Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data.

Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data.

Reaction-diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction-diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor 'grown' for ten days as dictated by the reaction-diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model's accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error <8.8%, Dice >0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction-diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.

The Image-to-Physical Liver Registration Sparse Data Challenge: comparison of state-of-the-art using a common dataset.

Purpose: Computational methods for image-to-physical registration during surgical guidance frequently rely on sparse point clouds obtained over a limited region of the organ surface. However, soft tissue deformations complicate the ability to accurately infer anatomical alignments from sparse descriptors of the organ surface. The Image-to-Physical Liver Registration Sparse Data Challenge introduced at SPIE Medical Imaging 2019 seeks to characterize the performance of sparse data registration methods on a common dataset to benchmark and identify effective tactics and limitations that will continue to inform the evolution of image-to-physical registration algorithms. Approach: Three rigid and five deformable registration methods were contributed to the challenge. The deformable approaches consisted of two deep learning and three biomechanical boundary condition reconstruction methods. These algorithms were compared on a common dataset of 112 registration scenarios derived from a tissue-mimicking phantom with 159 subsurface validation targets. Target registration errors (TRE) were evaluated under varying conditions of data extent, target location, and measurement noise. Jacobian determinants and strain magnitudes were compared to assess displacement field consistency. Results: Rigid registration algorithms produced significant differences in TRE ranging from 3.8±2.4 mm to 7.7±4.5 mm, depending on the choice of technique. Two biomechanical methods yielded TRE of 3.1±1.8 mm and 3.3±1.9 mm, which outperformed optimal rigid registration of targets. These methods demonstrated good performance under varying degrees of surface data coverage and across all anatomical segments of the liver. Deep learning methods exhibited TRE ranging from 4.3±3.3 mm to 7.6±5.3 mm but are likely to improve with continued development. TRE was weakly correlated among methods, with greatest agreement and field consistency observed among the biomechanical approaches. Conclusions: The choice of registration algorithm significantly impacts registration accuracy and variability of deformation fields. Among current sparse data driven image-to-physical registration algorithms, biomechanical simulations that incorporate task-specific insight into boundary conditions seem to offer best performance.

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.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model.

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE: Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation.

Purpose: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery. Approach: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation. Results: Surface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was -1.05 mm and using NRM-VC was -1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively. Conclusion: The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model.

Emerging computational tools such as healthcare digital twin modeling are enabling the creation of patient-specific surgical planning, including microwave ablation to treat primary and secondary liver cancers. Healthcare digital twins (DTs) are anatomically one-to-one biophysical models constructed from structural, functional, and biomarker-based imaging data to simulate patient-specific therapies and guide clinical decision-making. In microwave ablation (MWA), tissue-specific factors including tissue perfusion, hepatic steatosis, and fibrosis affect therapeutic extent, but current thermal dosing guidelines do not account for these parameters. This study establishes an MR imaging framework to construct three-dimensional biophysical digital twins to predict ablation delivery in livers with 5 levels of fat content in the presence of a tumor. Four microwave antenna placement strategies were considered, and simulated microwave ablations were then performed using 915 MHz and 2450 MHz antennae in Tumor Naïve DTs (control), and Tumor Informed DTs at five grades of steatosis. Across the range of fatty liver steatosis grades, fat content was found to significantly increase ablation volumes by approximately 29-l42% in the Tumor Naïve and 55-60% in the Tumor Informed DTs in 915 MHz and 2450 MHz antenna simulations. The presence of tumor did not significantly affect ablation volumes within the same steatosis grade in 915 MHz simulations, but did significantly increase ablation volumes within mild-, moderate-, and high-fat steatosis grades in 2450 MHz simulations. An analysis of signed distance to agreement for placement strategies suggests that accounting for patient-specific tumor tissue properties significantly impacts ablation forecasting for the preoperative evaluation of ablation zone coverage.

Sliding Mode Control of Steerable Needles.

Steerable needles can potentially increase the accuracy of needle-based diagnosis and therapy delivery, provided they can be adequately controlled based on medical image information. We propose a novel sliding mode control law that can be used to deliver the tip of a flexible asymmetric-tipped needle to a desired point, or to track a desired trajectory within tissue. The proposed control strategy requires no a priori knowledge of model parameters, has bounded input speeds, and requires little computational resources. We show that if the standard nonholonomic model for tip-steered needles holds, then the control law will converge to desired targets in a reachable workspace, within a tolerance that can be defined by the control parameters. Experimental results validate the control law for target points and trajectory following in phantom tissue and ex vivo liver. Experiments with targets that move during insertion illustrate robustness to disturbances caused by tissue deformation.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling.

BACKGROUND: Simulating 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. METHODS: A 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. FINDINGS: The 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). INTERPRETATION: While 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.

Soft Tissue Monitoring of the Surgical Field: Detection and Tracking of Breast Surface Deformations.

OBJECTIVE: Deformable object tracking is common in the computer vision field, with applications typically focusing on nonrigid shape detection and usually not requiring specific three-dimensional point localization. In surgical guidance however, accurate navigation is intrinsically linked to precise correspondence of tissue structure. This work presents a contactless, automated fiducial acquisition method using stereo video of the operating field to provide reliable three-dimensional fiducial localization for an image guidance framework in breast conserving surgery. METHODS: On n = 8 breasts from healthy volunteers, the breast surface was measured throughout the full range of arm motion in a supine mock-surgical position. Using hand-drawn inked fiducials, adaptive thresholding, and KAZE feature matching, precise three-dimensional fiducial locations were detected and tracked through tool interference, partial and complete marker occlusions, significant displacements and nonrigid shape distortions. RESULTS: Compared to digitization with a conventional optically tracked stylus, fiducials were automatically localized with 1.6 ± 0.5 mm accuracy and the two measurement methods did not significantly differ. The algorithm provided an average false discovery rate <0.1% with all cases' rates below 0.2%. On average, 85.6 ± 5.9% of visible fiducials were automatically detected and tracked, and 99.1 ± 1.1% of frames provided only true positive fiducial measurements, which indicates the algorithm achieves a data stream that can be used for reliable on-line registration. CONCLUSIONS: Tracking is robust to occlusions, displacements, and most shape distortions. SIGNIFICANCE: This work-flow friendly data collection method provides highly accurate and precise three-dimensional surface data to drive an image guidance system for breast conserving surgery.

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

Purpose: 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. Approach: 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. Results: Average target registration errors were 2.56±2.45 mm for a biomechanical image registration algorithm and 4.15±3.63 mm 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. Conclusions: 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.

Design and evaluation of an optically-tracked single-CCD laser range scanner.

PURPOSE: Acquisition of laser range scans of an organ surface has the potential to efficiently provide measurements of geometric changes to soft tissue during a surgical procedure. A laser range scanner design is reported here which has been developed to drive intraoperative updates to conventional image-guided neurosurgery systems. METHODS: The scanner is optically-tracked in the operating room with a multiface passive target. The novel design incorporates both the capture of surface geometry (via laser illumination) and color information (via visible light collection) through a single-lens onto the same charge-coupled device (CCD). The accuracy of the geometric data was evaluated by scanning a high-precision phantom and comparing relative distances between landmarks in the scans with the corresponding ground truth (known) distances. The range-of-motion of the scanner with respect to the optical camera was determined by placing the scanner in common operating room configurations while sampling the visibility of the reflective spheres. The tracking accuracy was then analyzed by fixing the scanner and phantom in place, perturbing the optical camera around the scene, and observing variability in scan locations with respect to a tracked pen probe ground truth as the camera tracked the same scene from different positions. RESULTS: The geometric accuracy test produced a mean error and standard deviation of 0.25 ± 0.40 mm with an RMS error of 0.47 mm. The tracking tests showed that the scanner could be tracked at virtually all desired orientations required in the OR set up, with an overall tracking error and standard deviation of 2.2 ± 1.0 mm with an RMS error of 2.4 mm. There was no discernible difference between any of the three faces on the lasers range scanner (LRS) with regard to tracking accuracy. CONCLUSIONS: A single-lens laser range scanner design was successfully developed and implemented with sufficient scanning and tracking accuracy for image-guided surgery.

