Application of soft tissue modelling to image-guided surgery

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

A fast technique for hyper-echoic region separation from brain ultrasound images using patch based thresholding and cubic B-spline based contour smoothing

Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50-100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.

Reaction Force Mapping by 3-Axis Tactile Sensing With Arbitrary Angles for Tissue Hard-Inclusion Localization

Although robot-assisted diagnosis and minimally invasive surgery (MIS) brings distinct benefits, deficient multi-dimensional force feedback remains a noteworthy limitation and challenge in MIS. Aiming for a comprehensive high-fidelity perception of tissue-instrument interactions, we present a Fiber Bragg Grating (FBG)-based 3-axis tactile sensing for surface reaction force mapping, identification and localization of tissue hard-inclusion. The tactile sensing probe consists of five optical fibers inscribed with FBGs and a force-sensitive 3D printed deformable body. All fibers are suspended inside the deformable body in a parallel manner, leading to the direct compression or tension of each FBG. Such configuration can effectively avoid the chirping failure of FBG compared with the pasting FBG-based sensors. A linearized difference model is proposed to calibrate the 3-axis force detection and enhance the resistance to nonlinear interferences. Hard-inclusion identification experiments with varied hard-inclusion sizes and depths have been implemented through discrete palpation and dragging palpation modes. Results indicate that the probe can effectively identify the presence and location of these small hard-inclusions from the force mapping. Furthermore, lengthy vessels embedded in the phantom can be accurately identified through dragging palpation with an arbitrary contact angle. Another novelty of the probe is the reconstruction of the surface profile of a non-planar tissue, which further allows hard-inclusion identification and 3D localization. Ex-vivo tissue palpation on a porcine kidney further validates the effectiveness and feasibility of the probe to map surface reaction forces and localize the hard-inclusions intraoperatively.

Comparison of strength and depth cut with scalpel on porcine gingival tissues

The composition of the gums confers some physical characteristics that make it resistant to mechanical stimulation.  The objective of the study was to compare the difference of the utilized forces when performing cuts in the anterior and posterior sections of porcine gingival tissue, measuring the depth of the tissue. A comparative descriptive study was performed with a non-probability convenience sampling, sectioned pig mandibles were used. The experimental trials were performed with an EZ-S SHIMADZU texture analyzer. All of the samples were submitted to a vertical shear force, thus identified the force level used to perform the incision and its depth. the necessary force to perform a cut in porcine gingival tissue was evaluated, comparing the posterior section (39.3571 Newton and 2.160 mm)  and  with the anterior ( 37.8424 newton and 1.747 mm), just as the depth of said cut, showing a statistical difference on the depth, (p=0.022 p< 0.59); regarding the force, no statistically significant difference was found. In the analyzed samples where the shear force in the posterior and anterior section were compared, no difference was found in both groups; as for the cut depth, this was greater in the posterior section than in the anterior.

Image Updating for Brain Shift Compensation During Resection

BACKGROUND

In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved.

OBJECTIVE

To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency.

METHODS

In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed.

RESULTS

Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min.

CONCLUSION

We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

Deformable Registration for Dose Accumulation

As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.

Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study

BACKGROUND

Augmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible.

METHODS

In this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer.

RESULTS

In the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer.

CONCLUSIONS

Mobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

Intraoperative image updating for brain shift following dural opening

OBJECTIVE Preoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process. METHODS In 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes. RESULTS The uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7-8 minutes. CONCLUSIONS This study compensated for brain deformation caused by intraoperative dural opening using computational model-based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7-8 minutes) and the process caused minimal interruption of surgical workflow.

Automatic deformable surface registration for medical applications by radial basis function-based robust point-matching

Deformable surface mesh registration is a useful technique for various medical applications, such as intra-operative treatment guidance and intra- or inter-patient study. In this paper, we propose an automatic deformable mesh registration technique. The proposed method iteratively deforms a source mesh to a target mesh without manual feature extraction. Each iteration of the registration consists of two steps, automatic correspondence finding using robust point-matching (RPM) and local deformation using a radial basis function (RBF). The proposed RBF-based RPM algorithm solves the interlocking problems of correspondence and deformation using a deterministic annealing framework with fuzzy correspondence and RBF interpolation. Simulation tests showed promising results, with the average deviations decreasing by factors of 21.2 and 11.9, respectively. In the human model test, the average deviation decreased from 1.72±1.88mm to 0.57±0.66mm. We demonstrate the effectiveness of the proposed method by presenting some medical applications.

