We developed a novel multi-beat image fusion technique using a special spatiotemporal interpolation for sparse, irregularly sampled data (ISI). It is applied to irregularly distributed 3D cardiac ultrasound data acquired with a fast rotating ultrasound (FRU) transducer. ISI is based on Normalized Convolution with Gaussian kernels tuned to irregular beam data spacing over cardiac phase (τ), and beam rotation (θ) and elevation angles (φ). Methods: images are acquired with the FRU transducer developed in our laboratory, a linear phased array rotating mechanically continuously at very high speed (240-480rpm). High-quality 2D images are acquired at ∼100 frames/s over 5-10 seconds. ECG is recorded simultaneously. Images are irregularly distributed over τ and θ, because rotation is not synchronized to heartrate. ISI was compared quantitatively to spatiotemporal nearest neighbor interpolation (STNI) on synthetic (distance function) data of a pulsating ellipsoid for 32 angles (θ) and 37 phases (τ). ISI was also tested qualitatively on 20 in-vivo cardiac image sets and compared to classical temporal binning with trilinear voxel interpolation, at resolutions of 256*256*400 for 16 phases. Results: From the synthetic data simulations, ISI showed absolute distance errors (mean±SD) of 1.23 ± 1.52mm; considerably lower than for STNI (3.45 ± 3.03mm). For in-vivo images, ISI voxel sets showed reduced motion artifacts, suppression of noise and interpolation artifacts and better delineation of endocardium. Conclusions: ISI improves the quality of 3D+T images acquired with a fast rotating transducer in simulated and in-vivo data. It may also be useful for similar spatiotemporal irregularly distributed data, e.g. freehand 3D echocardiography.