Radiofrequency ablation of hepatocellular carcinoma guided by real-time physics-based ablation simulation: a prospective study.

OBJECTIVES: To assess the safety and efficacy of radiofrequency ablation (RFA) guidance software that incorporated patient-specific physics-based simulation of each ablation volume. MATERIALS AND METHODS: Patients referred for curative ablation of hepatocellular carcinoma (HCC) of 2-5 cm diameter were prospectively enrolled. RFA was performed under general anesthesia. Procedure planning and intraprocedural modifications were guided by computer simulation of each ablation. The segmented target (tumor with 5 mm margin) was registered to and superimposed on subsequent 3D multiplanar images. The applied RF energy was used to calculate a simulated ablation volume which was displayed relative to the electrode and segmented target, to depict any untreated target tissue. After each additional ablation, the software updated the accumulated simulated ablation volume in relation to the target. The primary endpoints were technical efficacy and rate of local tumor progression (LTP). RESULTS: Sixty-eight tumors were ablated during 57 procedures in 52 patients (68.3 ± 9.2 years old, 78.8% male); 15 (26.3%) had multiple lesions and 23 (39.1%) had prior HCC treatment. The mean tumor diameter was 2.73 (±0.64) cm. The intraprocedural simulation directed additional overlapping ablations in 75.9% of tumors. Technical success and efficacy were 100% at 3-month contrast enhanced CT or MRI follow-up after the single treatment session. Cumulative incidence function estimates for 1- and 2-year LTP were 3.9% and 20.2%, respectively. CONCLUSION: This prospective study found computer-assisted guidance that simulated each ablation was both safe and efficacious. The low rate of LTP was similar to studies that employed stereotactic guidance and ablation confirmation, without requiring a second contrast enhanced study.

Validation of Software for Patient-Specific Real-Time Simulation of Hepatic Radiofrequency Ablation.

RATIONALE AND OBJECTIVES: CT-guided radiofrequency ablation (RFA) is a potentially curative minimally invasive treatment for liver cancer. Local tumor recurrence limits the success of RFA for large or irregular tumors as it is difficult to visualize the tissue destroyed. This study was designed to validate a real-time software-simulated ablation volume for intraprocedural guidance. MATERIALS AND METHODS: Software that simulated RFA physics calculated ablation volumes in 17 agar-albumin phantoms (7 with a simulated vessel) and in six in-vivo (porcine) ablations. The software-modeled volumes were compared with the actual ablations (physical lesion in agar, contrast CT in the porcine model) and to the volume predicted by the manufacturer's charts. Error was defined as the distance from evenly distributed points on the segmented true ablation volume surfaces to the closest points on the corresponding computer-generated model, and for the porcine model, to the manufacturer-specified ablation volume. RESULTS: The average maximum error of the simulation was 2.8 mm (range to 4.9 mm) in the phantoms. The heat-sink effect from the simulated vessel was well-modeled by the simulation. In the porcine model, the average maximum error of the simulation was 5.2 mm (range to 8.1 mm) vs 7.8 mm (range to 10.0mm) for the manufacturer's model (p = 0.009). CONCLUSION: A real-time computer-generated RFA model incorporated tine position, energy deposited, and large vessel proximity to predict the ablation volume in agar phantoms with less than 3mm maximum error. Although the in-vivo model had slightly higher maximum error, the software better predicted the achieved ablation volume compared to the manufacturer's ablation maps.

FPGA-based voltage and current dual drive system for high frame rate electrical impedance tomography.

Electrical impedance tomography (EIT) is used to image the electrical property distribution of a tissue under test. An EIT system comprises complex hardware and software modules, which are typically designed for a specific application. Upgrading these modules is a time-consuming process, and requires rigorous testing to ensure proper functioning of new modules with the existing ones. To this end, we developed a modular and reconfigurable data acquisition (DAQ) system using National Instruments' (NI) hardware and software modules, which offer inherent compatibility over generations of hardware and software revisions. The system can be configured to use up to 32-channels. This EIT system can be used to interchangeably apply current or voltage signal, and measure the tissue response in a semi-parallel fashion. A novel signal averaging algorithm, and 512-point fast Fourier transform (FFT) computation block was implemented on the FPGA. FFT output bins were classified as signal or noise. Signal bins constitute a tissue's response to a pure or mixed tone signal. Signal bins' data can be used for traditional applications, as well as synchronous frequency-difference imaging. Noise bins were used to compute noise power on the FPGA. Noise power represents a metric of signal quality, and can be used to ensure proper tissue-electrode contact. Allocation of these computationally expensive tasks to the FPGA reduced the required bandwidth between PC, and the FPGA for high frame rate EIT. In 16-channel configuration, with a signal-averaging factor of 8, the DAQ frame rate at 100 kHz exceeded 110 frames s (-1), and signal-to-noise ratio exceeded 90 dB across the spectrum. Reciprocity error was found to be for frequencies up to 1 MHz. Static imaging experiments were performed on a high-conductivity inclusion placed in a saline filled tank; the inclusion was clearly localized in the reconstructions obtained for both absolute current and voltage mode data.

