Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction

• URL: http://arxiv.org/abs/2508.20605v1
• Date: Thu, 28 Aug 2025 09:51:14 GMT
• Title: Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction
• Abstract summary: 3D IntraVascular UltraSound (IVUS) offers a potential solution by enabling the reconstruction of the entire organ.<n>We propose an optimization-based calibration method using a 3D-printed phantom for accurate 3D Intravascular Ultrasound volume reconstruction.
• Score: 1.0392533119774425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intraoperative ultrasound images are inherently challenging to interpret in liver surgery due to the limited field of view and complex anatomical structures. Bridging the gap between preoperative and intraoperative data is crucial for effective surgical guidance. 3D IntraVascular UltraSound (IVUS) offers a potential solution by enabling the reconstruction of the entire organ, which facilitates registration between preoperative computed tomography (CT) scans and intraoperative IVUS images. In this work, we propose an optimization-based calibration method using a 3D-printed phantom for accurate 3D Intravascular Ultrasound volume reconstruction. Our approach ensures precise alignment of tracked IVUS data with preoperative CT images, improving intraoperative navigation. We validated our method using in vivo swine liver images, achieving a calibration error from 0.88 to 1.80 mm and a registration error from 3.40 to 5.71 mm between the 3D IVUS data and the corresponding CT scan. Our method provides a reliable and accurate means of calibration and volume reconstruction. It can be used to register intraoperative ultrasound images with preoperative CT images in the context of liver surgery, and enhance intraoperative guidance.

Related papers

• Towards Autonomous Robotic Kidney Ultrasound: Spatial-Efficient Volumetric Imaging via Template Guided Optimal Pivoting [0.8952229340927186]
  We propose an autonomous workflow to achieve efficient kidney imaging via template-guided optimal pivoting.<n>The proposed method was validated in simulation and in-vivo.
  arXiv  Detail & Related papers  (2026-02-18T17:31:11Z)
• BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation [69.14180476971602]
  We introduce BridgeSplat, a novel approach for deformable surgical navigation.<n>Our method rigs 3D Gaussians to a CT mesh, enabling joint optimization of Gaussian parameters and mesh deformation.<n>We demonstrate BridgeSplat's effectiveness on visceral pig surgeries and synthetic data of a human liver under simulation.
  arXiv  Detail & Related papers  (2025-09-23T01:09:36Z)
• Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery [4.250558597144547]
  Our framework seamlessly integrates with monocular surgical microscopes, making it highly practical for clinical use without additional hardware dependencies and requirements.<n>Our results suggest that our approach achieves clinically relevant accuracy in predicting 6D camera poses for registering 3D preoperative CT scans to 2D surgical scenes with an angular error within 10 degrees in most cases.
  arXiv  Detail & Related papers  (2025-05-23T21:15:00Z)
• EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion [11.798218793025974]
  In endoscopic sinus surgery (ESS), intraoperative CT offers valuable intraoperative assessment but is constrained by slow deployment and radiation exposure.<n>Existing techniques often struggle to achieve the submillimeter precision required for dense reconstruction.<n>We propose an online learning approach that leverages Neural Radiance Fields (NeRF) as an intermediate representation.<n>Our method attains a point-to-point accuracy below 0.5 mm, with a demonstrated theoretical depth accuracy of 0.125 $pm$ 0.443 mm.
  arXiv  Detail & Related papers  (2024-10-05T05:26:21Z)
• 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)
• EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting [53.38166294158047]
  EndoGSLAM is an efficient approach for endoscopic surgeries, which integrates streamlined representation and differentiable Gaussianization. Experiments show that EndoGSLAM achieves a better trade-off between intraoperative availability and reconstruction quality than traditional or neural SLAM approaches.
  arXiv  Detail & Related papers  (2024-03-22T11:27:43Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery [3.8909273404657556]
  We develop a method that permits direct 2D-to-3D registration of the view microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively.
  arXiv  Detail & Related papers  (2024-03-12T00:26:08Z)
• An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models [8.516340459721484]
  We propose a first vision-based approach to update the preoperative 3D anatomical model. Results show a decrease in error during surgical progression as opposed to increasing when no update is employed.
  arXiv  Detail & Related papers  (2024-02-19T05:06:52Z)
• 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)
• Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern [49.240017254888336]
  Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties. A current drawback is the limited field-of-view provided by the conventionally applied 2D probes. We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
  arXiv  Detail & Related papers  (2020-11-10T09:27:56Z)

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.