 cm sec⁻¹ [70.5% relative to mean]) than GRASP (mean difference = −21.3 cm sec⁻¹ [−17.4% relative to mean]; LOA = 107.3 cm sec⁻¹ [87.4% relative to mean]). For forward volume, backward volume, and regurgitant fraction, GRASP+VS+KWIC and GRASP achieved similar agreement with clinical PC.

According to the Kruskal-Wallis test, GRASP produced significantly lower peak velocity than clinical PC (P < 0.007), whereas all other pair-wise comparisons of hemodynamic parameters were not significantly different (P > 0.17).

Beat-to-beat variation

Figure 8 demonstrates the ability of rtPC with proposed GRASP+VS+KWIC reconstruction for assessing beat-to-beat variations. Summarizing the results over 17 patients per vessel (see Table 2), the mean peak velocity from clinical PC was 124.0 ± 33.5 cm/s, 127.2 ± 58.2 cm/s, 111.8 ± 26.8 cm/s, and 126.8 ± 39.6 cm/s for the aortic valve, pulmonary valve, left pulmonary artery, and right pulmonary artery, respectively; the corresponding mean maximum peak velocity from rtPC with proposed GRASP+VS+KWIC was 120.5 ± 33.1 cm/s, 128.1 ± 56.3 cm/s, 122.3 ± 36.0 cm/s, and 120.1 ± 32.4 cm/s, respectively; the corresponding mean medium peak velocity from rtPC with proposed GRASP+VS+KWIC was 116.1 ± 34.2 cm/s, 119.8 ± 52.8 cm/s, 112.1 ± 36.4 cm/s, and 110.7 ± 27.9 cm/s, respectively.

Table 2.

Peak velocity results per vessel over 17 patients from clinical PC and from rtPC with GRASP+VS+KWIC reconstruction. Unit: cm/s. The middle and right columns show the mean maximum and mean median peak velocity values across multiple heartbeats in rtPC, respectively. AV: aortic valve, PV: pulmonary valve, LPA: left pulmonary artery, RPA: right pulmonary artery.

Imaging Plane	Clinical	RtPC (maximum)	RtPC (median)
AV	124.0 ± 33.5	120.5 ± 33.1	116.1 ± 34.2
PV	127.2 ± 58.2	128.1 ± 56.3	119.8 ± 52.8
LPA	111.8 ± 26.8	122.3 ± 36.0	112.1 ± 36.4
RPA	126.8 ± 39.6	120.1 ± 32.4	110.7 ± 27.9

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Discussion

This study describes our solution to achieve high spatial and temporal resolution in rtPC MRI. We incorporated VS and KWIC filtering into GRASP to achieve 25 ms effective temporal resolution (40 fps frame rate) and 1.5 × 1.5 mm² spatial resolution. Our solution provides a means to address the challenges associated with pediatric cardiovascular MRI.

Our rtPC compares favorably with other 2D rtPC MRI pulse sequences (6–10,34,35). Kowalik et al. (14) combined perturbed spiral k-space sampling with compressed sensing and achieved 1.76 × 1.76 mm² spatial resolution, 26.6 ms temporal resolution, and acceleration rate of 18. In comparison, our method is 17.3% better in spatial resolution and 6.4% better in temporal resolution. Compared with radial k-space sampling, spiral k-space sampling is more susceptible to geometric distortion due to longer readout. Compared with the Cartesian k-space sampling method proposed by Sun et al. (13) with 1.8 × 1.8 mm² spatial resolution and 18 ms temporal resolution, our method is 20% better in spatial resolution and 38.9% worse in temporal resolution. The method by Sun et al. (13) is sensitive to potential mismatch between the training and imaging data for temporal interpolation, whereas our method does not rely on training data. Compared with the model-based radial k-space sampling method proposed by Tan et al. (36) with 1.5 × 1.5 mm² spatial resolution and 25.6 ms temporal resolution, our method provides similar spatio-temporal resolution without special assumptions or approximations required for model development. In these prior studies, the temporal resolution was fixed during acquisition, whereas in our study the golden angle sampling scheme enabled retrospective rebinning for arbitrary or patient-specific temporal resolution based on heart rate. Compared with two previous studies performed on adult subjects (13,36), our study was validated on pediatric CHD patients, which is a more challenging clinical context due to higher heart rates and smaller patient size. Compared with previous studies examining only the aortic flow (13,14,36), we examined more challenging planes such as the pulmonary valve and pulmonary arteries. A direct head-to-head comparison study involving multiple developers and/or vendors is warranted to evaluate the relative accuracies in a pediatric patient cohort.

