Biomechanical modelling and computer aided simulation of deep brain retraction in neurosurgery

Understanding the Mechanical Properties of Pituitary Adenomas for Optimized Surgery

Pituitary adenoma (PA) is a common brain tumor located in a small cavity at the cranial base. It disrupts hormonal balance and compresses the optic nerves, leading to abnormal body growth, sexual dysfunction, vision loss, and mortality if untreated. Its surgical resection is highly challenging due to its small size, heterogeneous structure, deep location, and indistinct interface with surrounding nerves, arteries, and brain tissues. Mechanical properties of tumor tissues play a crucial role in their microstructure, growth, and progression. However, data on the mechanical properties of PA tissues is scarce. This study aims to provide detailed mechanical properties of various PA tissues and demonstrate the differences in stiffness between tumors and brain tissues. The viscoelastic properties and collagen content of postoperative PA tissues (n = 40) and normal human brain white matter (n = 7) were analyzed using in vitro nanoindentation and histological staining, respectively. Tumor consistency was also assessed preoperatively via magnetic resonance images (MRIs) and intraoperatively through surgeon feedback. PA tissues exhibited a considerable variation in viscoelastic properties; however, their average stiffness was significantly higher than normal brain white matter (p < 0.05). Tumors with firm consistency showed higher collagen content (29.8%± $$ \pm $$ 21.2%) than the soft (9.1%± $$ \pm $$ 8.1%) and medium (12.9%± $$ \pm $$ 9.7%) consistency tumors, however the correlation with mechanical properties was not strong (r = 0.40, p = 0.01). Strong correlations between preoperative predictions, intraoperative observations, and postoperative measurements emphasize the clinical relevance of these findings. These results underscore the potential of mechanical biomarkers to enhance surgical strategies, improve outcomes, and support applications in diagnosis, development of elastography and elastic image fusion algorithms, as well as in robot-assisted interventions.

Intraoperative interaction modeling between surgical instruments and soft tissues in neurosurgery based on energy functions

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas. Experimental results show that the proposed method meets the requirements of neurosurgical simulation.

A comprehensive review on modeling aspects of infusion-based drug delivery in the brain

Brain disorders represent an ever-increasing health challenge worldwide. While conventional drug therapies are less effective due to the presence of the blood-brain barrier, infusion-based methods of drug delivery to the brain represent a promising option. Since these methods are mechanically controlled and involve multiple physical phases ranging from the neural and molecular scales to the brain scale, highly efficient and precise delivery procedures can significantly benefit from a comprehensive understanding of drug-brain and device-brain interactions. Behind these interactions are principles of biophysics and biomechanics that can be described and captured using mathematical models. Although biomechanics and biophysics have received considerable attention, a comprehensive mechanistic model for modeling infusion-based drug delivery in the brain has yet to be developed. Therefore, this article reviews the state-of-the-art mechanistic studies that can support the development of next-generation models for infusion-based brain drug delivery from the perspective of fluid mechanics, solid mechanics, and mathematical modeling. The supporting techniques and database are also summarized to provide further insights. Finally, the challenges are highlighted and perspectives on future research directions are provided. STATEMENT OF SIGNIFICANCE: Despite the immense potential of infusion-based drug delivery methods for bypassing the blood-brain barrier and efficiently delivering drugs to the brain, achieving optimal drug distribution remains a significant challenge. This is primarily due to our limited understanding of the complex interactions between drugs and the brain that are governed by principles of biophysics and biomechanics, and can be described using mathematical models. This article provides a comprehensive review of state-of-the-art mechanistic studies that can help to unravel the mechanism of drug transport in the brain across the scales, which underpins the development of next-generation models for infusion-based brain drug delivery. More broadly, this review will serve as a starting point for developing more effective treatments for brain diseases and mechanistic models that can be used to study other soft tissue and biomaterials.

