# SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation

arXiv: https://arxiv.org/abs/2608.28715 · HTML: https://mlsanity.com/paper/2608.28715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI](https://mlsanity.com/paper/2206.10802/) (2022)
- [Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI](https://mlsanity.com/paper/1906.08827/) (2019)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [FusionMamba: Efficient Remote Sensing Image Fusion with State Space Model](https://mlsanity.com/paper/2404.07932/) (2024)
- [End-to-end Ultrasound Frame to Volume Registration](https://mlsanity.com/paper/2107.06449/) (2021)
- [DISA: DIfferentiable Similarity Approximation for Universal Multimodal Registration](https://mlsanity.com/paper/2307.09931/) (2023)
- [Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images](https://mlsanity.com/paper/2409.08492/) (2024)
- [Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration](https://mlsanity.com/paper/2406.14534/) (2024)
- [Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching](https://mlsanity.com/paper/2410.18683/) (2024)
- [CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising](https://mlsanity.com/paper/2411.07930/) (2024)
- [DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration](https://mlsanity.com/paper/2606.18825/) (2026)
- [Transformer-Based Local Feature Matching for Multimodal Image Registration](https://mlsanity.com/paper/2404.16802/) (2024)
- [Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration](https://mlsanity.com/paper/2507.04397/) (2025)
- [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026)

## Abstract (arXiv)

Ultrasound-guided interventions can require localization of an untracked 2D frame within a 3D anatomical reference. Rigid slice-to-volume registration (SVR) estimates this six-degree-of-freedom pose but remains challenging because of limited anatomical context, acoustic artifacts, and view-dependent appearance. Existing methods often use dense cross-attention, whose cost scales with the product of slice and volume token counts, or direct pose regression without explicit correspondence constraints. We introduce SCoPE-Reg, combining state-space slice--volume interaction, dense 3D coordinate prediction, and parameter-free weighted Kabsch estimation. On SVR tasks from CAMUS and μ-RegPro, SCoPE-Reg yields mean target registration errors of 0.73 mm and 2.27 mm against 1.24 mm and 2.63 mm for the state of the art (SOTA), reduces peak error on CAMUS by 56% below SOTA (12.5!→!5.5 mm), and registers 100% and 80% of frames within 3 mm. On CAMUS at 1282 it retains the lowest error at increasing pose-perturbation magnitude. It holds 6.49 M parameters independent of resolution, sustaining 51 FPS at 5122. SCoPE-Reg establishes a SOTA in rigid ultrasound SVR: by coupling correspondence-based accuracy with bounded worst-case error and resolution-independent cost, it becomes viable at native acquisition resolution during intervention, where prior methods trade accuracy, reliability, or frame rate against one another. Supplementary code provided and will be open-sourced upon acceptance.