We systematically tested different levels of VS and KWIC filtering. Sharing a sufficient number of radial spokes improved accuracy in velocity measurements, but sharing too many radial spokes worsened the accuracy compared with no VS. This was due to the “averaging” effect from all the spokes contributing equally to the central part of k-space, in effect worsening temporal resolution. KWIC filtering mitigated the “averaging” effect from shared k-space lines and retained high accuracy, but excessively large KWIC filters resulted in reduced accuracy. We optimized the level of VS and the shape of KWIC filtering to maintain high accuracy in peak velocity, since it is influenced by spatial resolution.

We demonstrated the ability of rtPC with GRASP+VS+KWIC reconstruction to assess beat-to-beat variation in hemodynamics. Clinical scenarios in which beat-to-beat variations would be meaningful include, cardiac rhythm disorders, exercise stress testing, and Valsalva maneuver. This feature afforded by rtPC is not available in clinical PC due to the averaging nature with segmented k-space imaging.

Our study has several limitations. Firstly, our sample size was insufficient for adjusting for factors such as age, sex, CHD type, contrast agent, intrathoracic pressure (breath-hold or free-breathing in clinical PC), general anesthesia, and imaging plane. Secondly, the impact of heart rate on the accuracy of hemodynamic measurements was not investigated. The proposed technique enables retrospective adjustment of temporal resolution that could be personalized according to heart rate. Theoretically, a higher heart rate could benefit more from better temporal resolution. A future study involving more subjects with a wider range of heart rates is warranted to verify the benefit of patient specific temporal resolution. Thirdly, the ability to assess beat-to-beat variations was not fully explored, because in this study we focused on improving the spatial and temporal resolution of rtPC MRI and the accuracy of peak velocity measurements in pediatric CHD patients. Future studies will investigate beat-to-beat variations in patients with arrhythmias. Fourthly, the time-consuming nature of reconstruction based on GRASP was not addressed. The term “real-time” in this study refers to “single-shot” sampling of all requisite k-space lines per frame (i.e., no repetition)(37); it does not indicate “real-time” inline display of images with low latency (38). Several studies (39,40) have used deep learning to shorten the reconstruction time in rtPC MRI. A future study is warranted to apply deep learning to accelerate the reconstruction of our rtPC images, which may support “real-time” inline display of image with low latency.

In conclusion, the synergistic combination of GRASP, VS, and KWIC achieves 25 ms effective temporal resolution (40 fps frame rate), while minimizing the underestimation of peak velocity compared with conventional GRASP.

Supplementary Material

Video S2

Supporting Video S2. Dynamic display of the representative aortic valve images shown in Figure 5A. Left: clinical PC, middle: rtPC with GRASP alone, right: rtPC with GRASP+VS+KWIC. Clinical PC was resampled through space and time to match rtPC.

Download video file^{(502.4KB, mp4)}

Video S3

Supporting Video S3. Dynamic display of the representative pulmonary valve images shown in Figure 5B. Left: clinical PC, middle: rtPC with GRASP alone, right: rtPC with GRASP+VS+KWIC. Clinical PC was resampled through space and time to match rtPC.

