Motion compensation for MRI-compatible patient-mounted needle guide device: estimation of targeting accuracy in MRI-guided kidney cryoablations. (13th April 2018)

• Record Type: Journal Article
• Authors: Tokuda, Junichi; Chauvin, Laurent; Ninni, Brian; Kato, Takahisa; King, Franklin; Tuncali, Kemal; Hata, Nobuhiko
• Is Part Of: Physics in medicine & biology. Volume 63:Number 8(2018:Apr.)
• Publication Date: 2018-04-13
• Subjects: MRI-guided interventions -- renal cryoablation -- MRI-compatible robot; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aab736
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 mm, 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 ( ) 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.