Mr. Rohan C. Vijayan Profile

Rohan C. Vijayan

at Johns Hopkins Univ

SPIE Involvement:

Author

Publications (18)

Proceedings Article | 3 April 2023 Presentation + Paper

Multi-body 3D-2D registration for robot-assisted joint reduction: preclinical evaluation in the ankle syndesmosis

N. Sheth, R. Vijayan, K. Venkataraman, G. Li, J. Wei, B. Shafiq, J. Siewerdsen, W. Zbijewski, K. Cleary, A. Uneri

Proceedings Volume 12466, 124661F (2023) https://doi.org/10.1117/12.2654481

KEYWORDS: Image registration, Bone, Robotics, Radiography, Orthopedic imaging, Robotic surgery, Image guided intervention, Fluoroscopy, X-ray computed tomography

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Proceedings Article | 4 April 2022 Paper

Motion-compensated targeting in pulmonary interventions using cone-beam CT and locally rigid / globally deformable 3D-2D registration

Rohan Vijayan, Niral Sheth, Lina Mekki, Alex Lu, Ali Uneri, Alejandro Sisniega, Jessica Maggaragia, Gerhard Kleinszig, Sebastian Vogt, Jeffrey Thiboutot, Hans Lee, Lonny Yarmus, Jeffrey Siewerdsen

Proceedings Volume 12034, 120342M (2022) https://doi.org/10.1117/12.2612986

KEYWORDS: Lung, Image registration, Bone, Fluoroscopy, Detection and tracking algorithms, Image processing, Image enhancement, Robotic systems, Navigation systems

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Proceedings Article | 4 April 2022 Presentation + Paper

Digital application to display brain shift simulation in tumor resection procedures

Kush Hari, Rohan Vijayan, Ma Luo, Jaime Tierney, Lola Chambless, Reid Thompson, Michael Miga, Jon Heiselman

Proceedings Volume 12034, 120341J (2022) https://doi.org/10.1117/12.2611479

KEYWORDS: Brain, Surgery, 3D displays, 3D modeling, Prototyping, Mixed reality, Head, Tumors, Neuroimaging, Data modeling

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SPIE Journal Paper | 9 June 2021

Development of a fluoroscopically guided robotic assistant for instrument placement in pelvic trauma surgery

Rohan Vijayan, Runze Han, Pengwei Wu, Niral Sheth, Michael Ketcha, Prasad Vagdargi, Sebastian Vogt, Gerhard Kleinszig, Greg Osgood, Jeffrey Siewerdsen, Ali Uneri

JMI, Vol. 8, Issue 03, 035001, (June 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.3.035001

KEYWORDS: Image registration, Robotics, Calibration, Bone, Surgery, Imaging systems, Computed tomography, Solid modeling, Instrument modeling, Robotic systems

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Proceedings Article | 25 February 2021 Poster + Presentation + Paper

Intraoperative guidance of orthopaedic instruments using 3D correspondence of 2D object instance segmentations

R. Vijayan, R. Han, N. Sheth, A. Uneri, I. Bataeva, K. Shah, G. Kleinszig, S. Vogt, G. Osgood, J. Siewerdsen

Proceedings Volume 11598, 1159826 (2021) https://doi.org/10.1117/12.2582239

KEYWORDS: 3D modeling, Image segmentation, Process modeling, Radiography, 3D image processing, Surgery, Instrument modeling, Imaging systems, Image registration, Fluoroscopy

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Proceedings Article | 25 February 2021 Presentation + Paper

Fluoroscopic guidance of a surgical robot: pre-clinical evaluation in pelvic guidewire placement

R. Vijayan, R. Han, P. Wu, N. Sheth, P. Vagdargi, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen, A. Uneri

Proceedings Volume 11598, 115981G (2021) https://doi.org/10.1117/12.2582188

KEYWORDS: Radiography, Computer aided design, Solid modeling, Instrument modeling, Surgery, Robotics, Robotic systems, Prototyping, Process modeling, Imaging devices

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SPIE Journal Paper | 13 May 2020

Automatic analysis of global spinal alignment from simple annotation of vertebral bodies

Sophia Doerr, Tharindu De Silva, Rohan Vijayan, Runze Han, Ali Uneri, Michael Ketcha, Xiaoxuan Zhang, Nishanth Khanna, Erick Westbroek, Bowen Jiang, Corinna Zygourakis, Nafi Aygun, Nicholas Theodore, Jeffrey Siewerdsen

