A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery

Reconstruction of 3D lung models from 2D planning data sets for Hodgkin's lymphoma patients using combined deformable image registration and navigator channels

PURPOSE

Late complications (cardiac toxicities, secondary lung, and breast cancer) remain a significant concern in the radiation treatment of Hodgkin's lymphoma (HL). To address this issue, predictive dose-risk models could potentially be used to estimate radiotherapy-related late toxicities. This study investigates the use of deformable image registration (DIR) and navigator channels (NCs) to reconstruct 3D lung models from 2D radiographic planning images, in order to retrospectively calculate the treatment dose exposure to HL patients treated with 2D planning, which are now experiencing late effects.

METHODS

Three-dimensional planning CT images of 52 current HL patients were acquired. 12 image sets were used to construct a male and a female population lung model. 23 "Reference" images were used to generate lung deformation adaptation templates, constructed by deforming the population model into each patient-specific lung geometry using a biomechanical-based DIR algorithm, MORFEUS. 17 "Test" patients were used to test the accuracy of the reconstruction technique by adapting existing templates using 2D digitally reconstructed radiographs. The adaptation process included three steps. First, a Reference patient was matched to a Test patient by thorax measurements. Second, four NCs (small regions of interest) were placed on the lung boundary to calculate 1D differences in lung edges. Third, the Reference lung model was adapted to the Test patient's lung using the 1D edge differences. The Reference-adapted Test model was then compared to the 3D lung contours of the actual Test patient by computing their percentage volume overlap (POL) and Dice coefficient.

RESULTS

The average percentage overlapping volumes and Dice coefficient expressed as a percentage between the adapted and actual Test models were found to be 89.2 +/- 3.9% (Right lung = 88.8%; Left lung = 89.6%) and 89.3 +/- 2.7% (Right = 88.5%; Left = 90.2%), respectively. Paired T-tests demonstrated that the volumetric reconstruction method made a statistically significant improvement to the population lung model shape (p < 0.05). The error in the results were also comparable to the volume overlap difference observed between inhale and exhale lung volumes during free-breathing respiratory motion (POL: p = 0.43; Dice: p = 0.20), which implies that the accuracies of the reconstruction method are within breathing constraints and would not be the confining factor in estimating normal tissue dose exposure.

CONCLUSIONS

The result findings show that the DIR-NC technique can achieve a high degree of reconstruction accuracy, and could be useful in approximating 3D dosimetric representations of historical 2D treatment. In turn, this could provide a better understanding of the biophysical relationship between dose-volume exposure and late term radiotherapy effects.

Magnetic resonance poroelastography: an algorithm for estimating the mechanical properties of fluid-saturated soft tissues

Magnetic resonance poroelastography (MRPE) is introduced as an alternative to single-phase model-based elastographic reconstruction methods. A 3-D finite element poroelastic inversion algorithm was developed to recover the mechanical properties of fluid-saturated tissues. The performance of this algorithm was assessed through a variety of numerical experiments, using synthetic data to probe its stability and sensitivity to the relevant model parameters. Preliminary results suggest the algorithm is robust in the presence of noise and capable of producing accurate assessments of the underlying mechanical properties in simulated phantoms. Furthermore, a 3-D time-harmonic motion field was recorded for a poroelastic phantom containing a single cylindrical inclusion and used to assess the feasibility of MRPE image reconstruction from experimental data. The elastograms obtained from the proposed poroelastic algorithm demonstrate significant improvement over linearly elastic MRE images generated using the same data. In addition, MRPE offers the opportunity to estimate the time-harmonic pressure field resulting from tissue excitation, highlighting the potential for its application in the diagnosis and monitoring of disease processes associated with changes in interstitial pressure.

Constitutive modeling of liver tissue: experiment and theory

Realistic surgical simulation requires incorporation of the mechanical properties of soft tissue in mathematical models. In actual deformation of soft-tissue during surgical intervention, the tissue is subject to tension, compression, and shear. Therefore, characterization and modeling of soft-tissue in all these three deformation modes are necessary. In this paper we applied two types of pure shear test, un-confined compression and uniaxial tension test to characterize porcine liver tissue. Digital image correlation technique was used to accurately measure the tissue deformation field. Due to gravity and its effect on the soft tissue, a maximum stretching band was observed from the relative strain field on sample undergoing tension and pure shear test. The zero strain state was identified according to the position of this maximum stretching band. Two new constitutive models based on combined exponential/logarithmic and Ogden strain energy were proposed. The models are capable to represent the observed non-linear stress-strain relation of liver tissue for full range of tension and compression and also the general response of pure shear.

