Simulated accuracy assessment of small footprint body-mounted probe alignment device for MRI-guided cryotherapy of abdominal lesions.

PURPOSE: Magnetic resonance imaging (MRI)-guided percutaneous cryotherapy of abdominal lesions, an established procedure, uses MRI to guide and monitor the cryoablation of lesions. Methods to precisely guide cryotherapy probes with a minimum amount of trial-and-error are yet to be established. To aid physicians in attaining precise probe alignment without trial-and-error, a body-mounted motorized cryotherapy-probe alignment device (BMCPAD) with motion compensation was clinically tested in this study. The study also compared the contribution of body motion and organ motion compensation to the guidance accuracy of a body-mounted probe alignment device. METHODS: The accuracy of guidance using the BMCPAD was prospectively measured during MRI-guided percutaneous cryotherapies before insertion of the probes. Clinical parameters including patient age, types of anesthesia, depths of the target, and organ sites of target were collected. By using MR images of the target organs and fiducial markers embedded in the BMCPAD, we retrospectively simulated the guidance accuracy with body motion compensation, organ motion compensation, and no compensation. The collected data were analyzed to test the impact of motion compensation on the guidance accuracy. RESULTS: Thirty-seven physical guidance of probes were prospectively recorded for sixteen completed cases. The accuracy of physical guidance using the BMCPAD was 13.4 ± 11.1 mm. The simulated accuracy of guidance with body motion compensation, organ motion compensation, and no compensation was 2.4 ± 2.9 mm, 2.2 ± 1.6 mm, and 3.5 ± 2.9 mm, respectively. Data analysis revealed that the body motion compensation and organ motion compensation individually impacted the improvement in the accuracy of simulated guidance. Moreover, the difference in the accuracy of guidance either by body motion compensation or organ motion compensation was not statistically significant. The major clinical parameters impacting the accuracy of guidance were the body and organ motions. Patient age, types of anesthesia, depths of the target, and organ sites of target did not influence the accuracy of guidance using BMCPAD. The magnitude of body surface movement and organ movement exhibited mutual statistical correlation. CONCLUSIONS: The BMCPAD demonstrated guidance accuracy comparable to that of previously reported devices for CT-guided procedures. The analysis using simulated motion compensation revealed that body motion compensation and organ motion compensation individually impact the improvement in the accuracy of device-guided cryotherapy probe alignment. Considering the correlation between body and organ movements, we also determined that body motion compensation using the ring fiducial markers in the BMCPAD can be solely used to address both body and organ motions in MRI-guided cryotherapy.

Motion compensation for MRI-compatible patient-mounted needle guide device: estimation of targeting accuracy in MRI-guided kidney cryoablations.

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.