JMI, Vol. 7, Issue 03, 035001, (May 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.3.035001

KEYWORDS: Image segmentation, Computed tomography, Spine, Radiography, Surgery, 3D image processing, Image analysis, Visualization, Reliability, Image-guided intervention

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Proceedings Article | 16 March 2020 Presentation + Paper

Image-guided robotic k-wire placement for orthopaedic trauma surgery

R. Vijayan, R. Han, P. Wu, N. Sheth, M. Ketcha, P. Vagdargi, G. Osgood, J. Siewerdsen, A. Uneri, S. Vogt, G. Kleinszig

Proceedings Volume 11315, 113151A (2020) https://doi.org/10.1117/12.2549713

KEYWORDS: Radiography, Robotics, Surgery, 3D modeling, Image-guided intervention, Image registration, Computed tomography

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Purpose. We report the initial development of an image-based solution for robotic assistance of pelvic fracture fixation. The approach uses intraoperative radiographs, preoperative CT, and an end effector of known design to align the robot with target trajectories in CT. The method extends previous work to solve the robot-to-patient registration from a single radiographic view (without C-arm rotation) and addresses the workflow challenges associated with integrating robotic assistance in orthopaedic trauma surgery in a form that could be broadly applicable to isocentric or non-isocentric C-arms. Methods. The proposed method uses 3D-2D known-component registration to localize a robot end effector with respect to the patient by: (1) exploiting the extended size and complex features of pelvic anatomy to register the patient; and (2) capturing multiple end effector poses using precise robotic manipulation. These transformations, along with an offline hand-eye calibration of the end effector, are used to calculate target robot poses that align the end effector with planned trajectories in the patient CT. Geometric accuracy of the registrations was independently evaluated for the patient and the robot in phantom studies. Results. The resulting translational difference between the ground truth and patient registrations of a pelvis phantom using a single (AP) view was 1.3 mm, compared to 0.4 mm using dual (AP+Lat) views. Registration of the robot in air (i.e., no background anatomy) with five unique end effector poses achieved mean translational difference ~1.4 mm for K-wire placement in the pelvis, comparable to tracker-based margins of error (commonly ~2 mm). Conclusions. The proposed approach is feasible based on the accuracy of the patient and robot registrations and is a preliminary step in developing an image-guided robotic guidance system that more naturally fits the workflow of fluoroscopically guided orthopaedic trauma surgery. Future work will involve end-to-end development of the proposed guidance system and assessment of the system with delivery of K-wires in cadaver studies.

Proceedings Article | 16 March 2020 Presentation + Paper

Multi-body registration for fracture reduction in orthopaedic trauma surgery

J. Siewerdsen, R. Han, A. Uneri, P. Wu, P. Vagdargi, R. Vijayan, M. Ketcha, N. Sheth, S. Vogt, G. Kleinszig, G. Osgood

Proceedings Volume 11315, 113150F (2020) https://doi.org/10.1117/12.2549708

KEYWORDS: Image segmentation, Image registration, Surgery, Statistical modeling, 3D modeling, X-ray computed tomography, Computer aided diagnosis and therapy, Optimization (mathematics), Orthopedic imaging

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Proceedings Article | 16 March 2020 Paper

SpineCloud: image analytics for predictive modeling of spine surgery outcomes

S. Vedula, A. Perdomo-Pantoja, T. Witham, N. Theodore, J. Siewerdsen, B. Caffo, T. De Silva, R. Vijayan, S. Doerr, A. Uneri, R. Han, M. Ketcha, R. Skolasky, G. Hager

Proceedings Volume 11315, 113151O (2020) https://doi.org/10.1117/12.2566372

KEYWORDS: Surgery, Spine, Image analysis, Data modeling, Analytics, Cadmium sulfide, Feature extraction, Medicine, Performance modeling, Medical imaging

