# SPARC: Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI

arXiv: https://arxiv.org/abs/2608.18616 · HTML: https://mlsanity.com/paper/2608.18616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI](https://mlsanity.com/paper/1906.08827/) (2019)
- [Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry](https://mlsanity.com/paper/1805.01026/) (2018)

## Abstract (arXiv)

Fetal cardiac MRI (fCMR) provides valuable diagnostic information complementary to echocardiography, particularly for complex congenital heart disease (CHD). Dynamic cine imaging captures cardiac motion essential for assessment of cardiac function; however, the reconstruction of 3D+time cine volumes from 2D+time acquired slices remains challenging due to unpredictable fetal motion and the absence of automated and robust processing tools suitable for clinical deployment. We present the SPARC pipeline (Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI) which combines physics-informed slice-to-volume reconstruction (SVR) of Doppler ultrasound (DUS) gated stacks of slices, assisted by deep learning (DL) models for thoracic segmentation and anatomical reorientation. The proposed SVR algorithm achieves a tenfold reduction in reconstruction time relative to existing frame-wise approaches (4.8 ± 1.0 vs 49.0 ± 14.1 min, p < 0.0001) while improving the reconstruction quality. Thoracic segmentation performance using ensemble aggregation exceeded inter-rater agreement (Dice 84.7 ± 3.9% vs 81.4 ± 7.7%, p<0.05), while anatomical reorientation achieved a success rate of 90.1%. End-to-end evaluation on a large held-out clinical cohort (n = 121) demonstrated fully automatic processing in 82.6% of cases with a mean runtime of 7.1 ± 1.3 min, compatible with clinical deployment. The complete SPARC pipeline is publicly available as a Docker container https://hub.docker.com/r/aboutill/sparc and is currently deployed at our institution as a clinical research tool.