Treatment Planning and Aberration Correction Algorithm for HIFU Ablation of Renal Tumors

High-intensity focused ultrasound (HIFU) applications for thermal or mechanical ablation of renal tumors often encounter challenges due to significant beam aberration and refraction caused by oblique beam incidence, inhomogeneous tissue layers, and presence of gas and bones within the beam. These losses can be significantly mitigated through sonication geometry planning, patient positioning, and aberration correction using multielement phased arrays. Here, a sonication planning algorithm is introduced, which uses the simulations to select the optimal transducer position and evaluate the effect of aberrations and acoustic field quality at the target region after aberration correction. Optimization of transducer positioning is implemented using a graphical user interface (GUI) to visualize a segmented 3-D computed tomography (CT)-based acoustic model of the body and to select sonication geometry through a combination of manual and automated approaches. An HIFU array (1.5 MHz, 256 elements) and three renal cell carcinoma (RCC) cases with different tumor locations and patient body habitus were considered. After array positioning, the correction of aberrations was performed using a combination of backpropagation from the focus with an ordinary least squares (OLS) optimization of phases at the array elements. The forward propagation was simulated using a combination of the Rayleigh integral and k-space pseudospectral method (k-Wave toolbox). After correction, simulated HIFU fields showed tight focusing and up to threefold higher maximum pressure within the target region. The addition of OLS optimization to the aberration correction method yielded up to 30% higher maximum pressure compared to the conventional backpropagation and up to 250% higher maximum pressure compared to the ray-tracing method, particularly in strongly distorted cases.

A transducer positioning method for transcranial focused ultrasound treatment of brain tumors

Purpose

As a non-invasive method for brain diseases, transcranial focused ultrasound (tFUS) offers higher spatial precision and regulation depth. Due to the altered path and intensity of sonication penetrating the skull, the focus and intensity in the skull are difficult to determine, making the use of ultrasound therapy for cancer treatment experimental and not widely available. The deficiency can be effectively addressed by numerical simulation methods, which enable the optimization of sonication modulation parameters and the determination of precise transducer positioning.

Methods

A 3D skull model was established using binarized brain CT images. The selection of the transducer matrix was performed using the radius positioning (RP) method after identifying the intracranial target region. Simulations were performed, encompassing acoustic pressure (AP), acoustic field, and temperature field, in order to provide compelling evidence of the safety of tFUS in sonication-induced thermal effects.

Results

It was found that the angle of sonication path to the coronal plane obtained at all precision and frequency models did not exceed 10° and 15° to the transverse plane. The results of thermal effects illustrated that the peak temperatures of tFUS were 43.73°C, which did not reach the point of tissue degeneration. Once positioned, tFUS effectively delivers a Full Width at Half Maximum (FWHM) stimulation that targets tumors with diameters of up to 3.72 mm in a one-off. The original precision model showed an attenuation of 24.47 ± 6.13 mm in length and 2.40 ± 1.42 mm in width for the FWHM of sonication after penetrating the skull.

Conclusion

The vector angles of the sonication path in each direction were determined based on the transducer positioning results. It has been suggested that when time is limited for precise transducer positioning, fixing the transducer on the horizontal surface of the target region can also yield positive results for stimulation. This framework used a new transducer localization method to offer a reliable basis for further research and offered new methods for the use of tFUS in brain tumor-related research.

Development of a Numerical Model of High-Intensity Focused Ultrasound Treatment in Mobile and Elastic Organs: Application to a Beating Heart

High-intensity focused ultrasound (HIFU) is a promising method used to treat cardiac arrhythmias, as it can induce lesions at a distance throughout myocardium thickness. Numerical modeling is commonly used for ultrasound probe development and optimization of HIFU treatment strategies. This study was aimed at describing a numerical method to simulate HIFU thermal ablation in elastic and mobile heart models. The ultrasound pressure field is computed on a 3-D orthonormal grid using the Rayleigh integral method, and the attenuation is calculated step by step between cells. The temperature distribution is obtained by resolution of the bioheat transfer equation on a 3-D non-orthogonally structured curvilinear grid using the finite-volume method. The simulation method is applied on two regions of the heart (atrioventricular node and ventricular apex) to compare the thermal effects of HIFU ablation depending on deformation, motion type and amplitude. The atrioventricular node requires longer sonication than the ventricular apex to reach the same lesion volume. Motion considerably influences treatment duration, lesion shape and distribution in cardiac HIFU treatment. These results emphasize the importance of considering local motion and deformation in numerical studies to define efficient and accurate treatment strategies.

Dissipative Particle Dynamics Simulation of Ultrasound Propagation through Liquid Water

Ultrasound is widely used as a noninvasive method in therapeutic and diagnostic applications. These can be further optimized by computational approaches, as they allow for controlled testing and rational optimization of the ultrasound parameters, such as frequency and amplitude. Usually, continuum numerical methods are used to simulate ultrasound propagating through different tissue types. In contrast, ultrasound simulations using particle description are less common, as the implementation is challenging. In this work, a dissipative particle dynamics model is used to perform ultrasound simulations in liquid water. The effects of frequency and thermostat parameters are studied and discussed. We show that frequency and thermostat parameters affect not only the attenuation but also the computed speed of sound. The present study paves the way for development and optimization of a virtual ultrasound machine for large-scale biomolecular simulations.