The role of blood vessels in high-resolution volume conductor head modeling of EEG

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

An Anthropometric-Based Subject-Specific Finite Element Model of the Human Breast for Predicting Large Deformations

The large deformation of the human breast threatens proper nodules tracking when the subject mammograms are used as pre-planning data for biopsy. However, techniques capable of accurately supporting the surgeons during biopsy are missing. Finite element (FE) models are at the basis of currently investigated methodologies to track nodules displacement. Nonetheless, the impact of breast material modeling on the mechanical response of its tissues (e.g., tumors) is not clear. This study proposes a subject-specific FE model of the breast, obtained by anthropometric measurements, to predict breast large deformation. A healthy breast subject-specific FE parametric model was developed and validated by Cranio-caudal (CC) and Medio-Lateral Oblique (MLO) mammograms. The model was successively modified, including nodules, and utilized to investigate the effect of nodules size, typology, and material modeling on nodules shift under the effect of CC, MLO, and gravity loads. Results show that a Mooney–Rivlin material model can estimate healthy breast large deformation. For a pathological breast, under CC compression, the nodules displacement is very close to zero when a linear elastic material model is used. Finally, when nodules are modeled, including tumor material properties, under CC, or MLO or gravity loads, nodules shift shows ~15% average relative difference.

A review of biomechanically informed breast image registration

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters

Finite element analysis (FEA)-based biomechanical modeling can be used to predict lung respiratory motion. In this technique, elastic models and biomechanical parameters are two important factors that determine modeling accuracy. We systematically evaluated the effects of lung and lung tumor biomechanical modeling approaches and related parameters to improve the accuracy of motion simulation of lung tumor center of mass (TCM) displacements. Experiments were conducted with four-dimensional computed tomography (4D-CT). A Quasi-Newton FEA was performed to simulate lung and related tumor displacements between end-expiration (phase 50%) and other respiration phases (0%, 10%, 20%, 30%, and 40%). Both linear isotropic and non-linear hyperelastic materials, including the neo-Hookean compressible and uncoupled Mooney-Rivlin models, were used to create a finite element model (FEM) of lung and tumors. Lung surface displacement vector fields (SDVFs) were obtained by registering the 50% phase CT to other respiration phases, using the non-rigid demons registration algorithm. The obtained SDVFs were used as lung surface displacement boundary conditions in FEM. The sensitivity of TCM displacement to lung and tumor biomechanical parameters was assessed in eight patients for all three models. Patient-specific optimal parameters were estimated by minimizing the TCM motion simulation errors between phase 50% and phase 0%. The uncoupled Mooney-Rivlin material model showed the highest TCM motion simulation accuracy. The average TCM motion simulation absolute errors for the Mooney-Rivlin material model along left-right, anterior-posterior, and superior-inferior directions were 0.80 mm, 0.86 mm, and 1.51 mm, respectively. The proposed strategy provides a reliable method to estimate patient-specific biomechanical parameters in FEM for lung tumor motion simulation.

Radiation dose response simulation for biomechanical-based deformable image registration of head and neck cancer treatment

Biomechanical-based deformable image registration is conducted on the head and neck region. Patient specific 3D finite element models consisting of parotid glands (PG), submandibular glands (SG), tumor, vertebrae (VB), mandible, and external body are used to register pre-treatment MRI to post-treatment MR images to model the dose response using image data of five patients. The images are registered using combinations of vertebrae and mandible alignments, and surface projection of the external body as boundary conditions. In addition, the dose response is simulated by applying a new loading technique in the form of a dose-induced shrinkage using the dose-volume relationship. The dose-induced load is applied as dose-induced shrinkage of the tumor and four salivary glands. The Dice Similarity Coefficient (DSC) is calculated for the four salivary glands, and tumor to calculate the volume overlap of the structures after deformable registration. A substantial improvement in the registration is found by including the dose-induced shrinkage. The greatest registration improvement is found in the four glands where the average DSC increases from 0.53, 0.55, 0.32, and 0.37 to 0.68, 0.68, 0.51, and 0.49 in the left PG, right PG, left SG, and right SG, respectively by using bony alignment of vertebrae and mandible (M), body (B) surface projection and dose (D) (VB+M+B+D).

