Motion Compensation for MRI-compatible Patient-mounted Needle Guide Device: Estimation of Targeting Accuracy in MRI-guided Kidney Cryoablations
Overview
Nuclear Medicine
Radiology
Authors
Affiliations
Patient-mounted needle guide devices for percutaneous ablation are vulnerable to patient motion. The objective of this study is to develop and evaluate a software system for an MRI-compatible patient-mounted needle guide device that can adaptively compensate for displacement of the device due to patient motion using a novel image-based automatic device-to-image registration technique. We have developed a software system for an MRI-compatible patient-mounted needle guide device for percutaneous ablation. It features fully-automated image-based device-to-image registration to track the device position, and a device controller to adjust the needle trajectory to compensate for the displacement of the device. We performed: (a) a phantom study using a clinical MR scanner to evaluate registration performance; (b) simulations using intraoperative time-series MR data acquired in 20 clinical cases of MRI-guided renal cryoablations to assess its impact on motion compensation; and (c) a pilot clinical study in three patients to test its feasibility during the clinical procedure. FRE, TRE, and success rate of device-to-image registration were 2.71 ± 2.29 mm, 1.74 ± 1.13 mm, and 98.3% for the phantom images. The simulation study showed that the motion compensation reduced the targeting error for needle placement from 8.2 mm to 5.4 mm (p < 0.0005) in patients under general anesthesia (GA), and from 14.4 mm to 10.0 mm (p < 1.0 × 10(−5)) in patients under monitored anesthesia care (MAC). The pilot study showed that the software registered the device successfully in a clinical setting. Our simulation study demonstrated that the software system could significantly improve targeting accuracy in patients treated under both MAC and GA. Intraprocedural image-based device-to-image registration was feasible.
Zotov A, Pushkarev A, Alekseeva A, Zaytsev K, Ryabikin S, Tsiganov D Sensors (Basel). 2024; 24(11).
PMID: 38894444 PMC: 11175356. DOI: 10.3390/s24113655.
MRI Distortion Correction and Robot-to-MRI Scanner Registration for an MRI-Guided Robotic System.
Tuna E, Poirot N, Franson D, Bayona J, Huang S, Seiberlich N IEEE Access. 2023; 10:99205-99220.
PMID: 37041984 PMC: 10085576. DOI: 10.1109/access.2022.3207156.
State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions.
Su H, Kwok K, Cleary K, Iordachita I, Cavusoglu M, Desai J Proc IEEE Inst Electr Electron Eng. 2022; 110(7):968-992.
PMID: 35756185 PMC: 9231642. DOI: 10.1109/jproc.2022.3169146.
Tuna E, Poirot N, Bayona J, Franson D, Huang S, Narvaez J Rep U S. 2021; 2020:2958-2964.
PMID: 34136309 PMC: 8202025. DOI: 10.1109/iros45743.2020.9341043.
Body-Mounted Robotics for Interventional MRI Procedures.
Li G, Patel N, Sharma K, Monfaredi R, Dumoulin C, Fritz J IEEE Trans Med Robot Bionics. 2021; 2(4):557-560.
PMID: 33778433 PMC: 7996400. DOI: 10.1109/tmrb.2020.3030532.