A number of approaches based on compressed sensing (1) have been presented to accelerate time-resolved phase-contrast velocity mapping (2,3). In 4D Flow MRI (3), scan time reduction is of critical importance to improve its clinical applicability and translation. To this end, various implementations of compressed sensing with and without joint parallel imaging have been described (4-8). A key challenge of present methods relates to finding an appropriate sparsifying transform that is suitable for data of limited spatial and temporal resolution. While spatial sparsification using Wavelets or other transforms (1) can compromise resolution in space, reconstruction based on temporal transforms may introduce temporal blurring. It is the objective of the present work to entangle spatial and temporal nonlinear denoising to better preserve spatiotemporal fidelity of 4D Flow MRI at high acceleration factors. The concept is demonstrated using in vivo data acquired in the aorta of healthy subjects.In-vivo 4D Flow MRI data were acquired on a 3T Ingenia system (Philips Healthcare, Best, The Netherlands) equipped with a 28-channel cardiac coil array using the following scan parameters: spatial resolution: 2x2x2mm³, temporal resolution: 40ms, isotropic venc of 200cm/s, retrospective cardiac triggering and respiratory navigator gating (1.5mm gating window). Data were retrospectively undersampled by factors of 8 and 10 following a random 3D Poisson distribution which was varied for every time frame. Coil calibration data were obtained from a separate fully sampled pre-scan. Entangled compressed sensing (eSPARSE) reconstruction was implemented following ideas presented in (9). In brief, multiple reconstructions are performed interleaved and results thereof are combined patch-based using a weighted sum in each iteration step until final convergence is achieved. In this work, k-t SPARSE-SENSE (4) using both the temporal Fourier transform (FT_time) and Principal Component Analysis (PCA) along with 3D-L1-Wavelets operating separately on each time frame were implemented to sparsify the data prior to the nonlinear denoising step. The FISTA optimization method (10) was implemented in Matlab (Mathworks, Natick, MA) and used to solve both minimization problems. The result of each iteration of the eSPARSE framework was subdivided into spatially localized patches of 10x10x5 voxels while keeping all time frames. The centroid of the patches was determined and the individual patches were weighted by the inverse of the Euclidean distance between themselves and the centroid. The weighted patches were then recombined. To reduce temporal aliasing during 3D-L1-Wavelet reconstruction, this step was dynamically activated during the iteration process (Figure 1). Reconstruction results of the eSPARSE approach for 8- and 10-fold undersampling were compared to the fully sampled reference and relative to standard k-t SPARSE-SENSE reconstruction (4) by calculating root-mean-square errors (RMSE) of velocity along with an assessment of directional velocity error.Compared to k–t SPARSE-SENSE, flow data derived with eSPARSE reconstruction showed less noise and preserved flow patterns more faithfully (Figure 2). In Figure 3 it is seen that in-plane velocity vectors are more aligned with the reference data compared to data reconstructed with k-t SPARSE-SENSE. Figure 4 shows that both directional error and RMSE of the velocity field are lower for eSPARSE when compared to k-t SPARSE-SENSE: directional error (eSPARSE/k-t SPARSE-SENSE) 3.1%/4.6% in systole and 23.6%/34.7% in diastole and RMSE velocity 13.1%/15.5% and 38.9%/44.4% for 8-fold undersampling For R = 10 the directional error is 3.5%/5.5% in systole and 25.0%/38.2% in diastole and the RMSE velocity is 14.1%/17.5% and 39.7%/46.6%. Regression analysis in Figure 5 shows that on average eSPARSE approximates the hypothetical slope of 1 better when compared to k-t SPARSE-SENSE (slope: 0.94 vs. 0.93 with R² 0.96 vs. 0.93 for R = 8 and 0.94 vs. 0.92 with R² 0.95 vs. 0.91).In this work an entangled denosing approach to reconstruct undersampled 4D Flow MRI data has been implemented and compared against k-t SPARSE-SENSE. A reduction of error of both velocity magnitude (by up to 16% for R = 8 and 19% for R = 10) and velocity direction (by up to 33% for R = 8 and 36% for R = 10) was achieved. These undersampling rates are currently targeted for practical 4D Flow MRI protocols. In eSPARSE, patches are combined and the individual weights employed are therefore crucial. It is essential that the L1-Wavelet algorithm acts only after a few iterations of k-t SPARSE-SENSE to first reduce temporal aliasing and to ensure convergence.Entangled denoising (eSPARSE) is a promising approach to accelerate 4D Flow MRI while preserving key features of the flow field.