skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery
Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, A * search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy.  more » « less
Award ID(s):
2133027
PAR ID:
10547464
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE International Conference on Robotics and Automation (ICRA)
Date Published:
ISBN:
979-8-3503-8457-4
Page Range / eLocation ID:
15478 to 15484
Format(s):
Medium: X
Location:
Yokohama, Japan
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, Polymers)
    null (Ed.)
    The advent of 3D digital printers has led to the evolution of realistic anatomical organ shaped structures that are being currently used as experimental models for rehearsing and preparing complex surgical procedures by clinicians. However, the actual material properties are still far from being ideal, which necessitates the need to develop new materials and processing techniques for the next generation of 3D printers optimized for clinical applications. Recently, the voxelated soft matter technique has been introduced to provide a much broader range of materials and a profile much more like the actual organ that can be designed and fabricated voxel by voxel with high precision. For the practical applications of 3D voxelated materials, it is crucial to develop the novel high precision material manufacturing and characterization technique to control the mechanical properties that can be difficult using the conventional methods due to the complexity and the size of the combination of materials. Here we propose the non-destructive ultrasound effective density and bulk modulus imaging to evaluate 3D voxelated materials printed by J750 Digital Anatomy 3D Printer of Stratasys. Our method provides the design map of voxelated materials and substantially broadens the applications of 3D digital printing in the clinical research area. 
    more » « less
  2. ; ; ; (, IEEE)
    While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most of the existing approaches in robotics require detailed environment representations which consume a significant amount of sensing, computing, and storage; these issues become particularly important in resource-constrained settings with limited power budgets. Deep learning methods can learn from prior experience to abstract knowledge from novel environments, and use it to more efficiently execute tasks such as frontier exploration, object search, or scene understanding. We propose BoxMap, a Detection-Transformer-based architecture that takes advantage of the structure of the sensed partial environment to update a topological graph of the environment as a set of semantic entities (rooms and doors) and their relations (connectivity). The predictions from low-level measurements can be leveraged to achieve high-level goals with lower computational costs than methods based on detailed representations. As an example application, we consider a robot equipped with a 2-D laser scanner tasked with exploring a residential building. Our BoxMap representation scales quadratically with the number of rooms (with a small constant), resulting in significant savings over a full geometric map. Moreover, our high-level topological representation results in 30.9% shorter trajectories in the exploration task with respect to a standard method. Code is available at: bit.ly/3F6w2Yl. 
    more » « less
  3. ; ; (, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    We consider a scenario in which an autonomous vehicle equipped with a downward-facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides a wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time. We illustrate the algorithm using a simulated multi-target search scenario. 
    more » « less
  4. ; (, IEEE 2024 International Symposium on Medical Robotics (ISMR))
    Vision dimensionality during minimally invasive surgery is a critical contributor to patient success. Traditional visualizations of the surgical scene are 2D camera streams that obfuscate depth perception inside the abdominal cavity. A lack of depth in surgical views cause surgeons to miss tissue targets, induce blood loss, and incorrectly assess deformation. 3D sensors, while offering key depth information, are expensive and often incompatible with current sterilization techniques. Furthermore, methods inferring a 3D space from stereoscopic video struggle with the inherent lack of unique features in the biological domain. We present an application of deep learning models that can assess simple binary occupancy from a single camera perspective to recreate the surgical scene in high-fidelity. Our quantitative results (IoU=O.82, log loss=0.346) indicate a strong representational capability for structure in surgical scenes, enabling surgeons to reduce patient injury during minimally invasive surgery. 
    more » « less
  5. ; (, Biomedical Optics Express)
    Photoacoustic imaging–the combination of optics and acoustics to visualize differences in optical absorption – has recently demonstrated strong viability as a promising method to provide critical guidance of multiple surgeries and procedures. Benefits include its potential to assist with tumor resection, identify hemorrhaged and ablated tissue, visualize metal implants (e.g., needle tips, tool tips, brachytherapy seeds), track catheter tips, and avoid accidental injury to critical subsurface anatomy (e.g., major vessels and nerves hidden by tissue during surgery). These benefits are significant because they reduce surgical error, associated surgery-related complications (e.g., cancer recurrence, paralysis, excessive bleeding), and accidental patient death in the operating room. This invited review covers multiple aspects of the use of photoacoustic imaging to guide both surgical and related non-surgical interventions. Applicable organ systems span structures within the head to contents of the toes, with an eye toward surgical and interventional translation for the benefit of patients and for use in operating rooms and interventional suites worldwide. We additionally include a critical discussion of complete systems and tools needed to maximize the success of surgical and interventional applications of photoacoustic-based technology, spanning light delivery, acoustic detection, and robotic methods. Multiple enabling hardware and software integration components are also discussed, concluding with a summary and future outlook based on the current state of technological developments, recent achievements, and possible new directions. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email