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Spinal degeneration and deformity present an enormous healthcare burden, with spine surgery among the main treatment modalities. Unfortunately, spine surgery (e.g., lumbar fusion) exhibits broad variability in the quality of outcome, with ~20-40% of patients gaining no benefit in pain or function (“failed back surgery”) and earning criticism that is difficult to reconcile versus rapid growth in frequency and cost over the last decade. Vital to advancing the quality of care in spine surgery are improved clinical decision support (CDS) tools that are accurate, explainable, and actionable: accurate in prediction of outcomes; explainable in terms of the physical / physiological factors underlying the prediction; and actionable within the shared decision process between a surgeon and patient in identifying steps that could improve outcome. This technical note presents an overview of a novel outcome prediction framework for spine surgery (dubbed SpineCloud) that leverages innovative image analytics in combination with explainable prediction models to achieve accurate outcome prediction. Key to the SpineCloud framework are image analysis methods for extraction of high-level quantitative features from multi-modality peri-operative images (CT, MR, and radiography) related to spinal morphology (including bone and soft-tissue features), the surgical construct (including deviation from an ideal reference), and longitudinal change in such features. The inclusion of such image-based features is hypothesized to boost the predictive power of models that conventionally rely on demographic / clinical data alone (e.g., age, gender, BMI, etc.). Preliminary results using gradient boosted decision trees demonstrate that such prediction models are explainable (i.e., why a particular prediction is made), actionable (identifying features that may be addressed by the surgeon and/or patient), and boost predictive accuracy compared to analysis based on demographics alone (e.g., AUC improved by ~25% in preliminary studies). Incorporation of such CDS tools in spine surgery could fundamentally alter and improve the shared decisionmaking process between surgeons and patients by highlighting actionable features to improve selection of therapeutic and rehabilitative pathways.

Proceedings Article | 16 March 2020 Presentation + Paper

Method for metal artifact avoidance in C-arm cone-beam CT

J. Siewerdsen, P. Wu, N. Sheth, A. Sisniega, A. Uneri, R. Han, B. Kreher, G. Kleinszig, S. Vogt, R. Vijayan, H. Kunze, P. Vagdargi, S.-F. Lo, N. Theodore

Proceedings Volume 11312, 1131226 (2020) https://doi.org/10.1117/12.2549840

KEYWORDS: Metals, Calibration, Image segmentation, Signal attenuation, Chest, Spine, Imaging systems, Surgery, Modulation transfer functions, Visualization

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Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories.

SPIE Journal Paper | 18 February 2020 Open Access

SpineCloud: image analytics for predictive modeling of spine surgery outcomes

Tharindu De Silva, Satyanarayana Vedula, Alexander Perdomo-Pantoja, Rohan Vijayan, Sophia Doerr, Ali Uneri, Runze Han, Michael Ketcha, Richard Skolasky, Timothy Witham, Nicholas Theodore, Jeffrey Siewerdsen

JMI, Vol. 7, Issue 03, 031502, (February 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.3.031502

KEYWORDS: Surgery, Spine, Analytics, Data modeling, Image analysis, Performance modeling, Binary data, Computed tomography, Brain-machine interfaces, Image segmentation

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Proceedings Article | 8 March 2019 Presentation + Paper

Automatic analysis of global spinal alignment from spine CT images

J. Siewerdsen, S. Doerr, T. De Silva, R. Vijayan, R. Han, A. Uneri, X. Zhang, C. Zygourakis, N. Theodore, M. Ketcha, N. Khanna, B. Jiang, E. Westbroek, N. Aygun

Proceedings Volume 10951, 1095104 (2019) https://doi.org/10.1117/12.2513975

KEYWORDS: Image segmentation, Computed tomography, Spine, Error analysis, Surgery, Image processing algorithms and systems, Clouds, Principal component analysis, 3D image processing, Radiography

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Purpose: A method for automatic computation of global spinal alignment (GSA) metrics is presented to mitigate the high variability of manual definitions in radiographic images. The proposed algorithm segments vertebral endplates in CT as a basis for automatic computation of metrics of global spinal morphology. The method is developed as a potential tool for intraoperative guidance in deformity correction surgery, and/or automatic definition of GSA in large datasets for analysis of surgical outcome. Methods: The proposed approach segments vertebral endplates in spine CT images using vertebral labels as input. The segmentation algorithm extracts vertebral boundaries using a continuous max-flow algorithm and segments the vertebral endplate surface by region-growing. The point cloud of the segmented endplate is forward-projected as a digitally reconstructed radiograph (DRR), and a linear fit is computed to extract the endplate angle in the radiographic plane. Two GSA metrics (lumbar lordosis and thoracic kyphosis) were calculated using these automatically measured endplate angles. Experiments were performed in seven patient CT images acquired from Spineweb and accuracy was quantified by comparing automatically-computed endplate angles and GSA metrics to manual definitions. Results: Endplate angles were automatically computed with median accuracy = 2.7°, upper quartile (UQ) = 4.8°, and lower quartile (LQ) = 1.0° with respect to manual ground-truth definitions. This was within the measured intra- observer variability = 3.1° (RMS) of manual definitions. GSA metrics had median accuracy = 1.1° (UQ = 3.1°) for lumbar lordosis and median accuracy = 0.4° (UQ = 3.0°) for thoracic kyphosis. The performance of GSA measurements was also within the variability of the manual approach. Conclusions: The method offers a potential alternative to time-consuming, manual definition of endplate angles for GSA computation. Such automatic methods could provide a means of intraoperative decision support in correction of spinal deformity and facilitate data-intensive analysis in identifying metrics correlating with surgical outcomes.

Proceedings Article | 8 March 2019 Presentation + Paper

Automatic vertebrae localization in spine CT: a deep-learning approach for image guidance and surgical data science

J. Siewerdsen, M. Levine, T. De Silva, R. Vijayan, S. Doerr, A. Uneri, S. Vedula, N. Theodore, M. Ketcha

Proceedings Volume 10951, 109510S (2019) https://doi.org/10.1117/12.2513915

KEYWORDS: Machine learning, Image-guided intervention, Spine, Computed tomography, 3D image processing, Algorithm development, Surgery, Image analysis

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Motivation/Purpose: This work reports the development and validation of an algorithm to automatically detect and localize vertebrae in CT images of patients undergoing spine surgery. Slice-by-slice detections using the state-of-the art 2D convolutional neural network (CNN) architectures were combined to estimate vertebra centroid location in 3D including a method that combined detections in sagittal and coronal slices. The solution facilitates applications in image guided surgery and automatic computation of image analytics for surgical data science. Methods: CNN-based object detection models in 3D (volume) and 2D (slice) images were implemented and evaluated for the task of vertebrae detection. Slice-by-slice detections in 2D architectures were combined to estimate the 3D centroid location including a model that simultaneously evaluated 2D detections in orthogonal directions (i.e., sagittal and coronal slices) to improve the robustness against spurious false detections – called Ortho-2D. Performance was evaluated in a data set consisting of 85 patients undergoing spine surgery at our institution, including images presenting spinal instrumentation/implants, spinal deformity, and anatomical abnormalities that are realistic exemplars of pathology in the patient population. Accuracy was quantified in terms of precision, recall, F1 score, and the 3D geometric error in vertebral centroid annotation compared to ground truth (expert manual) annotation. Results: Three CNN object detection models were able to successfully localize vertebrae, with Ortho-2D model that combined 2D detections in orthogonal directions achieving best performance: precision = 0.95, recall = 0.99, and F1 score = 0.97. Overall centroid localization accuracy was 3.4 mm (median) [interquartile range (IQR) = 2.7 mm], and ~97% of detections (154/159 lumbar cases) yielded acceptable centroid localization error <15 mm (considering average vertebrae size ~25 mm). Conclusions: State-of-the-art CNN architectures were adapted for vertebral centroid annotation, yielding accurate and robust localization even in the presence of anatomical abnormalities, image artifacts, and dense instrumentation. The methods are employed as a basis for streamlined image guidance (automatic initialization of 3D-2D and 3D-3D registration methods in image-guided surgery) and as an automatic spine labeling tool to generate image analytics.

Proceedings Article | 8 March 2019 Presentation + Paper

Automatic trajectory and instrument planning for robot-assisted spine surgery

Rohan Vijayan, Tharindu De Silva, Runze Han, Ali Uneri, Sophia Doerr, Michael Ketcha, Alexander Perdomo-Pantoja, Nicholas Theodore, Jeffrey Siewerdsen

Proceedings Volume 10951, 1095102 (2019) https://doi.org/10.1117/12.2513722

KEYWORDS: Spine, Image registration, Surgery, Computed tomography, Navigation systems, Algorithm development, Principal component analysis, Statistical modeling, Image-guided intervention, Robotic surgery, Robotic systems