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Biomechanical models that simulate brain deformation are gaining attention as alternatives for brain shift compensation. One approach, known as the "forced-displacement method", constrains the model to exactly match the measured data through boundary condition (BC) assignment. Although it improves model estimates and is computationally attractive, the method generates fictitious forces and may be ill-advised due to measurement uncertainty. Previously, we have shown that by assimilating intraoperatively acquired brain displacements in an inversion scheme, the Representer algorithm (REP) is able to maintain stress-free BCs and improve model estimates by 33% over those without data guidance in a controlled environment. However, REP is computationally efficient only when a few data points are used for model guidance because its costs scale linearly in the number of data points assimilated, thereby limiting its utility (and accuracy) in clinical settings. In this paper, we present a steepest gradient descent algorithm (SGD) whose computational complexity scales nearly invariantly with the number of measurements assimilated by iteratively adjusting the forcing conditions to minimize the difference between measured and model-estimated displacements (model-data misfit). Solutions of full linear systems of equations are achieved with a parallelized direct solver on a shared-memory, eight-processor Linux cluster. We summarize the error contributions from the entire process of model-updated image registration compensation and we show that SGD is able to attain model estimates comparable to or better than those obtained with REP, capturing about 74-82% of tumor displacement, but with a computational effort that is significantly less (a factor of 4-fold or more reduction relative to REP) and nearly invariant to the amount of sparse data involved when the number of points assimilated is large. Based on five patient cases, an average computational cost of approximately 2 min for estimating whole-brain deformation has been achieved with SGD using 100 sparse data points, suggesting the new algorithm is sufficiently fast with adequate accuracy for routine use in the operating room (OR).

Brain-skull contact boundary conditions in an inverse computational deformation model

Biomechanical models simulating brain motion under loading and boundary conditions in the operating room (OR) are gaining attention as alternatives for brain shift compensation during open cranial neurosurgeries. Although the significance of brain-skull boundary conditions (BCs) in these models has been explored in dynamic simulations, it has not been fully investigated in models representing the quasi-static brain motion that prevails during neurosurgery. In this study, we extend the application of a brain-skull contact BC by incorporating it into an inversion estimation scheme for the deformation field using the steepest gradient descent (SGD) framework. The technique allows parenchymal surface motion normal to the skull while maintaining stress-free BCs at the craniotomy and minimizing the effect of measurement noise. Application of the algorithm in five clinical cases using sparse data generated at the tumor boundary confirms the significance of brain-skull BCs in the model response. Specifically, the results demonstrate that the contact BC enhances model flexibility and achieves improved or comparable performance at the tumor boundary (recovering about 85% of the deformation) relative to that obtained when normal motion of the parenchymal surface is not allowed. It also significantly improves model estimation accuracy at the craniotomy (1.6mm on average), especially when the normal motion is large. The importance of the method is that model performance significantly improves when brain-skull contact influences the deformation field but does not degrade when the contact is less critical and simpler BCs would suffice. The computational cost of the technique is currently 3.9 min on average, but may be further reduced by applying an iterative solver to the linear systems of equations involved and/or by local refinement of the mesh in regions of interest.

Model-based estimation of ventricular deformation in the cat brain

The estimation of ventricular deformation has important clinical implications related to neuro-structural disorders such as hydrocephalus. In this paper, a poroelastic model was used to represent deformation effects resulting from the ventricular system and was studied in 5 feline experiments. Chronic or acute hydrocephalus was induced by injection of kaolin into the cisterna magna or saline into the ventricles; a catheter was then inserted in the lateral ventricle to drain the fluid out of the brain. The measured displacement data which was extracted from pre-drainage and post-drainage MR images were incorporated into the model through the Adjoint Equations Method. The results indicate that the computational model of the brain and ventricular system captured 33% of the ventricle deformation on average and the model-predicted intraventricular pressure was accurate to 90% of the recorded value during the chronic hydrocephalus experiments.

Modeling of soft poroelastic tissue in time-harmonic MR elastography

Elastography is an emerging imaging technique that focuses on assessing the resistance to deformation of soft biological tissues in vivo. Magnetic resonance elastography (MRE) uses measured displacement fields resulting from low-amplitude, low-frequency (10 Hz-1 kHz) time-harmonic vibration to recover images of the elastic property distribution of tissues including breast, liver, muscle, prostate, and brain. While many soft tissues display complex time-dependent behavior not described by linear elasticity, the models most commonly employed in MRE parameter reconstructions are based on elastic assumptions. Further, elasticity models fail to include the interstitial fluid phase present in vivo. Alternative continuum models, such as consolidation theory, are able to represent tissue and other materials comprising two distinct phases, generally consisting of a porous elastic solid and penetrating fluid. MRE reconstructions of simulated elastic and poroelastic phantoms were performed to investigate the limitations of current-elasticity-based methods in producing accurate elastic parameter estimates in poroelastic media. The results indicate that linearly elastic reconstructions of fluid-saturated porous media at amplitudes and frequencies relevant to steady-state MRE can yield misleading effective property distributions resulting from the complex interaction between their solid and fluid phases.

Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy.

