Vessel-based Ultrasound Registration

Trial Title: Vessel-based Ultrasound Registration

NCT ID: NCT05637346

Condition: Abdominal Cancer

Conditions: Keywords:
Surgical Navigation
Ultrasound
Registration
Electromagnetic Tracking
Pelvic Malignancies
Deep Learning
Segmentation

Study type: Interventional

Study phase: N/A

Overall status: Unknown status

Study design:

Allocation: N/A

Intervention model: Single Group Assignment

Intervention model description: One group to evaluate the feasibility and accuracy of the proposed methods.

Primary purpose: Other

Masking: None (Open Label)

Intervention:

Intervention type: Procedure
Intervention name: Intra-operative ultrasound measurement
Description: A patient-specific 3D model will be created using an available pre-operative CT scan. Anatomical target points are selected on this virtual model before the start of the surgery. Prior to surgery, a patient-reference electromagnetic (EM) sensor will be placed between the patient and the matrass on the operating table to account for patient movement during acquisition. Intra-operatively, an initial point registration of the 3D model with the electromagnetic tracking system (EMTS) will be performed based on ultrasound (US) imaging of the arterial bifurcations by the surgeon. Then, the surgeon will acquire multiple US sweeps of the pelvic arteries (abdominal aorta and left and right iliac arteries). For validation purposes, the pre-operatively defined anatomical target points will be visualized on US imaging and by pinpointing the EM tracked pointer. All tracking and US data will be recorded and stored for post-operative analysis.
Arm group label: Patients scheduled for laparotomy

Summary: In this study we aim to develop an automatic pelvic artery segmentation algorithm, which is required for future clinical implementation of US registration for surgical navigation. Various registration methods will be evaluated with the data of this study to obtain most optimal results. If automatic segmentation and registration is successful, the final accuracy of the developed US registration method for tumor tracking should be evaluated in future studies in patients eligible for surgical navigation. Eventually, we aim to replace the CBCT-scan with an automatic tracked US registration pipeline for a more efficient and accurate registration procedure, which could improve the applicability and accuracy of surgical navigation and patient outcomes.

Detailed description: Image-guided navigation surgery allows for full utilization of pre-operative imaging during surgery and has the potential of reducing both irradical resections and morbidity. To use navigation, a registration procedure is required to correlate pre-operative imaging with the patient's position on the operating room (OR). Currently, registration is done by Cone-Beam CT (CBCT) scanning on the OR prior to navigation surgery. However, the main limitation of the CBCT method is that it cannot compensate for per-operative changes such as bed rotation, retractor placement and tissue displacement due to the surgery. Alternatively, by using intra-operative tracked ultrasound and vessel-based patient registration, changing conditions during surgery can better be dealt with. This improved patient registration method could lead to an increased navigation accuracy and improved clinical usability and outcomes. The main difference between CBCT and proposed ultrasound registration is that CBCT is based on bones, while the ultrasound is based on vessels. Bones can be very easily imaged on the CBCT and therefore used for bone-bone registration with pre-operative CT-scans. However, vessels are more difficult to acquire, especially with ultrasound, and an automatic registration process with pre-operative imaging is needed for efficient clinical usability. For this, the vessels need to be extracted from the tracked ultrasound images to create a 3D representation that can be registered. Therefore, an algorithm needs to be developed that can automatically segment the pelvic vessels from ultrasound images.

Criteria for eligibility:
Criteria:
Inclusion Criteria: - ≥ 18 years old - Scheduled for laparotomy - A clinical pre-operative abdominal CT scan is available - Patient provides written informed consent Exclusion Criteria: - Metal implants in the pelvic area which could influence the 3D modelling or tracking accuracy - Patients with a pacemaker or defibrillator - Patient received pelvic-abdominal treatment, e.g. surgery or radiotherapy, between the pre-operative CT scan and laparotomy, which might altered the patient's anatomy

Gender: All

Minimum age: 18 Years

Maximum age: N/A

Healthy volunteers: No

Locations:

Facility:
Name: Netherlands Cancer Institute

Address:
City: Amsterdam
Zip: 1066 CX
Country: Netherlands

Status: Recruiting

Contact:
Last name: Marijn Hiep, MSc

Phone: +31205121751
Email: ma.hiep@nki.nl

Investigator:
Last name: Marijn Hiep, MSc
Email: Sub-Investigator

Investigator:
Last name: Theo Ruers, Prof. Dr.
Email: Principal Investigator

Investigator:
Last name: Wout Heerink, Dr.
Email: Sub-Investigator

Investigator:
Last name: Harald Groen, Dr.
Email: Sub-Investigator

Start date: December 5, 2021

Completion date: December 1, 2022

Lead sponsor:
Agency: The Netherlands Cancer Institute
Agency class: Other

Source: The Netherlands Cancer Institute

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT05637346