Guidance Core

N Hata

J Jagadeesan

Junichi Tokuda
Nobuhiko Hata, PhD
Core Lead
Jayender Jagadeesan, PhD
Project Lead
Junichi Tokuda, PhD
Project Lead

The long-term goal of Guidance Core is to provide novel guidance methods to improve the outcome of therapies of dynamically deforming and moving organs. This involves developing unique tissue-embedded wireless electromagnetic sensors (EM) and MR-tracked catheters to verify their feasibility and impact on the clinical procedures performed at the Advanced Multimodality Image Guided Operating Room (AMIGO). Projects within this Core are:

Integrated navigation system to accurately localize the tumor and guide the surgical instrument to the optimal resection margin in presence of significant tissue deformation.  We are developing hardware for the integrated navigation system to enable real-time tumor and instrument tracking; a tumor deformation model to estimate the tumor position and optimal resection margin; software for the integrated navigation system to visualize the tumor and surgical instrument. We continue to validate the design and performance of the integrated navigation system in ex-vivo phantoms and in human clinical trials. (Contact: Jayender Jagadeesan)

Verify the ability of active MRI-tracked metallic interventional devices to improve interventions through improved positional accuracy and improved therapy delivery. We are developing a miniature MRI tracking coil array embedded in flexible and rigid metallic stylets and catheters and improving dedicated MR-tracking pulse sequences to locate the devices so as to rapidly and accurately guide insertion of these devices in soft tissue and to monitor the non-rigid deformation of the target tissues. This is expected to enable clinicians to correct the access path based on these data. In addition, motional data provided by the devices is expected to aid in motion-compensated oxygenation imaging for radiation-dose augmentation to hypoxic tumor segments that resist radiation therapy. (Contact: Junichi Tokuda)

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Full Publication List in PubMed