On the unimportance of constitutive models in computing brain deformation for image-guided surgery

Imaging modalities that can be used intra-operatively do not provide sufficient details to confidently locate the abnormalities and critical healthy areas that have been identified from high-resolution pre-operative scans. However, as we have shown in our previous work, high quality pre-operative images can be warped to the intra-operative position of the brain. This can be achieved by computing deformations within the brain using a biomechanical model. In this paper, using a previously developed patient-specific model of brain undergoing craniotomy-induced shift, we conduct a parametric analysis to investigate in detail the influences of constitutive models of the brain tissue. We conclude that the choice of the brain tissue constitutive model, when used with an appropriate finite deformation solution, does not affect the accuracy of computed displacements, and therefore a simple linear elastic model for the brain tissue is sufficient.

Determination of the elasticity parameters of brain tissue with combined simulation and registration

Reliable elasticity parameters describing the behavior of a given material are an important issue in the context of physically-based simulation. In this paper we introduce a method for the determination of the mechanical properties of brain tissue. Elasticity parameters Young's modulus E and Poisson's ratio nu are estimated in an iterative framework coupling a finite element simulation with image registration. Within this framework, the outcome of the simulation is parameterized with both elasticity moduli that are automatically varied until optimal image correspondence between the simulated and the intraoperative data is achieved. We calculated optimal mechanical properties of brain tissue in six cases. The statistical analysis of the obtained values showed a good correlation of the results, thus proving the value of the method. An approach combining simulation and registration for the determination of the mechanical brain tissue properties is presented. This contributes to performing reliable physically-based simulation of soft tissue movement.

An atlas-based method to compensate for brain shift: preliminary results

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7+/-0.3 mm, and 1.0+/-0.4 mm, respectively, for deformations on the order of 1cm.

Multimodal image re-registration via mutual information to account for initial tissue motion during image-guided neurosurgery

Multimodal imaging modalities, such as high-resolution pre-operative MR (pMR) and intra-operative US (iUS), have been used widely to provide complementary information for guiding neurosurgery. Proper registration between the images is usually established fiducials placed on the patient skull, which can be identified both in MR and a tracking system that also records location and orientation of each iUS scan. Although such systems can achieve high registration accuracy, we have observed brain tissue motion with respect to the skull after the craniotomy and before opening of the dura. We developed a rigid registration strategy to re-align the brain in iUS and pMR to account for such motion. The mutual information between the two modalities is maximized through a genetic algorithm. Two patient cases are presented to show the improved registration accuracy of soft tissues.

A comprehensive system for intraoperative 3D brain deformation recovery

During neurosurgery, brain deformation renders preoperative images unreliable for localizing pathologic structures. In order to visualize the current brain anatomy, it is necessary to nonrigidly warp these preoperative images to reflect the intraoperative brain. This can be accomplished using a biomechanical model driven by sparse intraoperative information. In this paper, a linear elastic model of the brain is developed which can infer volumetric brain deformation given the cortical surface displacement. This model was tested on both a realistic brain phantom and in vivo, proving its ability to account for large brain deformations. Also, an efficient semiautomatic strategy for preoperative cortical feature detection is outlined, since accurate segmentation of cortical features can aid intraoperative cortical surface tracking.

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

OBJECTIVE

The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.

MATERIALS AND METHODS

Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration.

RESULTS

Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).

CONCLUSIONS

We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

Data-guided brain deformation modeling: evaluation of a 3-D adjoint inversion method in porcine studies

Biomechanical models of brain deformation are useful tools for estimating parenchymal shift that results during open cranial procedures. Intraoperative data is likely to improve model estimates, but incorporation of such data into the model is not trivial. This study tests the adjoint equations method (AEM) for data assimilation as a viable approach for integrating displacement data into a brain deformation model. AEM was applied to two porcine experiments. AEM-based estimates were compared both to measured displacement data [from computed tomography (CT) scans] and to model solutions obtained without the guidance of sparse data, which we term the best prior estimate (BPE). Additionally, the sensitivity of the AEM solution to inverse parameter selection was investigated. The results suggest that it is most important to estimate the size of the variance in the measurement error correctly, make the correlation length long and estimate displacement (over stress) boundary conditions. Application of AEM shows an average 33% improvement over BPE. This paper represents the first evidence of successful use of the AEM technique in three dimensions with experimental data validation. The guidelines established for selection of model parameters are starting points for further optimization of the method under clinical conditions.

Image-guided ultrasonography for recurrent cystic gliomas

BACKGROUND

Long-term survival of patients with recurrent gliomas depends on the extent of resection. Thus, the desirability of an intra-operative imaging modality that can augment the resection extension without affecting vital surrounding structures is more than obvious. It was the aim of the present study to evaluate a possible benefit of image-guided intra-operative ultrasonography for the surgery of recurrent gliomas.

METHOD

The authors performed ultrasonography-assisted image-guided resection of recurrent gliomas in 16 patients. An ultrasound device (IGSonic) was integrated into the VectorVision2 navigation system (BrainLAB, Heimstetten, Germany). The IGSonic Probe 10V5 was connected to the VectorVision Navigation station via an IGSonic Device Box. Following patient registration, MRI based neuronavigation was used to determine the skin incision and the bone flap. Before opening the dura, the underlying structures were explored by ultrasound combined with the corresponding MR images. The navigated ultrasound displayed the sonographic image of the intracranial anatomy on the navigation screen in a composed overlay fashion.

FINDINGS

The integration of intra-operative ultrasound into neuronavigation system offered quick and helpful intra-operative images in all 16 procedures. Due to the specific ultrasonic characteristics of the solid and the cystic parts, our technique created highly useful images in 10 patients with cystic recurrences. In these, user friendly images were obtained that were easy to understand even for neurosurgeons without major experience in intra-operative ultrasound.

CONCLUSIONS

Neurosonography is a time- and cost-effective technology offering intra-operative imaging. The improved orientation and visualization of tumour remnants, adjacent ventricles, and the enhanced intra- and peri-tumoural vasculature is one of the main advantages of ultrasonography-assisted image-guided surgery, which is most obvious during surgery for cystic gliomas.

Application of soft tissue modelling to image-guided surgery

The deformation of soft tissue compromises the accuracy of image-guided surgery based on preoperative images, and restricts its applicability to surgery on or near bony structures. One way to overcome these limitations is to combine biomechanical models with sparse intraoperative data, in order to realistically warp the preoperative image to match the surgical situation. We detail the process of biomechanical modelling in the context of image-guided surgery. We focus in particular on the finite element method, which is shown to be a promising approach, and review the constitutive relationships which have been suggested for representing tissue during surgery. Appropriate intraoperative measurements are required to constrain the deformation, and we discuss the potential of the modalities which have been applied to this task. This technology is on the verge of transition into clinical practice, where it promises to increase the guidance accuracy and facilitate less invasive interventions. We describe here how soft tissue modelling techniques have been applied to image-guided surgery applications.

Biomechanical analysis of silicon microelectrode-induced strain in the brain

The ability to successfully interface the brain to external electrical systems is important both for fundamental understanding of our nervous system and for the development of neuroprosthetics. Silicon microelectrode arrays offer great promise in realizing this potential. However, when they are implanted into the brain, recording sensitivity is lost due to inflammation and astroglial scarring around the electrode. The inflammation and astroglial scar are thought to result from acute injury during electrode insertion as well as chronic injury caused by micromotion around the implanted electrode. To evaluate the validity of this assumption, the finite element method (FEM) was employed to analyze the strain fields around a single Michigan Si microelectrode due to simulated micromotion. Micromotion was mimicked by applying a force to the electrode, fixing the boundaries of the brain region and applying appropriate symmetry conditions to nodes lying on symmetry planes. Characteristics of the deformation fields around the electrode including maximum electrode displacement, strain fields and relative displacement between the electrode and the adjacent tissue were examined for varying degrees of physical coupling between the brain and the electrode. Our analysis demonstrates that when physical coupling between the electrode and the brain increases, the micromotion-induced strain of tissue around the electrode decreases as does the relative slip between the electrode and the brain. These results support the use of neuro-integrative coatings on electrode arrays as a means to reduce the micromotion-induced injury response.

Stereopsis-guided brain shift compensation

Brain deformation models have proven to be a powerful tool in compensating for soft tissue deformation during image-guided neurosurgery. The accuracy of these models can be improved by incorporating intraoperative measurements of brain motion. We have designed and implemented a passive intraoperative stereo vision system capable of estimating the three-dimensional shape of the surgical scene in near real-time. This intraoperative shape is compared with the cortical surface in the co-registered preoperative magnetic resonance (MR) volume for the estimation of the cortical motion resulting from the open cranial surgery. The estimated cortical motion is then used to guide a full brain model, which updates a preoperative MR volume. We have found that the stereo vision system is accurate to within approximately 1 mm. Based on data from two representative clinical cases, we show that stereopsis guidance improves the accuracy of brain shift compensation both at and below the cortical surface.

Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data

A fully automatic, two-step, T1-weighted brain magnetic resonance imaging (MRI) segmentation method is presented. A preliminary mask of parenchyma is first estimated through adaptive image intensity analysis and mathematical morphological operations. It serves as the initial model and probability reference for a level-set algorithm in the second step, which finalizes the segmentation based on both image intensity and geometric information. The Dice coefficient and Euclidean distance between boundaries of automatic results and the corresponding references are reported for both phantom and clinical MR data. For the 28 patient scans acquired at our institution, the average Dice coefficient was 98.2% and the mean Euclidean surface distance measure was 0.074 mm. The entire segmentation for either a simulated or a clinical image volume finishes within 2 min on a modern PC system. The accuracy and speed of this technique allow us to automatically create patient-specific finite element models within the operating room on a timely basis for application in image-guided updating of preoperative scans.

Accuracy of finite element model-based multi-organ deformable image registration

As more pretreatment imaging becomes integrated into the treatment planning process and full three-dimensional image-guidance becomes part of the treatment delivery the need for a deformable image registration technique becomes more apparent. A novel finite element model-based multiorgan deformable image registration method, MORFEUS, has been developed. The basis of this method is twofold: first, individual organ deformation can be accurately modeled by deforming the surface of the organ at one instance into the surface of the organ at another instance and assigning the material properties that allow the internal structures to be accurately deformed into the secondary position and second, multi-organ deformable alignment can be achieved by explicitly defining the deformation of a subset of organs and assigning surface interfaces between organs. The feasibility and accuracy of the method was tested on MR thoracic and abdominal images of healthy volunteers at inhale and exhale. For the thoracic cases, the lungs and external surface were explicitly deformed and the breasts were implicitly deformed based on its relation to the lung and external surface. For the abdominal cases, the liver, spleen, and external surface were explicitly deformed and the stomach and kidneys were implicitly deformed. The average accuracy (average absolute error) of the lung and liver deformation, determined by tracking visible bifurcations, was 0.19 (s.d.: 0.09), 0.28 (s.d.: 0.12) and 0.17 (s.d.: 0.07) cm, in the LR, AP, and IS directions, respectively. The average accuracy of implicitly deformed organs was 0.11 (s.d.: 0.11), 0.13 (s.d.: 0.12), and 0.08 (s.d.: 0.09) cm, in the LR, AP, and IS directions, respectively. The average vector magnitude of the accuracy was 0.44 (s.d.: 0.20) cm for the lung and liver deformation and 0.24 (s.d.: 0.18) cm for the implicitly deformed organs. The two main processes, explicit deformation of the selected organs and finite element analysis calculations, require less than 120 and 495 s, respectively. This platform can facilitate the integration of deformable image registration into online image guidance procedures, dose calculations, and tissue response monitoring as well as performing multi-modality image registration for purposes of treatment planning.

Quasi-real-time neurosurgery support by MRI processing via grid computing

One problem of providing time-critical medical services over the grid is always its dependency on the Internet. It cannot be assumed that transfer ofa certain amount of data over the Internet is always achieved during a specified period. Such a requirement cannot be fulfilled by the infrastructure of the Web. There is always the risk of a network delay or even an overload. Because of this, another goal of this project is the evaluation of grid services versus the use of local services. A further point for future research related to the chain has to deal with the optimization approach for the linear registration step. Because the optimization uses the downhill simplex algorithm in a nine-dimensional search space, the number of iterations needed to find the optimum can vary dramatically. This makes linear registration the most unpredictable step of the chain in terms of execution time. It cannot be assured that the global optimum is found. Additional work has to be done in validating the registration accuracy, including the examination of the influence of intensity variations between intraoperative images as well as the influence of tumor resection and the presence of the opened skull versus the closed skull in the fluid-based registration.

Fast and adaptive finite element approach for modeling brain shift

OBJECTIVE

In this paper we introduce a finite element-based strategy for simulation of brain deformation occurring during neurosurgery. The phenomenon, known as brain shift, causes a decrease in the accuracy of neuronavigation systems that rely on preoperatively acquired data. This can be compensated for with a computational model of the brain deformation process. By applying model calculations to preoperative images, an update within the operating room can be performed.

METHODS

One of the crucial concerns in the context of developing a physical-based model is the choice of governing equations describing the physics of the phenomenon. In this work, deformation of brain tissue is expressed in terms of a 3D consolidation model for a linearly elastic and porous fluid. The next crucial issue is ensuring stable calculations within the chosen model. For this purpose, we developed a special technique for generating the underlying geometry for the simulation. With this technique an unstructured grid consisting of regular tetrahedra is created, whereupon time-dependent finite element simulation is performed in an adaptive manner.

RESULTS

We applied our algorithm to preoperative MR scans and investigated the value of the method. Due to the adaptivity of the method, only 5-10% of the computing time was needed as compared to traditional finite element approaches based on a uniformly subdivided grid. The results of the experiments were compared to the corresponding intraoperative MR scans. A close match between the computed deformation of the brain and the displacement resulting from the intraoperative data was observed.

CONCLUSION

A model-based approach for the simulation of brain shift is presented. In this computational model the brain tissue is described as an elastic and porous material using Biot consolidation theory. Validating experiments conducted with MR data provided promising results.

Brain Shift Estimation in Image-Guided Neurosurgery Using 3-D Ultrasound

Intraoperative brain deformation is one of the most important causes affecting the overall accuracy of image-guided neurosurgical procedures. One option for correcting for this deformation is to acquire three-dimensional (3-D) ultrasound data during the operation and use this data to update the information provided by the preoperatively acquired MR data. For 12 patients 3-D ultrasound images have been reconstructed from freehand sweeps acquired during neurosurgical procedures. Ultrasound data acquired prior to and after opening the dura, but prior to surgery, have been quantitatively compared to the preoperatively acquired MR data to estimate the rigid component of brain shift at the first stages of surgery. Prior to opening the dura the average brain shift measured was 3.0 mm parallel to the direction of gravity, with a maximum of 7.5 mm, and 3.9 mm perpendicular to the direction of gravity, with a maximum of 8.2 mm. After opening the dura the shift increased on average 0.2 mm parallel to the direction of gravity and 1.4 mm perpendicular to the direction of gravity. Brain shift can be detected by acquiring 3-D ultrasound data during image-guided neurosurgery. Therefore, it can be used as a basis for correcting image data and preoperative planning for intraoperative deformations.

Cortical Surface Tracking Using a Stereoscopic Operating Microscope

OBJECTIVE:

To measure and compensate for soft tissue deformation during image-guided neurosurgery, we have developed a novel approach to estimate the three-dimensional (3-D) topology of the cortical surface and track its motion over time.

METHODS:

We use stereopsis to estimate the 3-D cortical topology during neurosurgical procedures. To facilitate this process, two charge-coupled device cameras have been attached to the binocular optics of a stereoscopic operating microscope. Before surgery, this stereo imaging system is calibrated to obtain the extrinsic and intrinsic camera parameters. During surgery, the 3-D shape of the cortical surface is automatically estimated from a stereo pair of images and registered to the preoperative image volume to provide navigational guidance. This estimation requires robust matching of features between the images, which, when combined with the camera calibration, yields the desired 3-D coordinates. After the 3-D cortical surface has been estimated from stereo pairs, its motion is tracked by comparing the current surface with its previous locations.

RESULTS:

We are able to estimate the 3-D topology of the cortical surface with an average error of less than 1.2 mm. Executing on a 1.1-GHz Pentium machine, the 3-D estimation from a stereo pair of 1024 × 768 resolution images requires approximately 60 seconds of computation. By applying stereopsis over time, we are able to track the motion of the cortical surface, including the pulsatile movement of the cortical surface, gravitational sag, tissue bulge as a result of increased intracranial pressure, and the parenchymal shape changes associated with tissue resection. The results from 10 surgical patients are reported.

CONCLUSION:

We have demonstrated that a stereo vision system coupled to the operating microscope can be used to efficiently estimate the dynamic topology of the cortical surface during surgery. The 3-D surface can be coregistered to the preoperative image volume. This unique intraoperative imaging technique expands the capability of the current navigational system in the operating room and increases the accuracy of anatomic correspondence with preoperative images through compensation for brain deformation.

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.

Strategies for brain shift evaluation

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

Virtual Reality Neurosurgery: A Simulator Blueprint

OBJECTIVE

This article details preliminary studies undertaken to integrate the most relevant advancements across multiple disciplines in an effort to construct a highly realistic neurosurgical simulator based on a distributed computer architecture. Techniques based on modified computational modeling paradigms incorporating finite element analysis are presented, as are current and projected efforts directed toward the implementation of a novel bidirectional haptic device.

METHODS

Patient-specific data derived from noninvasive magnetic resonance imaging sequences are used to construct a computational model of the surgical region of interest. Magnetic resonance images of the brain may be coregistered with those obtained from magnetic resonance angiography, magnetic resonance venography, and diffusion tensor imaging to formulate models of varying anatomic complexity.

RESULTS

The majority of the computational burden is encountered in the presimulation reduction of the computational model and allows realization of the required threshold rates for the accurate and realistic representation of real-time visual animations.

CONCLUSION

Intracranial neurosurgical procedures offer an ideal testing site for the development of a totally immersive virtual reality surgical simulator when compared with the simulations required in other surgical subspecialties. The material properties of the brain as well as the typically small volumes of tissue exposed in the surgical field, coupled with techniques and strategies to minimize computational demands, provide unique opportunities for the development of such a simulator. Incorporation of real-time haptic and visual feedback is approached here and likely will be accomplished soon.

Breast deformation modelling for image reconstruction in near infrared optical tomography

Near infrared tomography (NIR) is a novel imaging technique that can be used to reconstruct tissue optical properties from measurements of light propagation through tissue. More specifically NIR measurements over a range of wavelengths can be used to obtain internal images of physiologic parameters and these images can be used to detect and characterize breast tumour. To obtain good NIR measurements, it is essential to have good contact between the optical fibres and the breast which in-turn results in the deformation of the breast due to the soft plasticity of the tissue. In this work, a tissue deformation model of the female breast is presented that will account for the altered shape of the breast during clinical NIR measurements. Using a deformed model of a breast, simulated NIR data were generated and used to reconstruct images of tissue absorption and reduced scatter using several assumptions about the imaging domain. Using either a circular or irregular 2D geometry for image reconstruction produces good localization of the absorbing anomaly, but it leads to degradation of the image quality. By modifying the assumptions about the imaging domain to a 3D conical model, with the correct diameter at the plane of NIR measurement, significantly improves the quality of reconstructed images and helps reduce image artefacts. Finally, assuming a non-deformed breast shape for image reconstruction is shown to lead to poor quality images since the geometry of the breast is greatly altered, whereas using the correct deformed geometry produces the best images.

A simulation model for analysing brain structure deformations

Recent developments of medical software applications--from the simulation to the planning of surgical operations--have revealed the need for modelling human tissues and organs, not only from a geometric point of view but also from a physical one, i.e. soft tissues, rigid body, viscoelasticity, etc. This has given rise to the term 'deformable objects', which refers to objects with a morphology, a physical and a mechanical behaviour of their own and that reflects their natural properties. In this paper, we propose a model, based upon physical laws, suitable for the realistic manipulation of geometric reconstructions of volumetric data taken from MR and CT scans. In particular, a physically based model of the brain is presented that is able to simulate the evolution of different nature pathological intra-cranial phenomena such as haemorrhages, neoplasm, haematoma, etc and to describe the consequences that are caused by their volume expansions and the influences they have on the anatomical and neuro-functional structures of the brain.

Reducing uncertainties in volumetric image based deformable organ registration

Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable-thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation.

Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.

Surgical navigation in the open MRI

The introduction of MRI into neurosurgery has opened multiple avenues, but also introduced new challenges. The open-configuration intraoperative MRI installed at the Brigham and Women's Hospital in 1996 has been used for more than 500 open craniotomies and beyond 100 biopsies. Furthermore the versatile applicability, employing the same principles, is evident by its frequent use in other areas of the body. However, while intraoperative scanning in the SignaSP yielded unprecedented imaging during neurosurgical procedures their usage for navigation proved bulky and unhandy. To be fully integrated into the procedure, acquisition and display of intraoperative data have to be dynamic and primarily driven by the surgeon performing the procedure. To use the benefits of computer-assisted navigation systems together with immediate availability of intraoperative imaging we developed a software package. This "3D Slicer" has been used routinely for biopsies and open craniotomies. The system is stable and reliable. Pre- and intraoperative data can be visualized to plan and perform surgery, as well as to accommodate for intraoperative deformations, "brain shift", by providing online data acquisition.

Virtual reality surgery: neurosurgery and the contemporary landscape

OBJECTIVE

Virtual reality-simulated environments have been used for the training of personnel, most notably for military applications, for more than 35 years. The advantages conferred by being able to train novice personnel in a low- to no-risk simulated environment have long been appreciated by the medical community. The recent availability of affordable gigahertz-range microprocessors (once the exclusive domain of the Cray supercomputer) has made photorealistic graphical rendering and manipulation of virtual surgical substrates a reality. Concomitant advances in artificial intelligence systems and the portability of patient-specific magnetic resonance imaging, computed tomographic scanning, and angiographic image data presage the emergence of the surgical simulator as a modern surgical training adjunct. An overview of the status of surgical simulation with regard to its adaptability to current surgical training regimens is presented.

METHODS

Extensive MEDLINE, Internet, and other database searches spanning the years 1960 to 2002 were conducted in an effort to delineate the status of simulated surgical environments.

RESULTS

As would be expected, most articles addressing surgical simulation as their primary focus have been published in the past decade. A review of this literature demonstrates the broadest application in the field of endoscopic (and laparoscopic) procedures, most likely as a result of the reduced engineering burden with respect to incorporation of a haptic interface.

CONCLUSION

The realization of ergonomically acceptable haptic interfaces remains elusive. Improvements in graphical rendering and the incorporation of artificial intelligence functions signal the certain emergence of surgical simulators as a viable supplement to the Halstedian method of surgical training.

A new approach to elastography using mutual information and finite elements

Historically, increased mechanical stiffness during tissue palpation exams has been associated with assessing organ health as well as with detecting the growth of a potentially life-threatening cell mass. As such, techniques to image elasticity parameters (i.e., elastography) have recently become of great interest to scientists. In this work, a new method of elastography will be introduced within the context of mammographic imaging. The elastography method proposed represents a non-rigid iterative image registration algorithm that varies material properties within a finite element model to improve registration. More specifically, regional measures of image similarity are used within an objective function minimization framework to reconstruct elasticity images of tissue stiffness. Numerical simulations illustrate: (1) the encoding of stiffness information within the context of a regional image similarity criterion, (2) the methodology for an iterative elastographic imaging framework and (3) elasticity reconstruction simulations. The real strength in this approach is that images from any modality (e.g., magnetic resonance, computed tomography, ultrasound. etc) that have sufficient anatomically-based intensity heterogeneity and remain consistent from a pre- to a post-deformed state could be used in this paradigm.

Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

Compensating for intraoperative brain shift using computational models has been used with promising results. Since computational time is an important factor during neurosurgery, a prior knowledge of a patient's orientation and changes in tissue buoyancy force would be valuable information to aid in predicting shift due to gravitational forces. Since the latter is difficult to quantify intraoperatively, a statistical model for predicting intraoperative brain deformations due to gravity is reported. This statistical model builds on a computational model developed earlier. For a given set of patient's orientation and amount of CSF drainage, the intraoperative brain shift is calculated using the computational model. These displacements are then validated against measured displacements to predict the intraoperative brain shift. Though initial results are promising, further study is needed before the statistical model can be used for model-updated image-guided surgery.