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

OBJECT

Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR.

METHODS

In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted.

RESULTS

To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4-5 minutes and minimal user interaction was required.

CONCLUSIONS

Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics

PURPOSE

NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.

METHODS

The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided.

RESULTS

Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.

CONCLUSION

The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery

PURPOSE

A surface registration method is presented to align intraoperative stereovision (iSV) with preoperative magnetic resonance (pMR) images, which utilizes both geometry and texture information to extract tissue displacements as part of the overall process of compensating for intraoperative brain deformation in order to maintain accurate neuronavigational image guidance during surgery.

METHODS

A sum-of-squared-difference rigid image registration was first executed to detect lateral shift of the cortical surface and was followed by a mutual-information-based block matching method to detect local nonrigid deformation caused by distention or collapse of the cortical surface. Ten (N = 10) surgical cases were evaluated in which an independent point measurement of a dominant cortical surface feature location was recorded with a tracked stylus in each case and compared to its surface-registered counterpart. The full three-dimensional (3D) displacement field was also extracted to drive a biomechanical brain deformation model, the results of which were reconciled with the reconstructed iSV surface as another form of evaluation.

RESULTS

Differences between the tracked stylus coordinates of cortical surface features and their surface-registered locations were 1.94 ± 0.59 mm on average across the ten cases. When the complete displacement map derived from surface registration was utilized, the resulting images generated from mechanical model updates were consistent in terms of both geometry (1-2 mm of model misfit) and texture, and were generated with less than 10 min of computational time. Analysis of the surface-registered 3D displacements indicate that the magnitude of motion ranged from 4.03 to 9.79 mm in the ten patient cases, and the amount of lateral shift was not related statistically to the direction of gravity (p = 0.73 ≫ 0.05) or the craniotomy size (p = 0.48 ≫ 0.05) at the beginning of surgery.

CONCLUSIONS

The iSV-pMR surface registration method utilizes texture and geometry information to extract both global lateral shift and local nonrigid movement of the cortical surface in 3D. The results suggest small differences exist in surface-registered locations when compared to positions measured independently with a coregistered stylus and when the full iSV surface was aligned with model-updated MR. The effectiveness and efficiency of the registration method is also minimally disruptive to surgical workflow.

Modeling lung deformation: a combined deformable image registration method with spatially varying Young's modulus estimates

PURPOSE

Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution.

METHODS

As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs.

RESULTS

In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively.

CONCLUSIONS

The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation.

An optimized system for interventional magnetic resonance imaging-guided stereotactic surgery: preliminary evaluation of targeting accuracy

BACKGROUND

Deep brain stimulation electrode placement with interventional magnetic resonance imaging (MRI) has previously been reported using a commercially available skull-mounted aiming device (Medtronic Nexframe MR) and native MRI scanner software. This first-generation method has technical limitations that are inherent to the hardware and software used. A novel system (SurgiVision ClearPoint) consisting of an aiming device (SMARTFrame) and software has been developed specifically for interventional MRI, including deep brain stimulation.

OBJECTIVE

To report a series of phantom and cadaver tests performed to determine the capability, preliminary accuracy, and workflow of the system.

METHODS

Eighteen experiments using a water phantom were used to determine the predictive accuracy of the software. Sixteen experiments using a gelatin-filled skull phantom were used to determine targeting accuracy of the aiming device. Six procedures in 3 cadaver heads were performed to compare the workflow and accuracy of ClearPoint with Nexframe MR.