Download video file^{(526.8KB, mp4)}

Video S1

Supporting Video S1. Dynamic display of the flow phantom images shown in Figure 4. Left: clinical PC, middle: rtPC with GRASP alone, right: rtPC with GRASP+VS+KWIC. Clinical PC was resampled through time to match rtPC.

Download video file^{(1MB, mp4)}

Supinfo

Supporting Figure S1. The impact of VS and KWIC filtering on measured peak velocities. Three aortic planes were included in the analysis. The number of shared radial spokes is denoted as “a” and the size of the wide KWIC base is denoted as “b” as shown in the diagram of radial spokes (A). Left plots show the averaged peak velocity curves in one cardiac cycle using clinical PC and rtPC with 1,3,5,7 shared spokes on each side of the native spokes (B) or with KWIC sizes of 32, 64, 96, 128 data points (D). Right plots show the measured peak velocities at peak systole using rtPC with varying numbers of shared spokes (C) or KWIC size (E). Each radial spoke contained 192 k-space data points and a size of 3 data points was used for the narrow KWIC base. RT: real-time.

NIHMS1948240-supplement-Supinfo.docx^{(5.9MB, docx)}

Acknowledgements

This work was partially supported by funding from the National Institutes of Health (R01HL116895, R01HL151079, R21EB030806, R01HL167148, F31H165915), the Radiological Society of North America (EILTC2302), and the American Heart Association (949899, 19IPLOI34760317, 23PRE1027440 https://doi.org/10.58275/AHA.23PRE1027440.pc.gr.161163).

Grant Support:

the National Institutes of Health (R01HL116895, R21EB030806, R01HL151079, R01HL167148, F31H165915); the American Heart Association (19IPLOI34760317, 949899, 23PRE1027440); the Radiological Society of North America (EILTC2302)

Footnotes

None of the authors have relationships with industry related to this study

Data availability statement

The reconstruction code used in this study is available on GitHub (https://github.com/yangh7/RtPC-Radial-GRASP-VS-KWIC.git).