Measurement and analysis of brain deformation during neurosurgery

Recent studies have shown that the surface of the brain is deformed by up to 20 mm after the skull is opened during neurosurgery, which could lead to substantial error in commercial image-guided surgery systems. We quantitatively analyze the intraoperative brain deformation of 24 subjects to investigate whether simple rules can describe or predict the deformation. Interventional magnetic resonance images acquired at the start and end of the procedure are registered nonrigidly to obtain deformation values throughout the brain. Deformation patterns are investigated quantitatively with respect to the location and magnitude of deformation, and to the distribution and principal direction of the displacements. We also measure the volume change of the lateral ventricles by manual segmentation. Our study indicates that brain shift occurs predominantly in the hemisphere ipsi-lateral to the craniotomy, and that there is more brain deformation during resection procedures than during biopsy or functional procedures. However, the brain deformation patterns are extremely complex in this group of subjects. This paper quantitatively demonstrates that brain deformation occurs not only at the surface, but also in deeper brain structure, and that the principal direction of displacement does not always correspond with the direction of gravity. Therefore, simple computational algorithms that utilize limited intraoperative information (e.g., brain surface shift) will not always accurately predict brain deformation at the lesion.

Intra-operative image updating

Intraoperative brain shift and deformation pose challenges for image-guided surgery. One strategy to address these problems utilizes computational modeling coupled with intraoperatively acquired information from efficient and economical sources such as ultrasound and the optics of the operating microscope. Calibration algorithms for the accurate integration of these sparse data sources have been implemented. Assessment has been performed in both phantom and pig brain models, and accuracy better than 2 mm has been achieved. Methods of incorporating these data into the computational model are being developed.

In vivo quantification of retraction deformation modeling for updated image-guidance during neurosurgery

The use of coregistered preoperative anatomical scans to provide navigational information in the operating room has greatly benefited the field of neurosurgery. Nonetheless, it has been widely acknowledged that significant errors between the operating field and the preoperative images are generated as surgery progresses. Quantification of tissue shift can be accomplished with volumetric intraoperative imaging; however, more functional, lower cost alternative solutions to this challenge are desirable. We are developing the strategy of exploiting a computational model driven by sparse data obtained from intraoperative ultrasound and cortical surface tracking to warp preoperative images to reflect the current state of the operating field. This paper presents an initial quantification of the predictive capability of the current model to computationally capture tissue deformation during retraction in the porcine brain. Performance validation is achieved through comparisons of displacement and pressure predictions to experimental measurements obtained from computed tomographic images and pressure sensor recordings. Group results are based upon a generalized set of boundary conditions for four subjects that, on average, account for at least 75% of tissue motion generated during interhemispheric retraction. Individualized boundary conditions can improve the degree of data-model match by 10% or more but warrant further study. Overall, the level of quantitative agreement achieved in these experiments is encouraging for updating preoperative images to reflect tissue deformation resulting from retraction, especially since model improvements are likely as a result of the intraoperative constraints that can be applied through sparse data collection.

Robotics for surgery

Robotic technology is enhancing surgery through improved precision, stability, and dexterity. In image-guided procedures, robots use magnetic resonance and computed tomography image data to guide instruments to the treatment site. This requires new algorithms and user interfaces for planning procedures; it also requires sensors for registering the patient's anatomy with the preoperative image data. Minimally invasive procedures use remotely controlled robots that allow the surgeon to work inside the patient's body without making large incisions. Specialized mechanical designs and sensing technologies are needed to maximize dexterity under these access constraints. Robots have applications in many surgical specialties. In neurosurgery, image-guided robots can biopsy brain lesions with minimal damage to adjacent tissue. In orthopedic surgery, robots are routinely used to shape the femur to precisely fit prosthetic hip joint replacements. Robotic systems are also under development for closed-chest heart bypass, for microsurgical procedures in ophthalmology, and for surgical training and simulation. Although results from initial clinical experience is positive, issues of clinician acceptance, high capital costs, performance validation, and safety remain to be addressed.

Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Intraoperative compensation for brain shift

BACKGROUND

Tumor removal, brain swelling, the use of brain retractors, and cerebrospinal-fluid drainage all result in an intraoperative brain deformation that is known as brain shift. Thus, neuronavigation systems relying on preoperative image data have a decreasing accuracy during the surgical procedure. Intraoperative image data represent the correct anatomic situation, so their use may compensate for the effects of brain shift.

METHODS

In a series of 16 brain tumor patients, we used intraoperative magnetic resonance (MR) imaging to obtain 3-D data, which were then transferred to the microscope-based neuronavigation system. With the help of bone fiducial markers these images were registered intraoperatively, updating the neuronavigation system.

RESULTS

In all patients the updating of the neuronavigation system with the intraoperative MR data was successful. It led to reliable neuronavigation with high accuracy; the mean registration error of the update procedure in all patients was 1.1 mm. The updating procedure added about 15 minutes to the operation time. In all patients the area suggestive of remaining tumor was reached and the additional tumor could be resected, resulting in a complete tumor removal in 14 patients. In the remaining patients extension of the tumor into eloquent brain areas prevented a complete excision.

CONCLUSIONS

The update of a neuronavigation system with intraoperative MR images reliably compensates for the effects of brain shift. This method allows completion of tumor removal in some difficult brain tumors.