RESULTS

Software prediction experiments showed an average error of 0.9 ± 0.5 mm in magnitude in pitch and roll (mean pitch error, -0.2 ± 0.7 mm; mean roll error, 0.2 ± 0.7 mm) and an average error of 0.7 ± 0.3 mm in X-Y translation with a slight anterior (0.5 ± 0.3 mm) and lateral (0.4 ± 0.3 mm) bias. Targeting accuracy experiments showed an average radial error of 0.5 ± 0.3 mm. Cadaver experiments showed a radial error of 0.2 ± 0.1 mm with the ClearPoint system (average procedure time, 88 ± 14 minutes) vs 0.6 ± 0.2 mm with the Nexframe MR (average procedure time, 92 ± 12 minutes).

CONCLUSION

This novel system provides the submillimetric accuracy required for stereotactic interventions, including deep brain stimulation placement. It also overcomes technical limitations inherent in the first-generation interventional MRI system.

Compensation for relative velocity between needle and soft tissue for friction modeling in needle insertion

Percutaneous therapies and in particular needle insertion treatments require an accurate model of needle-tissue interaction. Friction is a major interaction force component during needle insertion, and force-velocity mapping is an indicator of the interaction characteristics. However, soft tissue deformation is an inevitable and complex phenomenon that should be taken into account in order to correct the friction-velocity cycle. The main purpose of this study is to provide a means of compensating for the velocity of tissue with respect to a moving needle in the insertion direction. A high-gain observer is presented which can track this relative motion in a simple manner with no need for computer vision that may pose further challenges. To evaluate the performance of the velocity observer, experiments were carried out on gelatin and agar phantoms.

Modelling prostate motion for data fusion during image-guided interventions

There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.

Navigated laparoscopic ultrasound in abdominal soft tissue surgery: technological overview and perspectives

PURPOSE

Two-dimensinal laparoscopic ultrasound (LUS) is commonly used for many laparoscopic procedures, but 3D LUS and navigation technology are not conventional tools in the clinic. Navigated LUS can help the user understand and interpret the ultrasound images in relation to the laparoscopic view and preoperative images. When combined with information from MRI or CT, navigated LUS has the potential to provide information about anatomic shifts during the procedure. In this paper, we present an overview of the ongoing technological research and development related to LUS combined with navigation technology, The purpose of this overview is threefold: (1) an introduction for those new to the field of navigated LUS; (2) an overview for those working in the field and; and (3) as a reference for those searching for literature on technological developments related to navigation in ultrasound-guided laparoscopic surgery.

METHODS

Databases were searched to identify relevant publications from the last 10 years.

RESULTS

We were able to identify 18 key papers in the area of navigated LUS for the abdomen, originating from about 10-11 groups. We present the literature overview, including descriptions of our own experience in the field, and a discussion of the important clinical and technological aspects related to navigated LUS.

CONCLUSIONS

LUS integrated with miniaturized tracking technology is likely to play an important role in guiding future laparoscopic surgery.

A reduced order explicit dynamic finite element algorithm for surgical simulation

Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. Futhermore, we present a simple procedure for imposing inhomogeneous essential boundary conditions, thus overcoming one of the principal deficiencies of such approaches. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. It is shown that speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in interactive simulation and medical image-guidance problems, in which both speed and accuracy are vital.

Toward efficient biomechanical-based deformable image registration of lungs for image-guided radiotherapy

Both accuracy and efficiency are critical for the implementation of biomechanical model-based deformable registration in clinical practice. The focus of this investigation is to evaluate the potential of improving the efficiency of the deformable image registration of the human lungs without loss of accuracy. Three-dimensional finite element models have been developed using image data of 14 lung cancer patients. Each model consists of two lungs, tumor and external body. Sliding of the lungs inside the chest cavity is modeled using a frictionless surface-based contact model. The effect of the type of element, finite deformation and elasticity on the accuracy and computing time is investigated. Linear and quadrilateral tetrahedral elements are used with linear and nonlinear geometric analysis. Two types of material properties are applied namely: elastic and hyperelastic. The accuracy of each of the four models is examined using a number of anatomical landmarks representing the vessels bifurcation points distributed across the lungs. The registration error is not significantly affected by the element type or linearity of analysis, with an average vector error of around 2.8 mm. The displacement differences between linear and nonlinear analysis methods are calculated for all lungs nodes and a maximum value of 3.6 mm is found in one of the nodes near the entrance of the bronchial tree into the lungs. The 95 percentile of displacement difference ranges between 0.4 and 0.8 mm. However, the time required for the analysis is reduced from 95 min in the quadratic elements nonlinear geometry model to 3.4 min in the linear element linear geometry model. Therefore using linear tetrahedral elements with linear elastic materials and linear geometry is preferable for modeling the breathing motion of lungs for image-guided radiotherapy applications.