References

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2.Gatehouse PD, Keegan J, Crowe LA, Masood S, Mohiaddin RH, Kreitner KF, Firmin DN. Applications of phase-contrast flow and velocity imaging in cardiovascular MRI. Eur Radiol 2005;15(10):2172–2184. [DOI] [PubMed] [Google Scholar]
3.Cawley PJ, Hamilton-Craig C, Owens DS, Krieger EV, Strugnell WE, Mitsumori L, D’Jang CL, Schwaegler RG, Nguyen KQ, Nguyen B, Maki JH, Otto CM. Prospective comparison of valve regurgitation quantitation by cardiac magnetic resonance imaging and transthoracic echocardiography. Circ Cardiovasc Imaging 2013;6(1):48–57. [DOI] [PubMed] [Google Scholar]
4.Riederer SJ, Wright RC, Ehman RL, Rossman PJ, Holsinger-Bampton AE, Hangiandreou NJ, Grimm RC. Real-time interactive color flow MR imaging. Radiology 1991;181(1):33–39. [DOI] [PubMed] [Google Scholar]
5.Liu C-Y, Varadarajan P, Pohost GM, Nayak KS. Real-time color-flow MRI at 3 T using variable-density spiral phase contrast. Magnetic resonance imaging 2008;26(5):661–666. [DOI] [PubMed] [Google Scholar]
6.Joseph AA, Merboldt KD, Voit D, Zhang S, Uecker M, Lotz J, Frahm J. Real-time phase-contrast MRI of cardiovascular blood flow using undersampled radial fast low-angle shot and nonlinear inverse reconstruction. NMR Biomed 2012;25(7):917–924. [DOI] [PubMed] [Google Scholar]
7.Traber J, Wurche L, Dieringer MA, Utz W, von Knobelsdorff-Brenkenhoff F, Greiser A, Jin N, Schulz-Menger J. Real-time phase contrast magnetic resonance imaging for assessment of haemodynamics: from phantom to patients. Eur Radiol 2016;26(4):986–996. [DOI] [PubMed] [Google Scholar]
8.Lin HY, Bender JA, Ding Y, Chung YC, Hinton AM, Pennell ML, Whitehead KK, Raman SV, Simonetti OP. Shared velocity encoding: a method to improve the temporal resolution of phase-contrast velocity measurements. Magn Reson Med 2012;68(3):703–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Haji-Valizadeh H, Feng L, Ma LE, Shen D, Block KT, Robinson JD, Markl M, Rigsby CK, Kim D. Highly accelerated, real-time phase-contrast MRI using radial k-space sampling and GROG-GRASP reconstruction: a feasibility study in pediatric patients with congenital heart disease. NMR in biomedicine 2020;33(5):e4240. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Joseph A, Kowallick JT, Merboldt KD, Voit D, Schaetz S, Zhang S, Sohns JM, Lotz J, Frahm J. Real‐time flow MRI of the aorta at a resolution of 40 msec. Journal of Magnetic Resonance Imaging 2014;40(1):206–213. [DOI] [PubMed] [Google Scholar]
11.Nezafat R, Kellman P, Derbyshire JA, McVeigh ER. Real-time blood flow imaging using autocalibrated spiral sensitivity encoding. Magn Reson Med 2005;54(6):1557–1561. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Nayak KS, Pauly JM, Kerr AB, Hu BS, Nishimura DG. Real-time color flow MRI. Magn Reson Med 2000;43(2):251–258. [DOI] [PubMed] [Google Scholar]
13.Sun A, Zhao B, Li Y, He Q, Li R, Yuan C. Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 2017;19(1):19. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kowalik GT, Knight D, Steeden JA, Muthurangu V. Perturbed spiral real-time phase-contrast MR with compressive sensing reconstruction for assessment of flow in children. Magn Reson Med 2020;83(6):2077–2091. [DOI] [PubMed] [Google Scholar]
15.Perez-Casares A, Cesar S, Brunet-Garcia L, Sanchez-de-Toledo J. Echocardiographic Evaluation of Pericardial Effusion and Cardiac Tamponade. Front Pediatr 2017;5:79. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47(6):1202–1210. [DOI] [PubMed] [Google Scholar]
17.Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42(5):952–962. [PubMed] [Google Scholar]
18.Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 2014;72(3):707–717. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Markl M, Hennig J. Phase contrast MRI with improved temporal resolution by view sharing: k-space related velocity mapping properties. Magn Reson Imaging 2001;19(5):669–676. [DOI] [PubMed] [Google Scholar]
20.Levine E, Daniel B, Vasanawala S, Hargreaves B, Saranathan M. 3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling. Magn Reson Med 2017;77(5):1774–1785. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Song HK, Dougherty L. k-space weighted image contrast (KWIC) for contrast manipulation in projection reconstruction MRI. Magn Reson Med 2000;44(6):825–832. [DOI] [PubMed] [Google Scholar]