Towards real-time finite-strain anisotropic thermo-visco-elastodynamic analysis of soft tissues for thermal ablative therapy

BACKGROUND AND OBJECTIVES

Accurate and efficient prediction of soft tissue temperatures is essential to computer-assisted treatment systems for thermal ablation. It can be used to predict tissue temperatures and ablation volumes for personalised treatment planning and image-guided intervention. Numerically, it requires full nonlinear modelling of the coupled computational bioheat transfer and biomechanics, and efficient solution procedures; however, existing studies considered the bioheat analysis alone or the coupled linear analysis, without the fully coupled nonlinear analysis.

METHODS

We present a coupled thermo-visco-hyperelastic finite element algorithm, based on finite-strain thermoelasticity and total Lagrangian explicit dynamics. It considers the coupled nonlinear analysis of (i) bioheat transfer under soft tissue deformations and (ii) soft tissue deformations due to thermal expansion/shrinkage. The presented method accounts for anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, and it is implemented using GPU acceleration for real-time computation.

RESULTS

The presented method can achieve thermo-visco-elastodynamic analysis of anisotropic soft tissues undergoing large deformations with high computational speeds in tetrahedral and hexahedral finite element meshes for surgical simulation of thermal ablation. We also demonstrate the translational benefits of the presented method for clinical applications using a simulation of thermal ablation in the liver.

CONCLUSION

The key advantage of the presented method is that it enables full nonlinear modelling of the anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, instead of linear elastic, linear viscoelastic, and thermal-only modelling in the existing methods. It also provides high computational speeds for computer-assisted treatment systems towards enabling the operator to simulate thermal ablation accurately and visualise tissue temperatures and ablation zones immediately.

Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation

BACKGROUND AND OBJECTIVES

During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to surgical tool-tissue interaction and patient movement can affect the distribution of thermal energy induced. Soft tissue temperature must be obtained from the deformed tissue for precise delivery of thermal energy. However, the classical Pennes bio-heat transfer model can handle only the static non-moving state of tissue. In addition, in order to enable a surgeon to visualise the simulated results immediately, the solution procedure must be suitable for real-time thermal applications.

METHODS

This paper presents a formulation of bio-heat transfer under the effect of soft tissue deformation for fast or near real-time tissue temperature prediction, based on fast explicit dynamics finite element algorithm (FED-FEM) for transient heat transfer. The proposed thermal analysis under deformation is achieved by transformation of the unknown deformed tissue state to the known initial static state via a mapping function. The appropriateness and effectiveness of the proposed formulation are evaluated on a realistic virtual human liver model with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer.

RESULTS

For numerical accuracy, the proposed formulation can achieve a typical 10^-3 level of normalised relative error at nodes and between 10^-4 and 10^-5 level of total errors for the simulation, by comparing solutions against the commercial finite element analysis package. For computation time, the proposed formulation under tissue deformation with anisotropic temperature-dependent properties consumes 2.518 × 10^-4 ms for one element thermal loads computation, compared to 2.237 × 10^-4 ms for the formulation without deformation which is 0.89 times of the former. Comparisons with three other formulations for isotropic and temperature-independent properties are also presented.

CONCLUSIONS

Compared to conventional methods focusing on numerical accuracy, convergence and stability, the proposed formulation focuses on computational performance for fast tissue thermal analysis. Compared to the classical Pennes model that handles only the static state of tissue, the proposed formulation can achieve fast thermal analysis on deformed states of tissue and can be applied in addition to tissue deformable models for non-linear heating analysis at even large deformation of soft tissue, leading to great translational potential in dynamic tissue temperature analysis and thermal dosimetry computation for computer-integrated medical education and personalised treatment.

Image-Aligned Dynamic Liver Reconstruction Using Intra-Operative Field of Views for Minimal Invasive Surgery

During hepatic minimal invasive surgery (MIS), 3-D reconstruction of a liver surface by interpreting the geometry of its soft tissues is achieving attractions. One of the major issues to be addressed in MIS is liver deformation. Moreover, it severely inhibits free sight and dexterity of tissue manipulation, which causes its intra-operative morphology and soft tissue motion altered as compared to its pre-operative shape. While many applications focus on 3-D reconstruction of rigid or semi-rigid scenes, the techniques applied in hepatic MIS must be able to cope with a dynamic and deformable environment. We propose an efficient technique for liver surface reconstruction based on the structure from motion to handle liver deformation. The reconstructed liver will assist surgeons to visualize liver surface more efficiently with better depth perception. We use the intra-operative field of views to generate 3-D template mesh from a dense keypoint cloud. We estimate liver deformation by finding best correspondence between 3-D templates and reconstruct a liver image to calculate translation and rotational motions. Our technique then finely tunes deformed surface by adding smoothness using shading cues. Up till now, this technique is not used for solving the human liver deformation problem. Our approach is tested and validated with synthetic as well as real in vivo data, which reveal that the reconstruction accuracy can be enhanced using our approach even in challenging laparoscopic environments.

A focused ultrasound treatment system for moving targets (part I): generic system design and in-silico first-stage evaluation

Background

Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging.

Methods

A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives.

Results

With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator.

Conclusions

The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice.