RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning

• URL: http://arxiv.org/abs/2310.03406v1
• Date: Thu, 5 Oct 2023 09:22:16 GMT
• Title: RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning
• Authors: Deepak Raina, Abhishek Mathur, Richard M. Voyles, Juan Wachs, SH Chandrashekhara, Subir Kumar Saha
• Abstract summary: Proper orientation of the robotized probe plays a crucial role in governing the quality of ultrasound images. We propose a sample-efficient method to automatically adjust the orientation of the ultrasound probe normal to the point of contact on the scanning surface.
• Score: 4.420121239028863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The one of the significant challenges faced by autonomous robotic ultrasound systems is acquiring high-quality images across different patients. The proper orientation of the robotized probe plays a crucial role in governing the quality of ultrasound images. To address this challenge, we propose a sample-efficient method to automatically adjust the orientation of the ultrasound probe normal to the point of contact on the scanning surface, thereby improving the acoustic coupling of the probe and resulting image quality. Our method utilizes Bayesian Optimization (BO) based search on the scanning surface to efficiently search for the normalized probe orientation. We formulate a novel objective function for BO that leverages the contact force measurements and underlying mechanics to identify the normal. We further incorporate a regularization scheme in BO to handle the noisy objective function. The performance of the proposed strategy has been assessed through experiments on urinary bladder phantoms. These phantoms included planar, tilted, and rough surfaces, and were examined using both linear and convex probes with varying search space limits. Further, simulation-based studies have been carried out using 3D human mesh models. The results demonstrate that the mean ($\pm$SD) absolute angular error averaged over all phantoms and 3D models is $\boldsymbol{2.4\pm0.7^\circ}$ and $\boldsymbol{2.1\pm1.3^\circ}$, respectively.

Related papers

• Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture [45.97156332608481]
  Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots inside deep veins, which may block blood flow or even cause a life-threatening pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by pressing the target vein until its lumens is fully compressed. We present a robotic US system with a novel hybrid force motion control scheme ensuring position and force tracking accuracy, and soft landing of the probe onto the target surface.
  arXiv  Detail & Related papers  (2024-01-04T21:02:39Z)
• Mip-Splatting: Alias-free 3D Gaussian Splatting [52.366815964832426]
  3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. Strong artifacts can be observed when changing the sampling rate, eg, by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter.
  arXiv  Detail & Related papers  (2023-11-27T13:03:09Z)
• Deep Kernel and Image Quality Estimators for Optimizing Robotic Ultrasound Controller using Bayesian Optimization [2.0971479389679337]
  Autonomous Robotic Ultrasound (A-RUS) is an appealing alternative to manual procedure in order to reduce sonographers' workload. The key challenge to A-RUS is optimizing the ultrasound image quality for the region of interest across different patients. This requires knowledge of anatomy, recognition of error sources and precise probe position, orientation and pressure.
  arXiv  Detail & Related papers  (2023-10-11T11:20:35Z)
• 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)
• 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)
• A Novel Augmented Reality Ultrasound Framework Using an RGB-D Camera and a 3D-printed Marker [0.3061098887924466]
  Our goal is to develop a simple and low cost augmented reality echography framework using a standard RGB-D Camera. Prototype system consisted of an Occipital Structure Core RGB-D camera, a specifically-designed 3D marker, and a fast point cloud registration algorithm FaVoR. Prototype probe was calibrated on a 3D-printed N-wire phantom using the software PLUS toolkit.
  arXiv  Detail & Related papers  (2022-05-09T14:54:47Z)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification [55.96221340756895]
  Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is combined with an image clas-sifier using a recurrent neural network to generate two sets of predictions in real-time. The algorithm identified optimal probe alignment within a mean (standard deviation) range of 3.7$circ$ (1.2$circ$) from
  arXiv  Detail & Related papers  (2020-10-06T13:55:02Z)

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.