22.Barger AV, Block WF, Toropov Y, Grist TM, Mistretta CA. Time-resolved contrast-enhanced imaging with isotropic resolution and broad coverage using an undersampled 3D projection trajectory. Magn Reson Med 2002;48(2):297–305. [DOI] [PubMed] [Google Scholar]
23.Wundrak S, Paul J, Ulrici J, Hell E, Rasche V. A Small Surrogate for the Golden Angle in Time-Resolved Radial MRI Based on Generalized Fibonacci Sequences. IEEE Trans Med Imaging 2015;34(6):1262–1269. [DOI] [PubMed] [Google Scholar]
24.Lorenz R, Benk C, Bock J, Stalder AF, Korvink JG, Hennig J, Markl M. Closed circuit MR compatible pulsatile pump system using a ventricular assist device and pressure control unit. Magn Reson Med 2012;67(1):258–268. [DOI] [PubMed] [Google Scholar]
25.Captur G, Gatehouse P, Keenan KE, Heslinga FG, Bruehl R, Prothmann M, Graves MJ, Eames RJ, Torlasco C, Benedetti G, Donovan J, Ittermann B, Boubertakh R, Bathgate A, Royet C, Pang W, Nezafat R, Salerno M, Kellman P, Moon JC. A medical device-grade T1 and ECV phantom for global T1 mapping quality assurance-the T(1) Mapping and ECV Standardization in cardiovascular magnetic resonance (T1MES) program. J Cardiovasc Magn Reson 2016;18(1):58. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43(5):682–690. [DOI] [PubMed] [Google Scholar]
27.Rosenzweig S, Holme HCM, Uecker M. Simple auto-calibrated gradient delay estimation from few spokes using Radial Intersections (RING). Magn Reson Med 2019;81(3):1898–1906. [DOI] [PubMed] [Google Scholar]
28.Knoll F, Schwarzl A, Diwoky C, Sodickson DK. gpuNUFFT - An open source GPU library for 3D regridding with direct Matlab interface 2014.
29.Hong K, Schiffers F, DiCarlo AL, Rigsby CK, Haji-Valizadeh H, Lee DC, Markl M, Katsaggelos AK, Kim D. Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation. Phys Med Biol 2021;66(21). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58(6):1182–1195. [DOI] [PubMed] [Google Scholar]
31.Crete F, Dolmiere T, Ladret P, Nicolas M. The blur effect: perception and estimation with a new no-reference perceptual blur metric 2007. SPIE. p 196–206.
32.Dymerska B, Eckstein K, Bachrata B, Siow B, Trattnig S, Shmueli K, Robinson SD. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magn Reson Med 2021;85(4):2294–2308. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Walker PG, Cranney GB, Scheidegger MB, Waseleski G, Pohost GM, Yoganathan AP. Semiautomated method for noise reduction and background phase error correction in MR phase velocity data. J Magn Reson Imaging 1993;3(3):521–530. [DOI] [PubMed] [Google Scholar]
34.Untenberger M, Tan Z, Voit D, Joseph AA, Roeloffs V, Merboldt KD, Schatz S, Frahm J. Advances in real-time phase-contrast flow MRI using asymmetric radial gradient echoes. Magn Reson Med 2016;75(5):1901–1908. [DOI] [PubMed] [Google Scholar]
35.Kowalik GT, Steeden JA, Pandya B, Odille F, Atkinson D, Taylor A, Muthurangu V. Real-time flow with fast GPU reconstruction for continuous assessment of cardiac output. J Magn Reson Imaging 2012;36(6):1477–1482. [DOI] [PubMed] [Google Scholar]
36.Tan Z, Roeloffs V, Voit D, Joseph AA, Untenberger M, Merboldt KD, Frahm J. Model-based reconstruction for real-time phase-contrast flow MRI: Improved spatiotemporal accuracy. Magn Reson Med 2017;77(3):1082–1093. [DOI] [PubMed] [Google Scholar]
37.Nayak KS. Response to Letter to the Editor: “Nomenclature for real-time magnetic resonance imaging”. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 2019;82(2):525–526. [DOI] [PubMed] [Google Scholar]
38.Dietz B, Fallone BG, Wachowicz K. Nomenclature for real-time magnetic resonance imaging. Magn Reson Med 2019;81(3):1483–1484. [DOI] [PubMed] [Google Scholar]
39.Haji-Valizadeh H, Guo R, Kucukseymen S, Paskavitz A, Cai X, Rodriguez J, Pierce P, Goddu B, Kim D, Manning W, Nezafat R. Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning. Magn Reson Med 2021;86(2):804–819. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Oscanoa JA, Middione MJ, Syed AB, Sandino CM, Vasanawala SS, Ennis DB. Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation. Magn Reson Med 2023;89(1):356–369. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Video S2

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Video S3

Download video file^{(526.8KB, mp4)}

Video S1

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Supinfo

NIHMS1948240-supplement-Supinfo.docx^{(5.9MB, docx)}

Data Availability Statement

The reconstruction code used in this study is available on GitHub (https://github.com/yangh7/RtPC-Radial-GRASP-VS-KWIC.git).

[R1] 1.Pelc NJ, Herfkens RJ, Shimakawa A, Enzmann DR. Phase contrast cine magnetic resonance imaging. Magnetic resonance quarterly 1991;7(4):229–254. [PubMed] [Google Scholar]

[R2] 2.Gatehouse PD, Keegan J, Crowe LA, Masood S, Mohiaddin RH, Kreitner KF, Firmin DN. Applications of phase-contrast flow and velocity imaging in cardiovascular MRI. Eur Radiol 2005;15(10):2172–2184. [DOI] [PubMed] [Google Scholar]

[R3] 3.Cawley PJ, Hamilton-Craig C, Owens DS, Krieger EV, Strugnell WE, Mitsumori L, D’Jang CL, Schwaegler RG, Nguyen KQ, Nguyen B, Maki JH, Otto CM. Prospective comparison of valve regurgitation quantitation by cardiac magnetic resonance imaging and transthoracic echocardiography. Circ Cardiovasc Imaging 2013;6(1):48–57. [DOI] [PubMed] [Google Scholar]

[R4] 4.Riederer SJ, Wright RC, Ehman RL, Rossman PJ, Holsinger-Bampton AE, Hangiandreou NJ, Grimm RC. Real-time interactive color flow MR imaging. Radiology 1991;181(1):33–39. [DOI] [PubMed] [Google Scholar]

[R5] 5.Liu C-Y, Varadarajan P, Pohost GM, Nayak KS. Real-time color-flow MRI at 3 T using variable-density spiral phase contrast. Magnetic resonance imaging 2008;26(5):661–666. [DOI] [PubMed] [Google Scholar]

[R6] 6.Joseph AA, Merboldt KD, Voit D, Zhang S, Uecker M, Lotz J, Frahm J. Real-time phase-contrast MRI of cardiovascular blood flow using undersampled radial fast low-angle shot and nonlinear inverse reconstruction. NMR Biomed 2012;25(7):917–924. [DOI] [PubMed] [Google Scholar]

[R7] 7.Traber J, Wurche L, Dieringer MA, Utz W, von Knobelsdorff-Brenkenhoff F, Greiser A, Jin N, Schulz-Menger J. Real-time phase contrast magnetic resonance imaging for assessment of haemodynamics: from phantom to patients. Eur Radiol 2016;26(4):986–996. [DOI] [PubMed] [Google Scholar]

[R8] 8.Lin HY, Bender JA, Ding Y, Chung YC, Hinton AM, Pennell ML, Whitehead KK, Raman SV, Simonetti OP. Shared velocity encoding: a method to improve the temporal resolution of phase-contrast velocity measurements. Magn Reson Med 2012;68(3):703–710. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Haji-Valizadeh H, Feng L, Ma LE, Shen D, Block KT, Robinson JD, Markl M, Rigsby CK, Kim D. Highly accelerated, real-time phase-contrast MRI using radial k-space sampling and GROG-GRASP reconstruction: a feasibility study in pediatric patients with congenital heart disease. NMR in biomedicine 2020;33(5):e4240. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Joseph A, Kowallick JT, Merboldt KD, Voit D, Schaetz S, Zhang S, Sohns JM, Lotz J, Frahm J. Real‐time flow MRI of the aorta at a resolution of 40 msec. Journal of Magnetic Resonance Imaging 2014;40(1):206–213. [DOI] [PubMed] [Google Scholar]