A comparison of brain retraction mechanisms using finite element analysis and the effects of regionally heterogeneous material properties

Finite element (FE) simulations of the brain undergoing neurosurgical procedures present us with the great opportunity to better investigate, understand, and optimize surgical techniques and equipment. FE models provide access to data such as the stress levels within the brain that would otherwise be inaccessible with the current medical technology. Brain retraction is often a dangerous but necessary part of neurosurgery, and current research focuses on minimizing trauma during the procedure. In this work, we present a simulation-based comparison of different types of retraction mechanisms. We focus on traditional spatulas and tubular retractors. Our results show that tubular retractors result in lower average predicted stresses, especially in the subcortical structures and corpus callosum. Additionally, we show that changing the location of retraction can greatly affect the predicted stress results. As the model predictions highly depend on the material model and parameters used for simulations, we also investigate the importance of using region-specific hyperelastic and viscoelastic material parameters when modelling a three-dimensional human brain during retraction. Our investigations demonstrate how FE simulations in neurosurgical techniques can provide insight to surgeons and medical device manufacturers. They emphasize how further work into this direction could greatly improve the management and prevention of injury during surgery. Additionally, we show the importance of modelling the human brain with region-dependent parameters in order to provide useful predictions for neurosurgical procedures.

Brain retraction injury: systematic literature review

Cerebral retraction is frequently required in cranial surgery to access deep areas. Brain retractors have been systematically used in the past, but they have been associated with brain injury. Nonetheless, they are still used and, even recently, new systems have been advocated. The aim of this study is to provide a systematic and critical review of brain retraction injury. A systematic literature review was performed in February 2023 according to PRISMA statement. Search terms included brain retraction and injury, with their variations and pertinent associations. Studies reporting qualitative and quantitative data on brain retraction injury were included. Out of 1689 initially retrieved articles, 90 and 26 were included in the systematic review for qualitative and quantitative data, respectively. The definition of brain retraction injury varies and its reported incidence in clinical studies is 5-10%, up to 47% if cerebral edema is considered. Some studies have hypothesized threshold values of pressures to be respected in order to prevent complications, with most data deriving from animal studies. At present, there are no instruments for brain retraction that can guarantee full safety. Some form of cerebral retraction might always be necessary for specific scenarios. Further studies are needed to collect quantitative and, ideally, clinical and comparative data on pressure thresholds to develop retraction systems that can reduce injury to a minimum.

A wavelet neural operator based elastography for localization and quantification of tumors

BACKGROUND AND OBJECTIVES

The application of intelligent imaging techniques and deep learning in the field of computer-aided diagnosis and medical imaging have improved and accelerated the early diagnosis of many diseases. Elastography is an imaging modality where an inverse problem is solved to extract the elastic properties of tissues and subsequently mapped to anatomical images for diagnostic purposes. In the present work, we propose a wavelet neural operator-based approach for correctly learning the non-linear mapping of elastic properties directly from measured displacement field data.

METHODS

The proposed framework learns the underlying operator behind the elastic mapping and thus can map any displacement data from a family to the elastic properties. The displacement fields are first uplifted to a high-dimensional space using a fully connected neural network. On the lifted data, certain iterations are performed using wavelet neural blocks. In each wavelet neural block, the lifted data are decomposed into low, and high-frequency components using wavelet decomposition. To learn the most relevant patterns and structural information from the input, the neural network kernels are directly convoluted with the outputs of the wavelet decomposition. Thereafter the elasticity field is reconstructed from the outputs from convolution. The mapping between the displacement and the elasticity using wavelets is unique and remains stable during training.

RESULTS

The proposed framework is tested on several artificially fabricated numerical examples, including a benign-cum-malignant tumor prediction problem. The trained model was also tested on real Ultrasound-based elastography data to demonstrate the applicability of the proposed scheme in clinical usage. The proposed framework reproduces the highly accurate elasticity field directly from the displacement inputs.

CONCLUSIONS

The proposed framework circumvents different data pre-processing and intermediate steps utilized in traditional methods, hence providing an accurate elasticity map. The computationally efficient framework requires fewer epochs for training, which bodes well for its clinical usability for real-time predictions. The weights and biases from pre-trained models can also be employed for transfer learning, which reduces the effective training time with random initialization.

A deformation model of pulsating brain tissue for neurosurgery simulation

BACKGROUND AND OBJECTIVES

For neurological simulation, an accurate deformation model of brain tissue is of key importance for faithful visual feedback. Existing models, however, do not take into account intracranial pulsation, which degrades significantly the realism of visual feedback.

