One Frame, Full Heartbeat: ECG-Free Cardiac Cine MRI Synthesis via Phase-Conditioned Flow Matching

Shiyi Wang; Ruochen Sun; Peirong Liu; Xiang Li; Fangxu Xing

cs.CVOct 7, 2026

One Frame, Full Heartbeat: ECG-Free Cardiac Cine MRI Synthesis via Phase-Conditioned Flow Matching

Authors: Shiyi Wang, Ruochen Sun, Peirong Liu, Xiang Li, Fangxu Xing

Organizations: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA · Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA · Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Abstract

Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame acquisition depends heavily on electrocardiogram (ECG) gating and repeated breath-holds, posing challenges in uncooperative populations, resource-limited settings, and temporally corrupted datasets. Existing methods that synthesize full cardiac sequences either rely on explicit ECG signals to parameterize myocardium function, or employ deformable registration without physiological constraints, failing to faithfully reproduce clinically relevant dynamic metrics such as ejection fraction (EF) and ventricular contraction magnitude. We present PhaseFlow, a unified generative framework that overcomes both limitations. PhaseFlow estimates a non-linear cardiac phase signal directly from the input sequence via a segmentation-derived left-ventricular (LV) area curve, capturing the asymmetric dynamics of systole and diastole without any ECG dependency. At inference, this phase signal is provided by a pathology-specific template, informing phase-specific frame generation. A rectified flow model conditioned on the phase and slice position synthesizes the full cardiac motion trajectory in the latent space, decoded into a diffeomorphic displacement field that warps end-diastole pixel intensities directly, eliminating the reconstruction blur often accompanying the variational autoencoder. On the ACDC benchmark, PhaseFlow achieves superior physiological fidelity and image realism, with best LV volume curve $R^2$ , structural similarity (SSIM) and generative quality (FID) among all baselines. Ablation studies confirm that each proposed component contributes measurably to the overall performance.

Figures & tables

Figure 1: PhaseFlow encodes the ED frame into latent features, predicts a phase-conditioned latent velocity via rectified flow, and decodes a diffeomorphic displacement field that warps ED pixels into a fully synthesized cine sequence.

Figure 2: Linear phase (left) scatters ES across

\phi

values; nonlinear phase (right) aligns ES at

\phi\!\approx\!\pi

for different pathological types (e.g., normal and dilated cardiomyopathy).

	Image Quality				Physiological Fidelity
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	FID $\downarrow$	EF MAE $\downarrow$	Vol Corr $\uparrow$	Vol $R^{2}$ $\uparrow$	Vol MAE $\downarrow$
ED Repeat	30.63	0.950	0.022	18.22	51.37	—	$-$ 2.050	0.245
ConvLSTM	27.57	0.903	0.137	79.94	48.12	0.082	$-$ 1.539	0.230
Direct Reg	31.89	0.955	0.031	17.26	19.66	0.924	$-$ 0.105	0.116
CVAE	30.89	0.949	0.037	26.04	22.29	0.921	$-$ 0.038	0.122
EchoDiff ( Phi et al. 2024 )	15.20	0.219	0.391	115.17	15.47	0.414	$-$ 1.000	0.211

Table 1: Main results on ACDC test set (30 patients). Best in bold , second-best underlined .

	Image Quality				Physiological Fidelity
Variant	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	FID $\downarrow$	EF MAE $\downarrow$	Vol Corr $\uparrow$	Vol $R^{2}$ $\uparrow$	Vol MAE $\downarrow$
Full (pathology phase)	31.72	0.956	0.023	12.72	17.79	0.867	0.363	0.101
A1: VAE decoder	27.28	0.882	0.128	89.63	20.22	0.888	$-$ 0.548	0.128
A2: Linear phase	31.48	0.954	0.024	12.91	21.30	0.827	0.069	0.114
A3: No LNCC	15.17	0.238	0.176	50.25	14.88	0.876	0.295	0.094
A4: No vol loss	31.76	0.955	0.023	11.43	26.80	0.869	0.071	0.133

Table 2: Ablation study on the ACDC test set. Each row removes one component from the full PhaseFlow model.

Figure 3: LV volume curves. Ground-truth (solid) vs. predicted (dashed) trajectories per pathology group.

Figure 4: Qualitative comparison (HCM and NOR). Rows 1–3: GT, PhaseFlow-VAE, and our PhaseFlow (Deform) at ED, mid-systole, ES, and mid-diastole. Row 4: predicted displacement magnitude. PhaseFlow produces sharp, anatomically coherent frames; PhaseFlow-VAE shows characteristic reconstruction blur.

	Image Quality				Physiological Fidelity
Setting	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	FID $\downarrow$	EF MAE $\downarrow$	Vol Corr $\uparrow$	Vol $R^{2}$ $\uparrow$	Vol MAE $\downarrow$
ACDC (in-domain)	31.72	0.956	0.023	12.72	17.79	0.867	0.363	0.101
M&Ms (zero-shot)	32.80	0.937	0.016	0 4.82	19.89	0.845	0.309	0.114

Table 3: In-domain vs. zero-shot generalization. PhaseFlow trained on ACDC and evaluated on M&Ms without any adaptation.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Per-pathology nonlinear phase templates (coloured) vs. linear baseline (grey dashed). All groups show a fast-then-slow profile: rapid systolic rise, then slower diastolic progression.

