Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911369459138560 |
|---|---|
| author | Firenze, Margherita Young, Sean I. Wang, Clinton J. Yun, Hyuk Jin Adalsteinsson, Elfar Im, Kiho Grant, P. Ellen Golland, Polina |
| author_facet | Firenze, Margherita Young, Sean I. Wang, Clinton J. Yun, Hyuk Jin Adalsteinsson, Elfar Im, Kiho Grant, P. Ellen Golland, Polina |
| contents | Fully convolutional networks have become the backbone of modern medical imaging due to their ability to learn multi-scale representations and perform end-to-end inference. Yet their potential for slice-to-volume reconstruction (SVR), the task of jointly estimating 3D anatomy and slice poses from misaligned 2D acquisitions, remains underexplored. We introduce a fast convolutional framework that fuses multiple orthogonal 2D slice stacks to recover coherent 3D structure and refines slice alignment through lightweight model-based optimization. Applied to fetal brain MRI, our approach reconstructs high-quality 3D volumes in under 10s, with 1s slice registration and accuracy on par with state-of-the-art iterative SVR pipelines, offering more than speedup. The framework uses non-rigid displacement fields to represent transformations, generalizing to other SVR problems like fetal body and placental MRI. Additionally, the fast inference time paves the way for real-time, scanner-side volumetric feedback during MRI acquisition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07519 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization Firenze, Margherita Young, Sean I. Wang, Clinton J. Yun, Hyuk Jin Adalsteinsson, Elfar Im, Kiho Grant, P. Ellen Golland, Polina Image and Video Processing Computer Vision and Pattern Recognition Fully convolutional networks have become the backbone of modern medical imaging due to their ability to learn multi-scale representations and perform end-to-end inference. Yet their potential for slice-to-volume reconstruction (SVR), the task of jointly estimating 3D anatomy and slice poses from misaligned 2D acquisitions, remains underexplored. We introduce a fast convolutional framework that fuses multiple orthogonal 2D slice stacks to recover coherent 3D structure and refines slice alignment through lightweight model-based optimization. Applied to fetal brain MRI, our approach reconstructs high-quality 3D volumes in under 10s, with 1s slice registration and accuracy on par with state-of-the-art iterative SVR pipelines, offering more than speedup. The framework uses non-rigid displacement fields to represent transformations, generalizing to other SVR problems like fetal body and placental MRI. Additionally, the fast inference time paves the way for real-time, scanner-side volumetric feedback during MRI acquisition. |
| title | Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.07519 |