Real-time surgical simulation using reduced order finite element analysis

Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. Speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in medical image-guidance problems in which both speed and accuracy are vital.

A comparison of the accuracy of statistical models of prostate motion trained using data from biomechanical simulations

Statistical shape models (SSM) are widely used in medical image analysis to represent variability in organ shape. However, representing subject-specific soft-tissue motion using this technique is problematic for applications where imaging organ changes in an individual is not possible or impractical. One solution is to synthesise training data by using biomechanical modelling. However, for many clinical applications, generating a biomechanical model of the organ(s) of interest is a non-trivial task that requires a significant amount of user-interaction to segment an image and create a finite element mesh. In this study, we investigate the impact of reducing the effort required to generate SSMs and the accuracy with which such models can predict tissue displacements within the prostate gland due to transrectal ultrasound probe pressure. In this approach, the finite element mesh is based on a simplified geometric representation of the organs. For example, the pelvic bone is represented by planar surfaces, or the number of distinct tissue compartments is reduced. Such representations are much easier to generate from images than a geometrically accurate mesh. The difference in the median root-mean-square displacement error between different SSMs of prostate was <0.2 mm. We conclude that reducing the geometric complexity of the training model in this way made little difference to the absolute accuracy of SSMs to recover tissue displacements. The implication is that SSMs of organ motion based on simulated training data may be generated using simplified geometric representations, which are much more compatible with the time constraints of clinical workflows.

The Effect of Glisson’s Capsule on the Superficial Elasticity Measurements of the Liver

In the past decade, novel tools for surgical planning and disease diagnosis have been developed to detect the liver’s mechanical properties. Some tools utilize superficial indentation type measurements to determine the elasticity of the liver parenchyma and to assume material homogeneity. In fact, the liver is a soft tissue covered with a connective sheathing that is called Glisson’s capsule. This article quantifies the effect of this capsule on the measured or “effective” elastic modulus obtained by indentation with a spherical geometry. Two sets of parametric computational studies in which the Glisson capsule thickness and elasticity were varied, demonstrated the relationship between the measured elastic response and the underlying parenchymal elastic response. Previously reported in vivo indentation data on the human liver were utilized to determine the elasticity of its parenchyma. The results indicated a linear relationship between the effective (measured) elastic response and the underlying parenchyma for the Glisson capsule thicknesses considered. When previously published human liver indentation data were analyzed, the measured elastic modulus was nearly 6.9% greater than the underlying parenchyma elastic modulus. Although the analyzed data were obtained from a single liver and yet displayed a significant variation, the Glisson capsule elasticity induced a significant but systematic error as well. The Glisson capsule thickness error was negligible for capsule parameters associated with a normal liver. Based on this work, an emphasis on the Glisson capsule’s contribution to the mechanical response of the liver would enhance the clinical potential of indentation-based novel tools for liver care.

Image guidance and neuromonitoring in neurosurgery

INTRODUCTION

The localization of tumors and epileptogenic foci within the somatosensory or language cortex of the brain of a child poses unique neurosurgical challenges. In the past, lesions in these regions were not treated aggressively for fear of inducing neurological deficits. As a result, while function may have been preserved, the underlying disease may not have been optimally treated, and repeat neurosurgical procedures were frequently required. Today, with the advent of preoperative brain mapping, image guidance or neuronavigation, and intraoperative monitoring, peri-Rolandic and language cortex lesions can be approached directly and definitively with a high degree of confidence that neurosurgical function will be maintained.

METHODS AND RESULTS

The preoperative brain maps can now be achieved with magnetic resonance imaging (MRI), functional MRI, magnetoencephalography, and diffusion tensor imaging. Image guidance systems have improved significantly and include the use of the intraoperative MRI. Somatosensory, motor, and brainstem auditory-evoked potentials are used as standard neuromonitoring techniques in many centers around the world. Added to this now is the use of continuous train-of-five monitoring of the integrity of the corticospinal tract while operating in the peri-Rolandic region.