Figure 6: Per-frame phase RMSE: nonlinear group template (solid) vs. linear baseline (dashed). Shaded region: error gap. Nonlinear template reduces approximation error by up to $1.7\times$ .

Figure 7: $\det(\mathbf{J}_{\varphi})$ distribution (S&S, $K=7$ ). Left : global histogram; red dashed: fold boundary ( $\det=0$ ). Right : per-pathology mean $\pm$ std. Zero folding across all pixels confirms diffeomorphic synthesis.

Image quality (per slice, $n{=}298$ )
Metric	Mean $\pm$ Std
PSNR (dB)	$31.72\pm 3.33$
SSIM	$0.956\pm 0.019$
LPIPS	$0.023\pm 0.012$
Physiological fidelity (per patient, $n{=}30$ )
EF MAE (%)	$17.79\pm 11.15$

Appendix

Table 4: Per-case mean

\pm

standard deviation of PhaseFlow on the ACDC test set. Image metrics are per-slice (

n{=}298

); physiological metrics are per-patient (

n{=}30

	Wilcoxon $p$ (PhaseFlow vs.)
Metric	ED Rep.	ConvLSTM	CVAE	Dir. Reg.
EF MAE	$\mathbf{1.9{\times}10^{-9}}$	$\mathbf{1.9{\times}10^{-9}}$	$\mathbf{2.0{\times}10^{-2}}$	$0.36$
Vol Corr	—	$\mathbf{5.6{\times}10^{-9}}$	$0.36$	$0.72$
Vol $R^{2}$	$\mathbf{1.9{\times}10^{-9}}$	$\mathbf{6.2{\times}10^{-8}}$	$\mathbf{2.3{\times}10^{-4}}$	$\mathbf{1.6{\times}10^{-2}}$
Vol MAE	$\mathbf{5.6{\times}10^{-9}}$	$\mathbf{2.6{\times}10^{-8}}$	$\mathbf{2.3{\times}10^{-4}}$	$0.064$

Appendix

Table 5: Paired Wilcoxon signed-rank test (

n{=}30

patients) against four baselines. Every cell is a

p

-value; smaller means stronger evidence that PhaseFlow is better . Bold

=

significant at

\alpha{=}0.05

. PhaseFlow is significantly better on Vol

R^{2}

against all baselines; non-bold cells (Vol Corr vs. CVAE/Dir. Reg., EF/Vol MAE vs. Dir. Reg.) indicate no significant difference. Vol Corr vs. ED Repeat is undefined (constant volume curve).

Quantity	Train ( $n{=}1048$ )	Val ( $n{=}143$ )
$\sigma$ of $\mathbf{z}_{1}$	0.506	0.538
$\sigma$ of anchor $E_{\mathrm{vae}}(\mathrm{repeat}(\cdot))$	0.503	0.535
$\sigma$ of $\mathbf{z}_{1}-\mathbf{z}_{0}$	0.171	0.174
$\alpha/\sigma(\mathbf{z}_{1})$	19.8%	18.6%
$\alpha/\sigma(\mathbf{z}_{1}-\mathbf{z}_{0})$	58.3%	57.4%

Appendix

Table 6: Empirical latent statistics (

\sigma

= per-element standard deviation). The two splits agree closely, indicating the latent scale is a stable property of the frozen encoder rather than a split artifact.

Figure 8: Per-pathology qualitative results (one representative patient per group). Top row : ground truth; bottom row : PhaseFlow synthesis. ED frame ( $t=0$ , leftmost) is the sole model input.

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One Frame, Full Heartbeat: ECG-Free 4D Cardiac Cine MRI Synthesis via Radial-Decomposed Flow Matching

Shiyi Wang, Ruochen Sun, Xiang Li, Peirong Liu+1

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA · Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA · Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

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Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electrocardiogram (ECG) gating and repeated breath holds. Visual realism alone does not establish accurate patient-specific ejection fraction (EF) or ventricular volumes. We present PhaseFlow3D, a generative framework that synthesizes a complete 4D cine sequence from a single end-diastolic (ED) three-dimensional (3D) volume without ECG. To capture asymmetric systolic and diastolic dynamics, it represents the cardiac cycle as a piecewise linear phase anchored at ED and end-systolic (ES) time points. At inference, a population-level canonical template supplies this phase without patient-specific temporal information. A phase-conditioned rectified flow model generates a cardiac motion trajectory in latent space. Radial Contraction Decomposition converts each latent state into a 3D displacement field, combining a physics-informed radial component for centripetal myocardial contraction with an image-conditioned residual for rotation and out-of-plane motion. Each frame is generated by directly warping the ED volume, bypassing variational autoencoder decoding. On the combined ACDC and M&Ms benchmark, PhaseFlow3D achieves the lowest EF mean absolute error, the only positive left-ventricular volume-curve

R^2

, and the best distributional quality among compared methods. Ablations confirm each component's contribution. Downstream evaluations demonstrate the utility of the synthesized sequences and displacement fields for segmentation, pathology classification, label propagation, and myocardial strain analysis.

Abstract

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One Frame, Full Heartbeat: ECG-Free 4D Cardiac Cine MRI Synthesis via Radial-Decomposed Flow Matching

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