Towards Autonomous Robotic Kidney Ultrasound: Spatial-Efficient Volumetric Imaging via Template Guided Optimal Pivoting

• URL: http://arxiv.org/abs/2602.16641v1
• Date: Wed, 18 Feb 2026 17:31:11 GMT
• Title: Towards Autonomous Robotic Kidney Ultrasound: Spatial-Efficient Volumetric Imaging via Template Guided Optimal Pivoting
• Abstract summary: 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.
• Score: 0.8952229340927186
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical ultrasound (US) imaging is a frontline tool for the diagnosis of kidney diseases. However, traditional freehand imaging procedure suffers from inconsistent, operator-dependent outcomes, lack of 3D localization information, and risks of work-related musculoskeletal disorders. While robotic ultrasound (RUS) systems offer the potential for standardized, operator-independent 3D kidney data acquisition, the existing scanning methods lack the ability to determine the optimal imaging window for efficient imaging. As a result, the scan is often blindly performed with excessive probe footprint, which frequently leads to acoustic shadowing and incomplete organ coverage. Consequently, there is a critical need for a spatially efficient imaging technique that can maximize the kidney coverage through minimum probe footprint. Here, we propose an autonomous workflow to achieve efficient kidney imaging via template-guided optimal pivoting. The system first performs an explorative imaging to generate partial observations of the kidney. This data is then registered to a kidney template to estimate the organ pose. With the kidney localized, the robot executes a fixed-point pivoting sweep where the imaging plane is aligned with the kidney long axis to minimize the probe translation. The proposed method was validated in simulation and in-vivo. Simulation results indicate that a 60% exploration ratio provides optimal balance between kidney localization accuracy and scanning efficiency. In-vivo evaluation on two male subjects demonstrates a kidney localization accuracy up to 7.36 mm and 13.84 degrees. Moreover, the optimal pivoting approach shortened the probe footprint by around 75 mm when compared with the baselines. These results valid our approach of leveraging anatomical templates to align the probe optimally for volumetric sweep.

Related papers

• Rendering Novel Views of MRI Using 3D Gaussian Splatting [54.6286495315908]
  We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
  arXiv  Detail & Related papers  (2026-06-24T18:00:03Z)
• Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction [1.0392533119774425]
  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.
  arXiv  Detail & Related papers  (2025-08-28T09:51:14Z)
• DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy [1.9458647657637413]
  We introduce DINOMotion, a novel deep learning framework for robust, efficient, and interpretable motion tracking.<n>DINOMotion automatically detects corresponding landmarks to derive optimal image registration, enhancing interpretability.<n>Our experiments on volunteer and patient datasets demonstrate its effectiveness in estimating both linear and nonlinear transformations.
  arXiv  Detail & Related papers  (2025-08-14T01:02:26Z)
• Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms [2.1113382954657594]
  Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency interventions.<n>Despite its critical role, successful vascular access remains challenging due to anatomical variability, overlying adipose tissue, and the need for precise ultrasound guidance.<n>We present a method for autonomous robotic US scanning of bifurcated femoral arteries, and validate it on five vascular phantoms created from real patient computed tomography (CT) data.
  arXiv  Detail & Related papers  (2025-03-09T22:20:25Z)
• 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)
• 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)
• Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video [0.0]
  During the biopsy process of lung cancer, physicians use real-time ultrasound images to find suitable lesion locations for sampling. Previous studies have employed 2D convolutional neural networks to effectively differentiate between benign and malignant lung lesions. This study designs an automatic diagnosis system based on a 3D neural network.
  arXiv  Detail & Related papers  (2023-05-04T10:39:37Z)
• Localizing Scan Targets from Human Pose for Autonomous Lung Ultrasound Imaging [61.60067283680348]
  With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging. We propose a vision-based, data driven method that incorporates learning-based computer vision techniques. Our method attains an accuracy level of 15.52 (9.47) mm for probe positioning and 4.32 (3.69)deg for probe orientation, with a success rate above 80% under an error threshold of 25mm for all scan targets.
  arXiv  Detail & Related papers  (2022-12-15T14:34:12Z)
• The interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions [3.0969191504482247]
  The purpose of this study is to differentiate malignant and benign lesions by using endobronchial ultrasound (EBUS) image. Our model is robust to noise and able to fuse various imaging features and aspiration of EBUS videos.
  arXiv  Detail & Related papers  (2021-07-29T08:38:17Z)
• 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.