Select Recent Publications

Naoyuki Shono, Brian Ninni, Franklin King, Takahisa Kato, Junichi Tokuda, Takahiro Fujimoto, Kemal Tuncali, and Nobuhiko Hata. 6/2020. “Simulated Accuracy Assessment of Small Footprint Body-mounted Probe Alignment Device for MRI-guided Cryotherapy of Abdominal Lesions.” Med Phys, 47, 6, Pp. 2337-49.Abstract
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.
Haoyin Zhou and Jayender Jagadeesan. 2/2020. “Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video.” IEEE Trans Med Imaging, 39, 2, Pp. 400-12.Abstract
We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low-texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling, and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low-texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of the SLAM, which mainly include 1) a histogram voting-based method to roughly select possible inliers from the feature matching results; 2) a novel 1-point RANSAC-based [Formula: see text] algorithm called as DynamicR1PP [Formula: see text] to track the camera motion; and 3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on ex- and in vivo data showed that the reconstructed 3D models have high-resolution texture with an accuracy error of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with 960×540 resolution.
Junichi Tokuda, Qun Wang, Kemal Tuncali, Ravi T Seethamraju, Clare M Tempany, and Ehud J Schmidt. 5/2020. “Temperature-Sensitive Frozen-Tissue Imaging for Cryoablation Monitoring Using STIR-UTE MRI.” Invest Radiol, 55, 5, Pp. 310-7.Abstract
PURPOSE: The aim of this study was to develop a method to delineate the lethally frozen-tissue region (temperature less than -40°C) arising from interventional cryoablation procedures using a short tau inversion-recovery ultrashort echo-time (STIR-UTE) magnetic resonance (MR) imaging sequence. This method could serve as an intraprocedural validation of the completion of tumor ablation, reducing the number of local recurrences after cryoablation procedures. MATERIALS AND METHODS: The method relies on the short T1 and T2* relaxation times of frozen soft tissue. Pointwise Encoding Time with Radial Acquisition, a 3-dimensional UTE sequence with TE = 70 microseconds, was optimized with STIR to null tissues with a T1 of approximately 271 milliseconds, the threshold T1. Because the T1 relaxation time of frozen tissue in the temperature range of -40°C < temperature < -8°C is shorter than the threshold T1 at the 3-tesla magnetic field, tissues in this range should appear hyperintense. The sequence was evaluated in ex vivo frozen tissue, where image intensity and actual tissue temperatures, measured by thermocouples, were correlated. Thereafter, the sequence was evaluated clinically in 12 MR-guided prostate cancer cryoablations, where MR-compatible cryoprobes were used to destroy cancerous tissue and preserve surrounding normal tissue. RESULTS: The ex vivo experiment using a bovine muscle demonstrated that STIR-UTE images showed regions approximately between -40°C and -8°C as hyperintense, with tissues at lower and higher temperatures appearing dark, making it possible to identify the region likely to be above the lethal temperature inside the frozen tissue. In the clinical cases, the STIR-UTE images showed a dark volume centered on the cryoprobe shaft, Vinner, where the temperature is likely below -40°C, surrounded by a doughnut-shaped hyperintense volume, where the temperature is likely between -40°C and -8°C. The hyperintense region was itself surrounded by a dark volume, where the temperature is likely above -8°C, permitting calculation of Vouter. The STIR-UTE frozen-tissue volumes, Vinner and Vouter, appeared significantly smaller than signal voids on turbo spin echo images (P < 1.0 × 10), which are currently used to quantify the frozen-tissue volume ("the iceball"). The ratios of the Vinner and Vouter volumes to the iceball were 0.92 ± 0.08 and 0.29 ± 0.07, respectively. In a single postablation follow-up case, a strong correlation was seen between Vinner and the necrotic volume. CONCLUSIONS: Short tau inversion-recovery ultrashort echo-time MR imaging successfully delineated the area approximately between -40°C and -8°C isotherms in the frozen tissue, demonstrating its potential to monitor the lethal ablation volume during MR-guided cryoablation.
Atsushi Yamada, Junichi Tokuda, Shigeyuki Naka, Koichiro Murakami, Tohru Tani, and Shigehiro Morikawa. 3/2020. “Magnetic Resonance and Ultrasound Image-guided Navigation System using a Needle Manipulator.” Med Phys, 47, 3, Pp. 850-8.Abstract
PURPOSE: Image guidance is crucial for percutaneous tumor ablations, enabling accurate needle-like applicator placement into target tumors while avoiding tissues that are sensitive to injury and/or correcting needle deflection. Although ultrasound (US) is widely used for image guidance, magnetic resonance (MR) is preferable due to its superior soft tissue contrast. The objective of this study was to develop and evaluate an MR and US multi-modal image-guided navigation system with a needle manipulator to enable US-guided applicator placement during MR imaging (MRI)-guided percutaneous tumor ablation. METHODS: The MRI-compatible needle manipulator with US probe was installed adjacent to a 3 Tesla MRI scanner patient table. Coordinate systems for the MR image, patient table, manipulator, and US probe were all registered using an optical tracking sensor. The patient was initially scanned in the MRI scanner bore for planning and then moved outside the bore for treatment. Needle insertion was guided by real-time US imaging fused with the reformatted static MR image to enhance soft tissue contrast. Feasibility, targeting accuracy, and MR compatibility of the system were evaluated using a bovine liver and agar phantoms. RESULTS: Targeting error for 50 needle insertions was 1.6 ± 0.6 mm (mean ± standard deviation). The experiment confirmed that fused MR and US images provided real-time needle localization against static MR images with soft tissue contrast. CONCLUSIONS: The proposed MR and US multi-modal image-guided navigation system using a needle manipulator enabled accurate needle insertion by taking advantage of static MR and real-time US images simultaneously. Real-time visualization helped determine needle depth, tissue monitoring surrounding the needle path, target organ shifts, and needle deviation from the path.
Christian Herz, Kyle MacNeil, Peter A Behringer, Junichi Tokuda, Alireza Mehrtash, Parvin Mousavi, Ron Kikinis, Fiona M Fennessy, Clare M Tempany, Kemal Tuncali, and Andriy Fedorov. 2/2020. “Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 565-76.Abstract
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Yuanqian Gao, Kiyoshi Takagi, Takahisa Kato, Naoyuki Shono, and Nobuhiko Hata. 2/2020. “Continuum Robot With Follow-the-Leader Motion for Endoscopic Third Ventriculostomy and Tumor Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 379-90.Abstract
