# AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging

arXiv: https://arxiv.org/abs/2409.16898 · HTML: https://mlsanity.com/paper/2409.16898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling](https://mlsanity.com/paper/2403.03234/) (2024)
- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.10887/) (2024)
- [ViM-UNet: Vision Mamba for Biomedical Segmentation](https://mlsanity.com/paper/2404.07705/) (2024)
- [Goal-conditioned reinforcement learning for ultrasound navigation guidance](https://mlsanity.com/paper/2405.01409/) (2024)
- [More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring](https://mlsanity.com/paper/1812.03507/) (2018)
- [Cardiac ultrasound simulation for autonomous ultrasound navigation](https://mlsanity.com/paper/2402.06463/) (2024)
- [Automated Catheter Tip Repositioning for Intra-cardiac Echocardiography](https://mlsanity.com/paper/2201.08889/) (2022)
- [Towards Automatic Manipulation of Intra-cardiac Echocardiography Catheter](https://mlsanity.com/paper/2009.05859/) (2020)
- [AI-based Agents for Automated Robotic Endovascular Guidewire Manipulation](https://mlsanity.com/paper/2304.09286/) (2023)

## Built on by (most cited first, 3 of 3)

- [Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance](https://mlsanity.com/paper/2606.02634/) (2026)
- [Guidance for Intra-cardiac Echocardiography Manipulation to Maintain Continuous Therapy Device Tip Visibility](https://mlsanity.com/paper/2505.05518/) (2025)
- [Pose Estimation for Intra-cardiac Echocardiography Catheter via AI-Based Anatomical Understanding](https://mlsanity.com/paper/2505.07851/) (2025)

## Abstract (arXiv)

Intra-cardiac echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing realtime, high-resolution views from within the heart. Despite its advantages, effective manipulation of the ICE catheter requires significant expertise, which can lead to inconsistent outcomes, especially among less experienced operators. To address this challenge, we propose an AIdriven view guidance system that operates in a continuous closed-loop with human-in-the-loop feedback, designed to assist users in navigating ICE imaging without requiring specialized knowledge. Specifically, our method models the relative position and orientation vectors between arbitrary views and clinically defined ICE views in a spatial coordinate system. It guides users on how to manipulate the ICE catheter to transition from the current view to the desired view over time. By operating in a closedloop configuration, the system continuously predicts and updates the necessary catheter manipulations, ensuring seamless integration into existing clinical workflows. The effectiveness of the proposed system is demonstrated through a simulation-based performance evaluation using real clinical data, achieving an 89% success rate with 6,532 test cases. Additionally, a semi-simulation experiment with human-in-the-loop testing validated the feasibility of continuous yet discrete guidance. These results underscore the potential of the proposed method to enhance the accuracy and efficiency of ICE imaging procedures.