Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Improving Image-Guided Surgery Precision and Reliability with Real-time Modification Tracking in Endoscopic Sinus and Skull Base Surgery

Award Number: R15EB034519
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/02/2024
PERIOD OF PERFORMANCE END DATE: 12/31/2026

Group Awards By:

View Award Description

Improving Image-Guided Surgery Precision and Reliability with Real-time Modification Tracking in Endoscopic Sinus and Skull Base Surgery - ABSTRACT This project proposes to revolutionize image-guided surgery (IGS) technology by tracking surgical modiﬁcations in real time. IGS has profoundly changed modern endoscopic surgical techniques by allowing surgeons to visualize pathologies that are not visible by endoscopy, to direct surgical instruments through the patient's anatomy. However, revcurrent IGS is a static mechanism and can neither dynamically track surgery nor update IGS reference images by CT or MRI in real time. The reference images easily deviate from the actual anatomy and pathology during surgery, and surgeons must rely on memory to ensure the correctness and completeness of the resection. This is more pronounced when the surgery covers a big area or involves soft tissue. In endoscopic sinus and skull base surgery (ESSBS), for example, the surgery involves the wide sinus and skull base area, which is a honeycomb-shaped structure surrounded by dense blood vessels and nerves. revDriven by ensuring the safety of the patient during the operation, there is a reluctance to avoid more eﬀective but riskier resections. This results in a currently accepted revision rate of ESSBS of up to 28%. By real-time tracking and guiding, the precision of surgery would be dramatically increased, leading to far greater success in surgery, with a vastly reduced percentage of required revision surgery. This design approach will focus on the ability to track modiﬁcation and update images in real-time through system optimization and advanced algorithms. revThe project will use a novel deep causal learning (DCL) framework to break the bottleneck that results from the current unpredictable adverse factors in operating rooms. The framework will use invertible nets to learn from data and clinical knowledge and identify, isolate, and compensate for the adverse factors, leading to a realistic solution to this problem. A research team has been assembled including surgeons, and computer scientists, to identify the limitations and challenges to formulate a research plan to redesign the utility of IGS information to track surgery in real-time. The team will rely on years of previous collaboration together to verify the feasibility of motion-based modiﬁcation tracking. More speciﬁcally, this project will use motion and endoscopic videos (Aims 1 and 2) to independently track surgical modiﬁcations. The two methods are based on the DCL framework, and the tracking results not only have accuracy and estimated conﬁdence but will include the impacts of adverse factors as well. Moreover, the project will fuse the two methods and verify clinical usability in the operating room environment (Aim 3). revThis project will achieve reliable real-time updates of IGS in real clinical environments, and directly improve the reliability of ESSBS surgical navigation. The project will pioneer tracking of surgical modiﬁcation when both deformation and modiﬁcation are present, and extend the application of IGS to a broader array of endoscopic surgeries. The use of causal learning to solve the medical modeling problem will be explored, thus paving the way for using current and prior medical knowledge to formulate critical new learning of IGS procedures.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $459,375 )
2024	2024	ROCHESTER INSTITUTE OF TECHNOLOGY	1 LOMB MEMORIAL DR	ROCHESTER	NY	14623	MONROE	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	1/2/2024	NEW	$459,375
														Subtotal = $459,375

Grand Total All Awards = $459,375

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Improving Image-Guided Surgery Precision and Reliability with Real-time Modification Tracking in Endoscopic Sinus and Skull Base Surgery

Award Number: R15EB034519

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 01/02/2024

PERIOD OF PERFORMANCE END DATE: 12/31/2026

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer