Average Target Registration Error Research Articles

A back propagation neural network based respiratory motion modelling method.

This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases. Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement. The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511mm. The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.

Comparing Regularized Kelvinlet Functions and the Finite Element Method for Registration of Medical Images to Sparse Organ Data

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While finite element models can be effective in sparse data scenarios, computation time remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom liver deformation dataset and an in vivo breast deformation dataset. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between methods. Average target registration errors were 4.6 ± 1.0 and 3.2 ± 0.8 mm on the liver dataset and 5.4 ± 1.4 and 6.4 ± 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method, respectively. Limitations of regularized Kelvinlets include the lack of organ-specific geometry and the assumptions of linear elasticity and infinitesimal strain. Despite limitations, this work demonstrates the generalizability of regularized Kelvinlets registration on two soft-tissue elastic organs. This method may improve and accelerate registration for image-guided surgery, and it shows the potential of using regularized Kelvinlets on medical imaging data.

An alternately optimized generative adversarial network with texture and content constraints for deformable registration of 3D ultrasound images

Objective. 3D ultrasound non-rigid registration is significant for intraoperative motion compensation. Nevertheless, distorted textures in the registered image due to the poor image quality and low signal-to-noise ratio of ultrasound images reduce the accuracy and efficiency of the existing methods. Approach. A novel 3D ultrasound non-rigid registration objective function with texture and content constraints in both image space and multiscale feature space based on an unsupervised generative adversarial network based registration framework is proposed to eliminate distorted textures. A similarity metric in the image space is formulated based on combining self-structural constraint with intensity to strengthen the robustness to abnormal intensity change compared with common intensity-based metrics. The proposed framework takes two discriminators as feature extractors to formulate the texture and content similarity between the registered image and the fixed image in the multiscale feature space respectively. A distinctive alternating training strategy is established to jointly optimize the combination of various similarity loss functions to overcome the difficulty and instability of training convergence and balance the training of generator and discriminators. Main results. Compared with five registration methods, the proposed method is evaluated both with small and large deformations, and achieves the best registration accuracy with average target registration error of 1.089 mm and 2.139 mm in cases of small and large deformations, respectively. The performance on peak signal to noise ratio (PSNR) and structural similarity (SSIM) also proves the effective constraints on distorted textures of the proposed method (PSNR is 31.693 dB and SSIM is 0.9 in the case of small deformation; PSNR is 28.177 dB and SSIM is 0.853 in the case of large deformation). Significance. The proposed 3D ultrasound non-rigid registration method based on texture and content constraints with the distinctive alternating training strategy can eliminate the distorted textures with improving the registration accuracy.

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration.

Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging. Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators. Average target registration errors were for a biomechanical image registration algorithm and for a diffeomorphic intensity-based algorithm, corresponding to 1-2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators. Volumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Towards transcervical ultrasound image guidance for transoral robotic surgery.

Trans-oral robotic surgery (TORS) using the da Vinci surgical robot is a new minimally-invasive surgery method to treat oropharyngeal tumors, but it is a challenging operation. Augmented reality (AR) based on intra-operative ultrasound (US) has the potential to enhance the visualization of the anatomy and cancerous tumors to provide additional tools for decision-making in surgery. We propose a US-guided AR system for TORS, with the transducer placed on the neck for a transcervical view. Firstly, we perform a novel MRI-to-transcervical 3D US registration study, comprising (i) preoperative MRI to preoperative US registration, and (ii) preoperative to intraoperative US registration to account for tissue deformation due to retraction. Secondly, we develop a US-robot calibration method with an optical tracker and demonstrate its use in an AR system that displays anatomy models in the surgeon's console in real-time. Our AR system achieves a projection error from the US to the stereo cameras of 27.14 and 26.03 pixels (image is 540[Formula: see text]960) in a water bath experiment. The average target registration error (TRE) for MRI to 3D US is 8.90mm for the 3D US transducer and 5.85mm for freehand 3D US, and the TRE for pre-intra operative US registration is 7.90mm. We demonstrate the feasibility of each component of the first complete pipeline for MRI-US-robot-patient registration for a proof-of-concept transcervical US-guided AR system for TORS. Our results show that trans-cervical 3D US is a promising technique for TORS image guidance.

Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy

Objective. CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, and daily OAR variation throughout the course of treatment. The purpose of this study is to propose an unsupervised deep learning-based CBCT-CBCT deformable image registration which enables quantitative anatomic variation analysis. Approach. The proposed deformable registration workflow consists of training and inference stages that share the same feed-forward path through a spatial transformation-based network (STN). The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively. The network was trained by minimizing the image similarity loss and the deformable vector field (DVF) regularization loss without the supervision of ground truth DVFs. During the inference stage, patches of local DVF were predicted by the trained LocalGAN and fused to form a whole-image DVF. The local whole-image DVF was subsequently combined with the GlobalGAN generated DVF to obtain the final DVF. The proposed method was evaluated using 100 fractional CBCTs from 20 abdominal cancer patients in the experiments and 105 fractional CBCTs from a cohort of 21 different abdominal cancer patients in a holdout test. Main Results. Qualitatively, the registration results show good alignment between the deformed CBCT images and the target CBCT image. Quantitatively, the average target registration error calculated on the fiducial markers and manually identified landmarks was 1.91 ± 1.18 mm. The average mean absolute error, normalized cross correlation between the deformed CBCT and target CBCT were 33.42 ± 7.48 HU, 0.94 ± 0.04, respectively. Significance. In summary, an unsupervised deep learning-based CBCT-CBCT registration method is proposed and its feasibility and performance in fractionated image-guided radiotherapy is investigated. This promising registration method could provide fast and accurate longitudinal CBCT alignment to facilitate inter-fractional anatomic changes analysis and prediction.

Measurement and Analysis of Lobar Lung Deformation After a Change of Patient Position During Video-Assisted Thoracoscopic Surgery.

Video-assisted thoracoscopic surgery (VATS) is a minimally invasive surgical technique for the diagnosis and treatment of early-stage lung cancer. During VATS, large lung deformation occurs as a result of a change of patient position and a pneumothorax (lung deflation), which hinders the intraoperative localization of pulmonary nodules. Modeling lung deformation during VATS for surgical navigation is desirable, but the mechanisms causing such deformation are yet not well-understood. In this study, we estimate, quantify and analyze the lung deformation occurring after a change of patient position during VATS. We used deformable image registration to estimate the lung deformation between a preoperative CT (in supine position) and an intraoperative CBCT (in lateral decubitus position) of six VATS clinical cases. We accounted for sliding motion between lobes and against the thoracic wall and obtained consistently low average target registration errors (under 1mm). We observed large lung displacement (up to 40mm); considerable sliding motion between lobes and against the thoracic wall (up to 30mm); and localized volume changes indicating deformation. These findings demonstrate the complexity of the change of patient position phenomenon, which should necessarily be taken into account to model lung deformation for intraoperative guidance during VATS.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling

BackgroundSimulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow. MethodsA supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model. FindingsThe average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01). InterpretationWhile a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.

Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom.

The purpose of this study was to evaluate the deformable image registration (DIR) accuracy using various CT scan parameters with deformable thorax phantom. Our developed deformable thorax phantom (Dephan, Chiyoda Technol Corp, Tokyo, Japan) was used. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180mm, which simulates the chest wall. The inner phantom consists of deformable, 20mm thick disk-shaped sponges with 48 Lucite beads and 48 nylon cross-wires which simulate the vascular and bronchial bifurcations of the lung. Peak-exhale and peak-inhale images of the deformable phantom were acquired using a CT scanner (Aquilion LB, TOSHIBA). To evaluate the impact of CT scan parameters on DIR accuracy, we used the four tube voltages (80, 100, 120, and 135kV) and six reconstruction algorithms (FC11, FC13, FC15, FC41, FC44, and FC52). Intensity-based DIR was performed between the two images using MIM Maestro (MIM software, Cleveland, USA). Fiducial markers (beads and cross-wires) based target registration error (TRE) was used for quantitative evaluation of DIR. In case with different tube voltages, the range of average TRE were 4.44-5.69mm (reconstruction algorithm: FC13). In case with different reconstruction algorithms, the range of average TRE were 4.26-4.59mm (tube voltage: 120kV). The TRE were differed by up to 3.0mm (3.96-6.96mm) depending on the combination of tube voltage and reconstruction algorithm. Our result indicated that CT scan parameters had moderate impact of TRE, especially for reconstruction algorithms for the deformable thorax phantom.

Multimodal registration of ultrasound and MR images using weighted self-similarity structure vector

ProposeMultimodal registration of 2D Ultrasound (US) and 3D Magnetic Resonance (MR) for fusion navigation can improve the intraoperative detection accuracy of lesion. However, multimodal registration remains a challenge because of the poor US image quality. In the study, a weighted self-similarity structure vector (WSSV) is proposed to registrate multimodal images. MethodThe self-similarity structure vector utilizes the normalized distance of symmetrically located patches in the neighborhood to describe the local structure information. The texture weights are extracted using the local standard deviation to reduce the speckle interference in the US images. The multimodal similarity metric is constructed by combining a self-similarity structure vector with a texture weight map. ResultsExperiments were performed on US and MR images of the liver from 88 groups of data including 8 patients and 80 simulated samples. The average target registration error was reduced from 14.91 ± 3.86 mm to 4.95 ± 2.23 mm using the WSSV-based method. ConclusionsThe experimental results show that the WSSV-based registration method could robustly align the US and MR images of the liver. With further acceleration, the registration framework can be potentially applied in time-sensitive clinical settings, such as US-MR image registration in image-guided surgery.

An unsupervised image registration method employing chest computed tomography images and deep neural networks

BackgroundDeformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deformation between inspiration and expiration. With advancements in deep learning techniques, learning-based registration methods are considered efficient alternatives to traditional methods in terms of accuracy and computational cost. MethodIn this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expiratory levels without utilizing any ground-truth registration results. Generally, the LRN model uses three loss functions: image similarity, regularization, and Jacobian determinant. Here, LRN was trained on the CT datasets of 705 subjects and tested using 10 pairs of public CT DIR-Lab datasets. Furthermore, to evaluate the effectiveness of the registration technique, target registration errors (TREs) of the LRN model were compared with those of the conventional algorithm (sum of squared tissue volume difference; SSTVD) and a state-of-the-art unsupervised registration method (VoxelMorph). ResultsThe results showed that the LRN with an average TRE of 1.78 ± 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 ± 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 ± 1.49 mm. In addition, estimating the displacement vector field without any folding voxel consumed less than 2 s, demonstrating the superiority of the learning-based method with respect to fiducial marker tracking and the overall soft tissue alignment with a nearly real-time speed. ConclusionsTherefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery.

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery.

During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors. A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume. The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm. These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions. The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

Supine magnetic resonance image registration for breast surgery: insights on material mechanics.

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery. In this study, three registration methods were investigated on healthy volunteers' breasts ( ) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1)point-based rigid registration using synthetic fiducials, (2)nonrigid biomechanical model-based registration using sparse data, and (3)a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics. The average target registration errors (TRE) were , , and (mean ± standard deviation) and the average fiducial registration errors (FRE) were , , and for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration.

Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. Voxel side length of 0.2cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17mm, respectively. The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Pilot Study of Optical Topographic Imaging Based Neuronavigation for Mastoidectomy

Mastoidectomy involves drilling the temporal bone while avoiding the facial nerve, semicircular canals, sigmoid sinus, and tegmen. Optical topographic imaging (OTI) is a novel registration technique that allows rapid registration with minimal navigational error. To date, no studies have examined the use of OTI in skull-base procedures. In this cadaveric study, 8 mastoidectomies were performed in 2 groups-4 free-hand (FH) and 4 OTI-assisted mastoidectomies. Registration accuracy for OTI navigation was quantified with root mean square (RMS) and target registration error (TRE). Procedural time, percent of mastoid resected, and the proximity of the mastoidectomy cavity to critical structures were determined. The average RMS and TRE associated with OTI-based registration were 1.44 mm (±0.83 mm) and 2.17 mm (±0.89 mm), respectively. The volume removed, expressed as a percentage of the total mastoid volume, was 37.5% (±10.2%) versus 31.2% (±2.3%), P= 0.31, for FH and OTI-assisted mastoidectomy. There were no statistically significant differences between FH and OTI-assisted mastoidectomies with respect to proximity to critical structures or procedural time. This work is the first examining the application of OTI neuronavigation in lateral skull-base procedures. This pilot study revealed the RMS and TRE for OTI-based navigation in the lateral skull base are 1.44 mm (±0.83 mm) and 2.17 mm (±0.89 mm), respectively. This pilot study demonstrates that an OTI-based system is sufficiently accurate and may address barriers to widespread adoption of navigation for lateral skull-base procedures.

Automated machine learning (AutoML)-based surface registration methodology for image-guided surgical navigation system.

Although the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy. This research proposes automated machine learning (AutoML)-based surface registration to improve the accuracy of image-guided surgical navigation systems. The state-of-the-art surface registration concept is that first, using a neural network model, a new point-cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography. Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point-cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian optimization with expected improvement to yield improved registration accuracy. Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is 0.939±0.375mm, which shows 57.8% improvement compared to the average TRE of 2.227±0.193mm calculated by the conventional surface registration method (p<0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are 0.767±0.132 and 2.615±0.378mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML-based surface registration is also presented. Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

Target registration errors in navigation-assisted mandibular surgery according to the tracking methods and the type of markers: experiments using human dry mandibular bone.

This study was conducted to evaluate the accuracy of navigation process according to the type of tracking methods and registration markers. The target registration errors (TREs) were measured at seven anatomical landmarks of the mandible. Four different experiments were performed to obtain the TREs using two tracking methods, the optical tracker (Polaris) and the electromagnetic (EM) tracker (Aurora), and two types of registration markers, invasive and noninvasive markers. All comparisons of TREs were statistically analyzed using SPSS and Python-based statistical package. The average TRE values obtained from the four experiments were as follows: (1) 0.85mm (± 0.07) using invasive marker and Aurora, (2) 1.06mm (± 0.12) using invasive marker and Polaris, (3) 1.43mm (± 0.15) using noninvasive marker and Aurora, and (4) 1.57mm (± 0.23) using noninvasive marker and Polaris. Comparisons between all the experimental results revealed statistically significant differences except for the type of tracking system. Although the comparison between the modality of the tracking system showed no significant differences, the EM-based approach consistently demonstrated better performances than the optical type in all comparisons. This study demonstrates that irrespective of the tracking modality, using invasive marker is a better choice in terms of accuracy. When using noninvasive marker, it is important to consider the increased TREs. In this study, the noninvasive marker caused a maximum increment of TREs of 0.81mm compared with the invasive marker. Furthermore, using an EM-based tracker with invasive marker may result in the best accuracy for navigation.

Breast image registration for surgery: Insights on material mechanics modeling.