BACKGROUND: In a combined endoscopic third ventriculostomy (ETV) and endoscopic tumor biopsy (ETB) procedure, an optimal tool trajectory is mandatory to minimize trauma to surrounding cerebral tissue. OBJECTIVE: This paper presents wire-driven multi-section robot with push-pull wire. The robot is tested to attain follow-the-leader (FTL) motion to place surgical instruments through narrow passages while minimizing the trauma to tissues. METHODS: A wire-driven continuum robot with six sub-sections was developed and its kinematic model was proposed to achieve FTL motion. An accuracy test to assess the robot's ability to attain FTL motion along a set of elementary curved trajectory was performed. We also used hydrocephalus ventricular model created from human subject data to generate five ETV/ETB trajectories and conducted a study assessing the accuracy of the FTL motion along these clinically desirable trajectories. RESULTS: In the test with elementary curved paths, the maximal deviation of the robot was increased from 0.47 mm at 30 turn to 1.78 mm at 180 in a simple C-shaped curve. S-shaped FTL motion had lesser deviation ranging from 0.16 to 0.18 mm. In the phantom study, the greatest tip deviation was 1.45 mm, and the greatest path deviation was 1.23 mm. CONCLUSION: We present the application of a continuum robot with FTL motion to perform a combined ETV/ETB procedure. The validation study using human subject data indicated that the accuracy of FTL motion is relatively high. The study indicated that FTL motion may be useful tool for combined ETV and ETB.
Haoyin Zhou, Tao Zhang, and Jayender Jagadeesan. 12/2019. “Re-weighting and 1-Point RANSAC-Based P nP Solution to Handle Outliers.” IEEE Trans Pattern Anal Mach Intell, 41, 12, Pp. 3022-33.Abstract
The ability to handle outliers is essential for performing the perspective- n-point (P nP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P nP solution named R1PP nP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P nP algorithm, which serves as the core of R1PP nP, for solving the P nP problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP nP is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP nP is among the most accurate and fast P nP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP nP, which is the state-of-the-art P nP algorithm with an explicit outliers-handling mechanism, R1PP nP is slower but does not suffer from the percentage of outliers limitation as REPP nP.
JP Guenette, N Ben-Shlomo, J Jayender, RT Seethamraju, V Kimbrell, N-A Tran, RY Huang, CJ Kim, JI Kass, CE Corrales, and TC Lee. 11/2019. “MR Imaging of the Extracranial Facial Nerve with the CISS Sequence.” AJNR Am J Neuroradiol, 40, 11, Pp. 1954-9.Abstract
BACKGROUND AND PURPOSE: MR imaging is not routinely used to image the extracranial facial nerve. The purpose of this study was to determine the extent to which this nerve can be visualized with a CISS sequence and to determine the feasibility of using that sequence for locating the nerve relative to tumor. MATERIALS AND METHODS: Thirty-two facial nerves in 16 healthy subjects and 4 facial nerves in 4 subjects with parotid gland tumors were imaged with an axial CISS sequence protocol that included 0.8-mm isotropic voxels on a 3T MR imaging system with a 64-channel head/neck coil. Four observers independently segmented the 32 healthy subject nerves. Segmentations were compared by calculating average Hausdorff distance values and Dice similarity coefficients. RESULTS: The primary bifurcation of the extracranial facial nerve into the superior temporofacial and inferior cervicofacial trunks was visible on all 128 segmentations. The mean of the average Hausdorff distances was 1.2 mm (range, 0.3-4.6 mm). Dice coefficients ranged from 0.40 to 0.82. The relative position of the facial nerve to the tumor could be inferred in all 4 tumor cases. CONCLUSIONS: The facial nerve can be seen on CISS images from the stylomastoid foramen to the temporofacial and cervicofacial trunks, proximal to the parotid plexus. Use of a CISS protocol is feasible in the clinical setting to determine the location of the facial nerve relative to tumor.
Niravkumar A Patel, Gang Li, Weijian Shang, Marek Wartenberg, Tamas Heffter, Everette C Burdette, Iulian Iordachita, Junichi Tokuda, Nobuhiko Hata, Clare M Tempany, and Gregory S. Fischer. 6/2019. “System Integration and Preliminary Clinical Evaluation of a Robotic System for MRI-Guided Transperineal Prostate Biopsy.” J Med Robot Res, 4, 2.Abstract
This paper presents the development, preclinical evaluation, and preliminary clinical study of a robotic system for targeted transperineal prostate biopsy under direct interventional magnetic resonance imaging (MRI) guidance. The clinically integrated robotic system is developed based on a modular design approach, comprised of surgical navigation application, robot control software, MRI robot controller hardware, and robotic needle placement manipulator. The system provides enabling technologies for MRI-guided procedures. It can be easily transported and setup for supporting the clinical workflow of interventional procedures, and the system is readily extensible and reconfigurable to other clinical applications. Preclinical evaluation of the system is performed with phantom studies in a 3 Tesla MRI scanner, rehearsing the proposed clinical workflow, and demonstrating an in-plane targeting error of 1.5mm. The robotic system has been approved by the institutional review board (IRB) for clinical trials. A preliminary clinical study is conducted with the patient consent, demonstrating the targeting errors at two biopsy target sites to be 4.0 and 3.7, which is sufficient to target a clinically significant tumor foci. First-in-human trials to evaluate the system's effectiveness and accuracy for MR image-guide prostate biopsy are underway.
Marek Wartenberg, Joseph Schornak, Katie Gandomi, Paulo Carvalho, Chris Nycz, Niravkumar Patel, Iulian Iordachita, Clare Tempany, Nobuhiko Hata, Junichi Tokuda, and Gregory S. Fischer. 10/2018. “Closed-Loop Active Compensation for Needle Deflection and Target Shift During Cooperatively Controlled Robotic Needle Insertion.” Ann Biomed Eng, 46, 10, Pp. 1582-94.Abstract
Intra-operative imaging is sometimes available to assist needle biopsy, but typical open-loop insertion does not account for unmodeled needle deflection or target shift. Closed-loop image-guided compensation for deviation from an initial straight-line trajectory through rotational control of an asymmetric tip can reduce targeting error. Incorporating robotic closed-loop control often reduces physician interaction with the patient, but by pairing closed-loop trajectory compensation with hands-on cooperatively controlled insertion, a physician's control of the procedure can be maintained while incorporating benefits of robotic accuracy. A series of needle insertions were performed with a typical 18G needle using closed-loop active compensation under both fully autonomous and user-directed cooperative control. We demonstrated equivalent improvement in accuracy while maintaining physician-in-the-loop control with no statistically significant difference (p > 0.05) in the targeting accuracy between any pair of autonomous or individual cooperative sets, with average targeting accuracy of 3.56 mm. With cooperatively controlled insertions and target shift between 1 and 10 mm introduced upon needle contact, the system was able to effectively compensate up to the point where error approached a maximum curvature governed by bending mechanics. These results show closed-loop active compensation can enhance targeting accuracy, and that the improvement can be maintained under user directed cooperative insertion.
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