[R11] 11.Nezafat R, Kellman P, Derbyshire JA, McVeigh ER. Real-time blood flow imaging using autocalibrated spiral sensitivity encoding. Magn Reson Med 2005;54(6):1557–1561. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Nayak KS, Pauly JM, Kerr AB, Hu BS, Nishimura DG. Real-time color flow MRI. Magn Reson Med 2000;43(2):251–258. [DOI] [PubMed] [Google Scholar]

[R13] 13.Sun A, Zhao B, Li Y, He Q, Li R, Yuan C. Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 2017;19(1):19. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Kowalik GT, Knight D, Steeden JA, Muthurangu V. Perturbed spiral real-time phase-contrast MR with compressive sensing reconstruction for assessment of flow in children. Magn Reson Med 2020;83(6):2077–2091. [DOI] [PubMed] [Google Scholar]

[R15] 15.Perez-Casares A, Cesar S, Brunet-Garcia L, Sanchez-de-Toledo J. Echocardiographic Evaluation of Pericardial Effusion and Cardiac Tamponade. Front Pediatr 2017;5:79. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47(6):1202–1210. [DOI] [PubMed] [Google Scholar]

[R17] 17.Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42(5):952–962. [PubMed] [Google Scholar]

[R18] 18.Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 2014;72(3):707–717. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Markl M, Hennig J. Phase contrast MRI with improved temporal resolution by view sharing: k-space related velocity mapping properties. Magn Reson Imaging 2001;19(5):669–676. [DOI] [PubMed] [Google Scholar]

[R20] 20.Levine E, Daniel B, Vasanawala S, Hargreaves B, Saranathan M. 3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling. Magn Reson Med 2017;77(5):1774–1785. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Song HK, Dougherty L. k-space weighted image contrast (KWIC) for contrast manipulation in projection reconstruction MRI. Magn Reson Med 2000;44(6):825–832. [DOI] [PubMed] [Google Scholar]

[R22] 22.Barger AV, Block WF, Toropov Y, Grist TM, Mistretta CA. Time-resolved contrast-enhanced imaging with isotropic resolution and broad coverage using an undersampled 3D projection trajectory. Magn Reson Med 2002;48(2):297–305. [DOI] [PubMed] [Google Scholar]

[R23] 23.Wundrak S, Paul J, Ulrici J, Hell E, Rasche V. A Small Surrogate for the Golden Angle in Time-Resolved Radial MRI Based on Generalized Fibonacci Sequences. IEEE Trans Med Imaging 2015;34(6):1262–1269. [DOI] [PubMed] [Google Scholar]

[R24] 24.Lorenz R, Benk C, Bock J, Stalder AF, Korvink JG, Hennig J, Markl M. Closed circuit MR compatible pulsatile pump system using a ventricular assist device and pressure control unit. Magn Reson Med 2012;67(1):258–268. [DOI] [PubMed] [Google Scholar]

[R25] 25.Captur G, Gatehouse P, Keenan KE, Heslinga FG, Bruehl R, Prothmann M, Graves MJ, Eames RJ, Torlasco C, Benedetti G, Donovan J, Ittermann B, Boubertakh R, Bathgate A, Royet C, Pang W, Nezafat R, Salerno M, Kellman P, Moon JC. A medical device-grade T1 and ECV phantom for global T1 mapping quality assurance-the T(1) Mapping and ECV Standardization in cardiovascular magnetic resonance (T1MES) program. J Cardiovasc Magn Reson 2016;18(1):58. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43(5):682–690. [DOI] [PubMed] [Google Scholar]

[R27] 27.Rosenzweig S, Holme HCM, Uecker M. Simple auto-calibrated gradient delay estimation from few spokes using Radial Intersections (RING). Magn Reson Med 2019;81(3):1898–1906. [DOI] [PubMed] [Google Scholar]

[R28] 28.Knoll F, Schwarzl A, Diwoky C, Sodickson DK. gpuNUFFT - An open source GPU library for 3D regridding with direct Matlab interface 2014.