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Purpose. We report the initial implementation of an algorithm that automatically plans screw trajectories for spinal pedicle screw placement procedures to improve the workflow, accuracy, and reproducibility of screw placement in freehand navigated and robot-assisted spinal pedicle screw surgery. In this work, we evaluate the sensitivity of the algorithm to the settings of key parameters in simulation studies. Methods. Statistical shape models (SSMs) of the lumbar spine were constructed with segmentations of L1-L5 and bilateral screw trajectories of N=40 patients. Active-shape model (ASM) registration was devised to map the SSMs to the patient CT, initialized simply by alignment of (automatically annotated) single-point vertebral centroids. The atlas was augmented by definition of “ideal / reference” trajectories for each spinal pedicle, and the trajectories are deformably mapped to the patient CT. A parameter sensitivity analysis for the ASM method was performed on 3 parameters to determine robust operating points for ASM registration. The ASM method was evaluated by calculating the root-mean-square-error between the registered SSM and the ground-truth segmentation for the L1 vertebra, and the trajectory planning method was evaluated by performing a leave-one-out analysis and determining the entry point, end point, and angular differences between the automatically planned trajectories and the neurosurgeon-defined reference trajectories. Results. The parameter sensitivity analysis showed that the ASM registration algorithm was relatively insensitive to initial profile length (P_Linitial) less than ~4 mm, above which runtime and registration error increased. Similarly stable performance was observed for a maximum number of principal components (PC_max) of at least 8. Registration error ~2 mm was evident with diminishing return beyond a number of iterations, N_iter, ~2000. With these parameter settings, ASM registration of L1 achieved (2.0 ± 0.5) mm RMSE. Transpedicle trajectories for L1 agreed with reference definition by (2.6 ± 1.3) mm at the entry point, by (3.4 ± 1.8) mm at the end point, and within (4.9° ±2.8°) in angle. Conclusions. Initial results suggest that the algorithm yields accurate definition of pedicle trajectories in unsegmented CT images of the spine. The studies identified stable operating points for key algorithm parameters and support ongoing development and translation to clinical studies in free-hand navigated and robot-assisted spine surgery, where fast, accurate trajectory definition is essential to workflow.

Proceedings Article | 13 March 2018 Presentation + Paper

Trackerless surgical image-guided system design using an interactive extension of 3D Slicer

Xiaochen Yang, Reid Thompson, Rohan Vijayan, Ma Luo, Logan Clements, Benoit Dawant, Michael Miga

Proceedings Volume 10576, 105761F (2018) https://doi.org/10.1117/12.2295229

KEYWORDS: Head, Tumors, 3D image processing, Optical tracking, Surgery, Image registration, Clouds, Magnetic resonance imaging, Visualization

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Conventional optical tracking systems use cameras sensitive to near-infra-red (NIR) light detecting cameras and passively/actively NIR-illuminated markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely-used within the neurosurgical theatre and is a staple in the standard of care in craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure with the patient in a fixed OR head presentation orientation. In the work presented herein, we propose a framework to achieve this in the OR that is free of conventional tracking technology, i.e. a trackerless approach. Briefly, we are investigating a collaborative extension of 3D slicer that combines surgical planning and craniotomy designation in a novel manner. While taking advantage of the well-developed 3D slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with a novel intraoperative MR-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without use of a conventional optical tracking technology. To test this novel approach, an experienced neurosurgeon performed experiments on four different mock surgical cases using our module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this early-stage craniotomy planning and delivery approach, and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental new realization of an integrated trackerless surgical guidance platform.

SPIE Journal Paper | 2 March 2017

Android application for determining surgical variables in brain-tumor resection procedures

Rohan Vijayan, Reid Thompson, Lola Chambless, Peter Morone, Le He, Logan Clements, Rebekah Griesenauer, Hakmook Kang, Michael Miga

JMI, Vol. 4, Issue 01, 015003, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.015003

KEYWORDS: Head, Surgery, Tumors, Brain, 3D modeling, Tablets, Image registration, Neuroimaging, Image segmentation, Visualization

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Proceedings Article | 18 March 2016 Paper

Determination of surgical variables for a brain shift correction pipeline using an Android application

Michael Miga, Rohan Vijayan, Rebekah Conley, Logan Clements, Reid Thompson

Proceedings Volume 9786, 978610 (2016) https://doi.org/10.1117/12.2216991

KEYWORDS: Brain, Head, Neuroimaging, 3D modeling, Tablets, Data acquisition, Surgery, Computer graphics, Process modeling, Tumors, Interfaces, Image registration

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