METHODS

In this paper, a finite element model incorporating intracranial pressure is proposed for simulating brain tissue deformation with pulsation. An implicit Euler method is developed to calculate the deformation of brain tissue. A circuit model of intracranial pressure dynamics is established based on cerebral blood and cerebrospinal fluid circulations. The intracranial pulsation of pressure is introduced into the deformation model, so that the simulated brain tissues pulsate with a rhythm in accord with the changes of intracranial pressure, which resembles real-life neurosurgery.

RESULTS AND CONCLUSIONS

The experimental implementation of the proposed deformation model and the calculation method shows that it provides realistic simulation of brain tissue pulsation and real-time performance is achieved on an ordinary computer for certain procedures of neurosurgery.

Viscoelastic parameters of invasive breast cancer in correlation with porous structure and elemental analysis data

BACKGROUND AND OBJECTIVE

Invasive ductal carcinoma (IDC) is the most common and aggressive type of breast cancer. As many clinical diagnoses are concerned with the tumor behavior at the compression, the IDC characterization using a compression test is performed in the present study. In the field of tissue characterization, most of the previous studies have focused on healthy and cancerous breast tissues at the cellular level; however, characterization of cancerous tissue at the tissue level has been under-represented, which is the target of the present study.

METHODS

Throughout this article, 18 IDC samples are tested using a ramp-relaxation test. The strain rate in the ramp phase is similar for all samples, whereas the strain level is set at 2,4 and 6%. The experimental stress-time data is interpolated by a viscoelastic model. Two relaxation times, as well as the instantaneous and long-term shear moduli, are calculated for each specimen.

RESULTS

The results show that the long-term and instantaneous shear moduli vary in the range of 0.31-17.03 kPa and 6.03-55.13 kPa, respectively. Our assessment of the viscoelastic parameters is accompanied by observing structural images of the IDCs and inspecting their elemental composition. It is concluded that IDCs with lower Magnesium to Calcium ratio (Mg:Ca) have smaller shear modulus and longer relaxation time, with a p-value of 0.001 and 0.01 for the correlation between Mg:Ca and long-term shear modulus, and Mg:Ca and early relaxation time.

CONCLUSIONS

Our identification of the IDC viscoelastic parameters can contribute to the IDC inspection at the tissue level. The results also provide useful information for modeling of breast cancer.

MedmeshCNN - Enabling meshcnn for medical surface models

BACKGROUND AND OBJECTIVE

MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of popular benchmarking datasets. The medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion dedicated to complex, diverse, and fine-grained medical data.

METHODS

MedMeshCNN follows the functionality of MeshCNN with a significantly increased memory efficiency that allows retaining patient-specific properties during processing. Furthermore, it enables the segmentation of pathological structures that often come with highly imbalanced class distributions.

RESULTS

MedMeshCNN achieved an Intersection over Union of 63.24% on a highly complex part segmentation task of intracranial aneurysms and their surrounding vessel structures. Pathological aneurysms were segmented with an Intersection over Union of 71.4%.

CONCLUSIONS

MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. It considers imbalanced class distributions derived from pathological findings and retains patient-specific properties during processing.

Particle hydrodynamic simulation of thrombus formation using velocity decay factor

BACKGROUND AND OBJECTIVE

Thrombus simulation plays an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, treatment planning, etc. Although a considerable number of methods have been developed to simulate various kinds of fluid flows, it remains a non-trivial task to effectively simulate thrombus because of its unique physiological properties in contrast to other types of fluids. To tackle this issue, this study introduces a novel method to model the formation mechanism of thrombus and its interaction with blood flow.

METHODS

The proposed method for thrombus formation simulation mainly consists of three steps. First, we formulate the formation of thrombus as a particle-based model and obtain the fibrin concentration of the particles with a discretized form of the convection-diffusion-reaction equation; then, we calculate the velocity decay factor using the obtained fibrin concentration. Finally, the formation of thrombus can be simulated by applying the velocity decay factor on particles.

RESULTS

We carried out extensive experiments under different settings to verify the efficacy of the proposed method. The experimental results demonstrate that our method can yield more realistic simulation of thrombus and is superior to peer method in terms of computational efficiency, maintaining the stability of the dynamic particle motion, and preventing particle penetration at the boundary.

CONCLUSION

The proposed method can simulate the formation mechanism of thrombus and the interaction between blood flow and thrombus both efficiently and effectively.