An experimental clinical evaluation of EIT imaging with ℓ1 data and image norms.

Electrical impedance tomography (EIT) produces an image of internal conductivity distributions in a body from current injection and electrical measurements at surface electrodes. Typically, image reconstruction is formulated using regularized schemes in which ℓ2-norms are used for both data misfit and image prior terms. Such a formulation is computationally convenient, but favours smooth conductivity solutions and is sensitive to outliers. Recent studies highlighted the potential of ℓ1-norm and provided the mathematical basis to improve image quality and robustness of the images to data outliers. In this paper, we (i) extended a primal-dual interior point method (PDIPM) algorithm to 2.5D EIT image reconstruction to solve ℓ1 and mixed ℓ1/ℓ2 formulations efficiently, (ii) evaluated the formulation on clinical and experimental data, and (iii) developed a practical strategy to select hyperparameters using the L-curve which requires minimum user-dependence. The PDIPM algorithm was evaluated using clinical and experimental scenarios on human lung and dog breathing with known electrode errors, which requires a rigorous regularization and causes the failure of reconstruction with an ℓ2-norm solution. The results showed that an ℓ1 solution is not only more robust to unavoidable measurement errors in a clinical setting, but it also provides high contrast resolution on organ boundaries.

Transrectal electrical impedance tomography of the prostate: spatially coregistered pathological findings for prostate cancer detection.

PURPOSE: Prostate cancer ranks as one of the most common malignancies and currently represents the second leading cancer-specific cause of death in men. The current use of single modality transrectal ultrasound (TRUS) for biopsy guidance has a limited sensitivity and specificity for accurately identifying cancerous lesions within the prostate. This study introduces a novel prostate cancer imaging method that combines TRUS with electrical impedance tomography (EIT) and reports on initial clinical findings based on in vivo measurements. METHODS: The ultrasound system provides anatomic information, which guides EIT image reconstruction. EIT reconstructions are correlated with semiquantitative pathological findings. Thin plate spline warping transformations are employed to overlay electrical impedance images and pathological maps describing the spatial distribution of prostate cancer, with the latter used as reference for data analysis. Clinical data were recorded from a total of 50 men prior to them undergoing radical prostatectomy for prostate cancer treatment. Student's t-tests were employed to statistically examine the electrical property difference between cancerous tissue and benign tissue as defined through histological assessment of the excised gland. RESULTS: Example EIT reconstructions are presented along with a statistical analysis comparing EIT and pathology. An average transformation error of 1.67% is found when 381 spatially coregistered pathological images are compared with their target EIT reconstructed counterparts. At EIT signal frequencies of 0.4, 3.2, and 25.6 kHz, paired-testing demonstrated that the conductivity of cancerous regions is significantly greater than that of benign regions ( p < 0.0304). CONCLUSIONS: These preliminary clinical findings suggest the potential benefits electrical impedance measurements might have for prostate cancer detection.

Addressing the computational cost of large EIT solutions.

Electrical impedance tomography (EIT) is a soft field tomography modality based on the application of electric current to a body and measurement of voltages through electrodes at the boundary. The interior conductivity is reconstructed on a discrete representation of the domain using a finite-element method (FEM) mesh and a parametrization of that domain. The reconstruction requires a sequence of numerically intensive calculations. There is strong interest in reducing the cost of these calculations. An improvement in the compute time for current problems would encourage further exploration of computationally challenging problems such as the incorporation of time series data, wide-spread adoption of three-dimensional simulations and correlation of other modalities such as CT and ultrasound. Multicore processors offer an opportunity to reduce EIT computation times but may require some restructuring of the underlying algorithms to maximize the use of available resources. This work profiles two EIT software packages (EIDORS and NDRM) to experimentally determine where the computational costs arise in EIT as problems scale. Sparse matrix solvers, a key component for the FEM forward problem and sensitivity estimates in the inverse problem, are shown to take a considerable portion of the total compute time in these packages. A sparse matrix solver performance measurement tool, Meagre-Crowd, is developed to interface with a variety of solvers and compare their performance over a range of two- and three-dimensional problems of increasing node density. Results show that distributed sparse matrix solvers that operate on multiple cores are advantageous up to a limit that increases as the node density increases. We recommend a selection procedure to find a solver and hardware arrangement matched to the problem and provide guidance and tools to perform that selection.

Incorporating a biopsy needle as an electrode in transrectal electrical impedance imaging.

Previous studies have shown that prostate cancer may be detected by a combined transrectal ultrasound and electrical impedance tomography imaging system. However, the sensitivity of the imaging system is limited due to very little current established in the far field distant from the probe surface. Consequently, biopsy needles are introduced to the imaging system to provide current paths in the distal regions. This study demonstrates that image sensitivity can be improved by incorporating the needle electrodes. A phantom experiment is presented to show that contrast to the background is enhanced by 17.4% when imaging with needle electrodes. Simulated reconstructions and some preliminary clinical data also suggest the sensitivity improvement. In summary, TREIT with needle electrodes in the tissue may have great potential in future clinical prostate cancer detection.

Statistical estimation of EIT electrode contact impedance using magic Toeplitz matrix.

The goal of the paper is to propose a fast and reliable method of simultaneous estimation of conductivity and electrode contact impedances for a homogeneous 2D disk. Magic Toeplitz matrix as the Neumann-to-Dirichlet map with finite width electrodes plays the central role in our linear model, called the gapZ model. This model enables testing of various hypotheses using the F-test, such as the uniformity of electrode impedances and their statistical significance. The gapZ model is compared with the finite element approximation, and illustrated and validated with a phantom tank experiment filled with saline. Further this model was illustrated with the patient breast EIT data to identify bad contact electrodes.

Adaptive spatial calibration of a 3D ultrasound system.

PURPOSE: The authors present a method devised to calibrate the spatial relationship between a 3D ultrasound scanhead and its tracker completely automatically and reliably. The user interaction is limited to collecting ultrasound data on which the calibration is based. METHODS: The method of calibration is based on images of a fixed plane of unknown location with respect to the 3D tracking system. This approach has, for advantage, to eliminate the measurement of the plane location as a source of error. The devised method is sufficiently general and adaptable to calibrate scanheads for 2D images and 3D volume sets using the same approach. The basic algorithm for both types of scanheads is the same and can be run unattended fully automatically once the data are collected. The approach was devised by seeking the simplest and most robust solutions for each of the steps required. These are the identification of the plane intersection within the images or volumes and the optimization method used to compute a calibration transformation matrix. The authors use adaptive algorithms in these two steps to eliminate data that would otherwise prevent the convergence of the procedure, which contributes to the robustness of the method. RESULTS: The authors have run tests amounting to 57 runs of the calibration on two a scanhead that produce 3D imaging volumes, at all the available scales. The authors evaluated the system on two criteria: Robustness and accuracy. The program converged to useful values unattended for every one of the tests (100%). Its accuracy, based on the measured location of a reference plane, was estimated to be 0.7 +/- 0.6 mm for all tests combined. CONCLUSIONS: The system presented is robust and allows unattended computations of the calibration parameters required for freehand tracked ultrasound based on either 2D or 3D imaging systems.

In vivo impedance imaging with total variation regularization.

We show that electrical impedance tomography (EIT) image reconstruction algorithms with regularization based on the total variation (TV) functional are suitable for in vivo imaging of physiological data. This reconstruction approach helps to preserve discontinuities in reconstructed profiles, such as step changes in electrical properties at interorgan boundaries, which are typically smoothed by traditional reconstruction algorithms. The use of the TV functional for regularization leads to the minimization of a nondifferentiable objective function in the inverse formulation. This cannot be efficiently solved with traditional optimization techniques such as the Newton method. We explore two implementations methods for regularization with the TV functional: the lagged diffusivity method and the primal dual-interior point method (PD-IPM). First we clarify the implementation details of these algorithms for EIT reconstruction. Next, we analyze the performance of these algorithms on noisy simulated data. Finally, we show reconstructed EIT images of in vivo data for ventilation and gastric emptying studies. In comparison to traditional quadratic regularization, TV regularization shows improved ability to reconstruct sharp contrasts.

The correlation of in vivo and ex vivo tissue dielectric properties to validate electromagnetic breast imaging: initial clinical experience.

Electromagnetic (EM) breast imaging provides low-cost, safe and potentially a more specific modality for cancer detection than conventional imaging systems. A primary difficulty in validating these EM imaging modalities is that the true dielectric property values of the particular breast being imaged are not readily available on an individual subject basis. Here, we describe our initial experience in seeking to correlate tomographic EM imaging studies with discrete point spectroscopy measurements of the dielectric properties of breast tissue. The protocol we have developed involves measurement of in vivo tissue properties during partial and full mastectomy procedures in the operating room (OR) followed by ex vivo tissue property recordings in the same locations in the excised tissue specimens in the pathology laboratory immediately after resection. We have successfully applied all of the elements of this validation protocol in a series of six women with cancer diagnoses. Conductivity and permittivity gauged from ex vivo samples over the frequency range 100 Hz-8.5 GHz are found to be similar to those reported in the literature. A decrease in both conductivity and permittivity is observed when these properties are gauged from ex vivo samples instead of in vivo. We present these results in addition to a case study demonstrating how discrete point spectroscopy measurements of the tissue can be correlated and used to validate EM imaging studies.

GREIT: a unified approach to 2D linear EIT reconstruction of lung images.

Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.

3D electric impedance tomography reconstruction on multi-core computing platforms.

This manuscript presents results relative to the optimization of 3D impedance tomography reconstruction algorithms for execution on multi-core computing platforms. Speed-ups obtainable by the use of modern computing architectures and by an optimized implementation allow the use of much finer FEM meshes in the forward model, leading ultimately to a better image quality. We formulate the reconstruction as widely common in the EIT community: as a non-linear, least squares, Tikhonov regularized, discrete inverse problem. The forward model is based on a FEM solver that implements the Complete Electrode Model. By profiling a plain but careful MATLAB implementation of such an algorithm, we find that, in problems with mesh sizes in the order of 100.000 nodes, typically 95% of the computing time is spent in solving the forward problem and in computing the Jacobian matrix from the forward solutions. We have focused on optimizing the execution of these two functions, and we report relative results. On an octal Xeon 5355 based PC, on problems with forward meshes with a number of nodes in the range of 59,000 nodes to 146,000 nodes, the optimized algorithm has a speed-up of up to 7 times compared to an equivalent MATLAB implementation that makes use of the multithreading capabilities of the platform.

Arbitrary geometry patient interfaces for breast cancer detection and monitoring with electrical impedance tomography.

Electrical impedance tomography (EIT) is a promising technology enabling the detection or observation of many biological processes. This is typically accomplished by applying currents at known locations on an outer surface (in this case skin) and measuring voltages at other locations. This information is then used to determine electrical properties of tissue found between the electrodes by solving the associated Laplace equation. Such problems depend upon knowing the exact boundary conditions (BC). Unfortunately BCs are not always easily determined and approximations are accepted out of necessity due to problem complexity or time constraints. The EIT group at Dartmouth College has developed two new patient interfaces for breast cancer detection and monitoring both of which speed acquisition time and allow for precision BC information in natural and arbitrary geometries. Preliminary experimental results are presented.

Generation of anisotropic-smoothness regularization filters for EIT.

In the inverse conductivity problem, as in any ill-posed inverse problem, regularization techniques are necessary in order to stabilize inversion. A common way to implement regularization in electrical impedance tomography is to use Tikhonov regularization. The inverse problem is formulated as a minimization of two terms: the mismatch of the measurements against the model, and the regularization functional. Most commonly, differential operators are used as regularization functionals, leading to smooth solutions. Whenever the imaged region presents discontinuities in the conductivity distribution, such as interorgan boundaries, the smoothness prior is not consistent with the actual situation. In these cases, the reconstruction is enhanced by relaxing the smoothness constraints in the direction normal to the discontinuity. In this paper, we derive a method for generating Gaussian anisotropic regularization filters. The filters are generated on the basis of the prior structural information, allowing a better reconstruction of conductivity profiles matching these priors. When incorporating prior information into a reconstruction algorithm, the risk is of biasing the inverse solutions toward the assumed distributions. Simulations show that, with a careful selection of the regularization parameters, the reconstruction algorithm is still able to detect conductivities patterns that violate the prior information. A generalized singular-value decomposition analysis of the effects of the anisotropic filters on regularization is presented in the last sections of the paper.