Tumor deformation correction for an image guidance system in breast conserving surgery.

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

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.

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions. SIGNIFICANCE: 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.

Purpose: 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. Approach: In this study, three registration methods were investigated on healthy volunteers' breasts ( n = 11 ) 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. Results: 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 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. Conclusions: 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.

A Novel Clinically Immersive Pre-doctoral Training Program for Engineering in Surgery and Intervention: Initial Realization and Preliminary Results.

A novel pre-doctoral program is presented that combines (1) immersive observation in the surgical/interventional theatre and (2) thought-provoking exposition activities focused on answering clinically provocative questions. While the long-term goal is to train engineers to conduct clinical translational research in human systems, in this paper, perceived trainee improvements are assessed in: (1) their ability to pose important questions in surgery and intervention, (2) their knowledge of surgical technologies, and (3) their understanding of procedural medicine. The program combines constructivist and constructionist learning approaches through a dual-course suite consisting of: (1) a scaffold lecture design with ten physicians presenting their procedural specialties interleaved with lectures relating engineering principles, and (2) a second course with clinically mentored immersion experiences in the operating room/interventional suite, clinical conferences, and patient rounds. Details of the complementing technical core and learning environment are also provided. Preliminary data reports on the quantitative experiential clinical involvement and on a self-reported survey over 5 cohorts of trainees (n = 18). With respect to immersion, the average surgeries/interventions observed, number of different types, and clinical contact time per student was on average 15.6 ± 7.9 surgeries/interventions, 8.2 ± 3.6 types, and 48.2 ± 14.7 contact hours, respectively. With respect to trainee understanding of procedural medicine, surgical technologies, and value of clinical observation, an average perceived improvement of 41%, 38%, and 41% over the course series was detected, respectively (p < 0.001). Equally impressive, when rating ability to pose important questions affecting human health, an average perceived improvement of 34% was detected (p < 0.001). The preliminary realization of a novel pre-doctoral clinically immersive training program for engineering trainees is described and demonstrates extensive levels of clinical contact and strong evidence that the provided immersion experiences result in significant improvements in understanding of procedural medicine.

Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints.

Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated ( r = 0.78 ), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods.

Impact of deformation on a supine-positioned image-guided breast surgery approach.

PURPOSE: To reduce reoperation rates for image-guided breast-conserving surgery, the enhanced sensitivity of magnetic resonance (MR) supine imaging may be leveraged. However, accurate tissue correspondence between images and their physical counterpart in the surgical presentation is challenging due to breast deformations (e.g., from patient/arm position changes, and operating room table rotation differences). In this study, standard rigid registration methods are employed and tissue deformation is characterized. METHODS: On n = 10 healthy breasts, surface displacements were measured by comparing intraoperative fiducial locations as the arm was moved from conventional MR scanning positions (arm-down and arm-up) to the laterally extended surgical configuration. Supine MR images in the arm-down and arm-up positions were registered to mock intraoperative presentations. RESULTS: Breast displacements from a supine MR imaging configuration to a mock surgical presentation were 28.9 ± 9.2 mm with shifts occurring primarily in the inferior/superior direction. With respect to supine MR to surgical alignment, the average fiducial, target, and maximum target registration errors were 9.0 ± 1.7 mm, 9.3 ± 1.7 mm, and 20.0 ± 7.6 mm, respectively. Even when maintaining similar arm positions in the MR image and mock surgery, the respective averages were 6.0 ± 1.0 mm, 6.5 ± 1.1 mm, and 12.5 ± 2.8 mm. CONCLUSION: From supine MR positioning to surgical presentation, the breast undergoes large displacements (9.9-70.1 mm). The data also suggest that significant nonrigid deformations (9.3 ± 1.7 mm with 20.0 mm average maximum) exist that need to be considered in image guidance and modeling applications.

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.