CONCLUSION

We are in an era where continued advancements can be expected in mapping additional pathways such as visual, memory, and hearing pathways. With these new advances, neurosurgeons can expect to significantly improve their surgical outcomes further.

Sliding characteristic and material compressibility of human lung: parametric study and verification

PURPOSE

To find and verify the optimum sliding characteristics and material compressibility that provide the minimum error in deformable image registration of the lungs.

METHODS

A deformable image registration study has been conducted on a total of 16 lung cancer patients. Patient specific three dimensional finite element models have been developed to model left and right lungs, chest (body), and tumor based on 4D CT images. Contact surfaces have been applied to lung-chest cavity interfaces. Experimental test data are used to model nonlinear material properties of lungs. A parametric study is carried out on seven patients, 20 conditions for each, to investigate the sliding behavior and the tissue compressibility of lungs. Three values of coefficient of friction of 0, 0.1, and 0.2 are investigated to model lubrication and sliding restriction on the lung-chest cavity interface. The effect of material compressibility of lungs is studied using Poisson's ratios of 0.35, 0.4, 0.45, and 0.499. The model accuracy is examined by calculating the difference between the image-based displacement of bronchial bifurcation points identified in the lung images and the calculated corresponding model-based displacement. Furthermore, additional bifurcation points around the tumor and its center of mass are used to examine the effect of the mentioned parameters on the tumor localization.

RESULTS

The frictionless contact model with 0.4 Poisson's ratio provides the smallest residual errors of 1.1 +/- 0.9, 1.5 +/- 1.3, and 2.1 +/- 1.6 mm in the LR, AP, and SI directions, respectively. Similarly, this optimum model provides the most accurate location of the tumor with residual errors of 1.0 +/- 0.6, 0.9 +/- 0.7, and 1.4 +/- 1.0 mm in all three directions. The accuracy of this model is verified on an additional nine patients with average errors of 0.8 +/- 0.7, 1.3 +/- 1.1, and 1.7 +/- 1.6 mm in the LR, AP, and SI directions, respectively.

CONCLUSIONS

The optimum biomechanical model with the smallest registration error is when frictionless contact model and 0.4 Poisson's ratio are applied. The overall accuracies of all bifurcation points in all 16 patients including tumor points are 1.0 +/- 0.7, 1.2 +/- 1.0, and 1.7 +/- 1.4 mm in the LR, AP, and SI directions, respectively.

A reduced order finite element algorithm for surgical simulation

We present a reduced order finite element (FE) algorithm suitable for real-time nonlinear simulation of soft tissues. A dynamic FE formulation with explicit time integration is employed. We demonstrate significant computation acceleration by performing the time integration in a low-dimensional generalised basis, generated from a set of a priori training simulations. The key mechanism for the acceleration is the large increase in integration time step afforded by this means. Futhermore, we present a simple procedure for imposing inhomogeneous essential boundary conditions, thus overcoming one of the principal deficiencies of such approaches. The algorithm is described and demonstrated using an example neurosurgical simulation. The computation acceleration and errors introduced are examined.

A strain-hardening bi-power law for the nonlinear behaviour of biological soft tissues

Biological soft tissues exhibit a strongly nonlinear viscoelastic behaviour. Among parenchymous tissues, kidney and liver remain less studied than brain, and a first goal of this study is to report additional material properties of kidney and liver tissues in oscillatory shear and constant shear rate tests. Results show that the liver tissue is more compliant but more strain hardening than kidney. A wealth of multi-parameter mathematical models has been proposed for describing the mechanical behaviour of soft tissues. A second purpose of this work is to develop a new constitutive law capable of predicting our experimental data in the both linear and nonlinear viscoelastic regime with as few parameters as possible. We propose a nonlinear strain-hardening fractional derivative model in which six parameters allow fitting the viscoelastic behaviour of kidney and liver tissues for strains ranging from 0.01 to 1 and strain rates from 0.0151 s(-1) to 0.7s(-1).