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

181 Frameless Neuronavigation With Computer Vision and Real Time Tracking for Bedside External Ventricular Drain Placement: A Cadaveric Study

INTRODUCTION: A major obstacle to improving ventriculostomy safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. METHODS: Computer vision technology was used to develop an algorithm for near continuous, automatic, and marker-less image registration. The program fuses a subject’s pre-procedure CT scan to live 3D camera images (Snap-Surface), and movement is incorporated by artificial intelligence driven recalibration (Real-Track). Surface registration error (SRE) and target registration error (TRE) were calculated for five cadaver heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions), and several test conditions. Six catheters were placed in each cadaver (30 placements). Post-procedure CT scans allowed comparison of planned and actual catheter position for user error calculation. RESULTS: Registration was successful for all five cadaveric specimens (average SRE 0.429 mm, ±0.108). Accuracy of TRE was under 1.2mm throughout specimen movements of low and high velocities of roll, pitch, and yaw movements, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p-value = 0.336). Addition of bright light or performance under dim light did not significantly affect SRE (p-value = 0.742 and 0.859, respectively). Average TRE for catheter placements was 0.862 mm (±0.322), and average user error for the was 1.674 mm (±1.195). CONCLUSION: This computer vision-based registration system provides real-time tracking of cadaveric heads with recalibration time of less than one-quarter of a second with sub-millimetric accuracy, and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and could be extrapolated to other frameless stereotactic applications in awake, non-immobilized patients.

Achievable Accuracy of DIR for Tumor/Organ With Large Progressive Shrinkage During the Radiation Treatment: A Bio-Tissue Phantom Study

<h3>Purpose/Objective(s)</h3> The purpose of the study was to investigate voxel-wise accuracy of different deformable registration (DIR) schemes on tumor/organ with large shrinkage during radiotherapy, and to find the most suitable schemes for specific clinical scenarios. <h3>Materials/Methods</h3> An intact porcine liver lobe was utilized and 60 gold markers of 0.5 × 0.5 × 1mm were implanted both on the surface and inside to construct the phantom. To simulate the progressive radiation-induced shrinkage, it was heated by a microwave incrementally from 60 to 200s in 7 phases. For each phase, the phantom was heated and then CT scanned. Two extra image sets were generated from the original CTs by: 1) overriding the gold markers (feature image), and 2) overriding the entire phantom to the mean intensity (featureless image). Ten DIR schemes were evaluated on the secondary images to simulate different combinations of the boundary conditions, availability of image feature or intermediate imaging, and different algorithms as listed in Table 1 (Direct: D, Sequential: S; Surface: SU, Surface-marker: SM; image feature: IF, featureless: NF; IM-DIR: IM, BM-DIR: BM). For example, D/SM/NF/BM means a direct BM-DIR with surface-marker constrain on featureless image. The surface constrain forced the surface matching, while surface-marker constrain further enforced the matching of all surface markers. Direct DIR was between initial and target phase, while sequential DIR utilized all intermediate phases. IM-DIR is intensity based while BM-DIR is a hybrid DIR based on bio-mechanical model. Target registration errors (TRE) using the internal marks as ground truth and Jacobian map (JM) were evaluated for all schemes. <h3>Results</h3> The volume reduction was about 20%, 30% and 40% after 90s, 130s and 200s of heating, respectively. The TREs of all 10 DIR schemes are listed in Table 1. Without image features and with surface constrain, the schemes 1-4 achieved average TRE of 3.8 to 5.0 mm, while schemes 5 to 7 with the surface-marker constrain improved to 2.4 to 3.2mm. The best TRE of 1.9mm was from scheme 10, which combined the image feature, surface-marker constrain and intermediate imaging, but appeared large JM irregularity. The scheme 6 and 7, which utilized BM-DIR with better boundary condition, achieved the second-best TRE of 2.5 and 2.4mm and 90% points with TRE less than 4mm, as well as smooth JM. <h3>Conclusion</h3> Achievable DIR accuracy for tumors with large progressive shrinkage depends on multiple factors such as image feature within and around tumor, volume reduction level, and the underlying regularization of DIR. For tumors with salient feature surrounding its boundary, but in the absence of internal contrast, the hybrid DIR method (scheme 6) could achieve registration accuracy within 4mm.