A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
- URL: http://arxiv.org/abs/2506.21162v1
- Date: Thu, 26 Jun 2025 11:39:08 GMT
- Title: A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
- Authors: Shuwei Xing, Derek W. Cool, David Tessier, Elvis C. S. Chen, Terry M. Peters, Aaron Fenster,
- Abstract summary: 3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation.<n>Its clinical integration is crucial for transitioning 3D US into the therapeutic domain.<n>We propose a novel framework for integrating 3D US into the standard ablation workflow.
- Score: 5.585625844344932
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this work, we propose a novel framework for integrating 3D US into the standard ablation workflow. We present a key component, a clinically viable 2D US-CT/MRI registration approach, leveraging 3D US as an intermediary to reduce registration complexity. To facilitate efficient verification of the registration workflow, we also propose an intuitive multimodal image visualization technique. In our study, 2D US-CT/MRI registration achieved a landmark distance error of approximately 2-4 mm with a runtime of 0.22s per image pair. Additionally, non-rigid registration reduced the mean alignment error by approximately 40% compared to rigid registration. Results demonstrated the efficacy of the proposed 2D US-CT/MRI registration workflow. Our integration framework advanced the capabilities of 3D US imaging in improving percutaneous tumour ablation, demonstrating the potential to expand the therapeutic role of 3D US in clinical interventions.
Related papers
- Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection [50.388465935739376]
Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate.<n>Existing registration methods rely heavily on anatomical landmark-based, which encounter two major limitations.<n>We propose a landmark-free preoperative-to-intraoperative registration framework utilizing effective self-supervised learning.
arXiv Detail & Related papers (2025-04-21T14:55:57Z) - Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal Ablation [5.585625844344932]
Liver tumor ablation procedures require accurate placement of the needle applicator at the tumor centroid.
Image registration techniques can aid in interpreting anatomical details and identifying tumors, but their clinical application has been hindered by the tradeoff between alignment accuracy and runtime performance.
We propose a 2D-3D US registration approach to enable intra-procedural alignment that mitigates errors caused by liver motion.
arXiv Detail & Related papers (2024-10-03T15:24:45Z) - Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration [50.602074919305636]
This paper introduces a lightweight end-to-end Cardiac Ultrasound frame-to-volume Registration network, termed CU-Reg.<n>We use epicardium prompt-guided anatomical clues to reinforce the interaction of 2D sparse and 3D dense features, followed by a voxel-wise local-global aggregation of enhanced features.
arXiv Detail & Related papers (2024-06-20T17:47:30Z) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - On the Localization of Ultrasound Image Slices within Point Distribution
Models [84.27083443424408]
Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US)
Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology.
We present a framework for automated US image slice localization within a 3D shape representation.
arXiv Detail & Related papers (2023-09-01T10:10:46Z) - 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation.
Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
arXiv Detail & Related papers (2023-06-23T12:09:52Z) - Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole
Mount Histopathology Images of the Prostate: A Proof-of-Concept Study [7.323398943910078]
Early diagnosis of prostate cancer significantly improves a patient's 5-year survival rate.
Micro-ultrasound (micro-US) provides a cost-effective alternative to MRI while delivering comparable diagnostic accuracy.
We present a semi-automated pipeline for registering in vivo micro-US images with ex vivo whole-mount histopathology images.
arXiv Detail & Related papers (2023-05-31T15:22:58Z) - Enabling Augmented Segmentation and Registration in Ultrasound-Guided
Spinal Surgery via Realistic Ultrasound Synthesis from Diagnostic CT Volume [19.177141722698188]
The scarcity of intra-operative clinical US data is an insurmountable bottleneck in training a neural network.
We propose an In silico bone US simulation framework that synthesizes realistic US images from diagnostic CT volume.
We train a lightweight vision transformer model that can achieve accurate and on-the-fly bone segmentation for spinal sonography.
arXiv Detail & Related papers (2023-01-05T07:28:06Z) - Moving from 2D to 3D: volumetric medical image classification for rectal
cancer staging [62.346649719614]
preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment.
We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
arXiv Detail & Related papers (2022-09-13T07:10:14Z) - End-to-end Ultrasound Frame to Volume Registration [9.738024231762465]
We propose an end-to-end frame-to-volume registration network (FVR-Net) for 2D and 3D registration.
Our model shows superior efficiency for real-time interventional guidance with highly competitive registration accuracy.
arXiv Detail & Related papers (2021-07-14T01:59:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.