[R29] 29.Hong K, Schiffers F, DiCarlo AL, Rigsby CK, Haji-Valizadeh H, Lee DC, Markl M, Katsaggelos AK, Kim D. Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation. Phys Med Biol 2021;66(21). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58(6):1182–1195. [DOI] [PubMed] [Google Scholar]

[R31] 31.Crete F, Dolmiere T, Ladret P, Nicolas M. The blur effect: perception and estimation with a new no-reference perceptual blur metric 2007. SPIE. p 196–206.

[R32] 32.Dymerska B, Eckstein K, Bachrata B, Siow B, Trattnig S, Shmueli K, Robinson SD. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magn Reson Med 2021;85(4):2294–2308. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Walker PG, Cranney GB, Scheidegger MB, Waseleski G, Pohost GM, Yoganathan AP. Semiautomated method for noise reduction and background phase error correction in MR phase velocity data. J Magn Reson Imaging 1993;3(3):521–530. [DOI] [PubMed] [Google Scholar]

[R34] 34.Untenberger M, Tan Z, Voit D, Joseph AA, Roeloffs V, Merboldt KD, Schatz S, Frahm J. Advances in real-time phase-contrast flow MRI using asymmetric radial gradient echoes. Magn Reson Med 2016;75(5):1901–1908. [DOI] [PubMed] [Google Scholar]

[R35] 35.Kowalik GT, Steeden JA, Pandya B, Odille F, Atkinson D, Taylor A, Muthurangu V. Real-time flow with fast GPU reconstruction for continuous assessment of cardiac output. J Magn Reson Imaging 2012;36(6):1477–1482. [DOI] [PubMed] [Google Scholar]

[R36] 36.Tan Z, Roeloffs V, Voit D, Joseph AA, Untenberger M, Merboldt KD, Frahm J. Model-based reconstruction for real-time phase-contrast flow MRI: Improved spatiotemporal accuracy. Magn Reson Med 2017;77(3):1082–1093. [DOI] [PubMed] [Google Scholar]

[R37] 37.Nayak KS. Response to Letter to the Editor: “Nomenclature for real-time magnetic resonance imaging”. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 2019;82(2):525–526. [DOI] [PubMed] [Google Scholar]

[R38] 38.Dietz B, Fallone BG, Wachowicz K. Nomenclature for real-time magnetic resonance imaging. Magn Reson Med 2019;81(3):1483–1484. [DOI] [PubMed] [Google Scholar]

[R39] 39.Haji-Valizadeh H, Guo R, Kucukseymen S, Paskavitz A, Cai X, Rodriguez J, Pierce P, Goddu B, Kim D, Manning W, Nezafat R. Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning. Magn Reson Med 2021;86(2):804–819. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Oscanoa JA, Middione MJ, Syed AB, Sandino CM, Vasanawala SS, Ennis DB. Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation. Magn Reson Med 2023;89(1):356–369. [DOI] [PubMed] [Google Scholar]

PERMALINK

GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: a preliminary evaluation in pediatric patients with congenital heart disease

Huili Yang

KyungPyo Hong

Justin Baraboo

Lexiaozi Fan

Andrine Larsen

Michael Markl

Joshua D Robinson

Cynthia K Rigsby

Daniel Kim

Abstract

Purpose:

Methods:

Results:

Conclusion:

Introduction

Methods

Study population

Table 1.

Phantom experiment

MRI hardware

Pulse sequence

View-sharing and KWIC filtering

Figure 1.

Image reconstruction

Figure 2.

Image sharpness assessment

Figure 3.

Phase unwrapping and background phase correction

Velocity and volume quantification

Statistical analysis

Results

Image sharpness

Figure 4.

Figure 5.

Velocity and volume quantification

Figure 6.

Figure 7.

Beat-to-beat variation

Figure 8.

Table 2.

Discussion

Supplementary Material

Acknowledgements

Grant Support:

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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