Phantom experiments using soft-prior regularization EIT for breast cancer imaging.

OBJECTIVE: A soft-prior regularization (SR) electrical impedance tomography (EIT) technique for breast cancer imaging is described, which shows an ability to accurately reconstruct tumor/inclusion conductivity values within a dense breast model investigated using a cylindrical and a breast-shaped tank. APPROACH: The SR-EIT method relies on knowing the spatial location of a suspicious lesion initially detected from a second imaging modality. Standard approaches (using Laplace smoothing and total variation regularization) without prior structural information are unable to accurately reconstruct or detect the tumors. The soft-prior approach represents a very significant improvement to these standard approaches, and has the potential to improve conventional imaging techniques, such as automated whole breast ultrasound (AWB-US), by providing electrical property information of suspicious lesions to improve AWB-US's ability to discriminate benign from cancerous lesions. MAIN RESULTS: Specifically, the best soft-regularization technique found average absolute tumor/inclusion errors of 0.015 S m-1 for the cylindrical test and 0.055 S m-1 and 0.080 S m-1 for the breast-shaped tank for 1.8 cm and 2.5 cm inclusions, respectively. The standard approaches were statistically unable to distinguish the tumor from the mammary gland tissue. An analysis of false tumors (benign suspicious lesions) provides extra insight into the potential and challenges EIT has for providing clinically relevant information. SIGNIFICANCE: The ability to obtain accurate conductivity values of a suspicious lesion (>1.8 cm) detected from another modality (e.g. AWB-US) could significantly reduce false positives and result in a clinically important technology.

Comparative study of separation between ex vivo prostatic malignant and benign tissue using electrical impedance spectroscopy and electrical impedance tomography.

OBJECTIVE: Currently no efficient and reliable technique exists to routinely assess surgical margins during a radical prostatectomy. Electrical impedance spectroscopy (EIS) has been reported as a potential technique to provide surgeons with real-time intraoperative margin assessment. In addition to providing a quantified measure of margin status, a co-registered electrical impedance tomography (EIT) image presented on a surgeon's workstation could add value to the margin assessment process. APPROACH: To investigate this, we conducted a comparative study between EIS and EIT to evaluate the potential these technologies might have for margin assessment. EIS and EIT data was acquired from ex vivo human prostates using a multi-electrode endoscopic impedance acquisition probe. MAIN RESULTS: EIS and EIT show good predictive performance with a 0.76 and 0.80 area-under-curve (AUC), respectively, when considering discrete frequencies only. A machine learning (ML) algorithm is implemented to combine features, which improves the AUCs of EIS and EIT to 0.84 and 0.85, respectively. Single-step EIT takes significantly less time to reconstruct than multi-step EIT, yet provides similarly accurate classification results, making the single-step approach a potential candidate for real-time margin assessment. While the ML-based approach clearly exhibits benefits as compared to the single feature assessment, the decision to use EIS versus EIT is unclear since each approach performs better for different subsets of tissue classifications. SIGNIFICANCE: The results presented in this paper corroborate our previous studies and present the strongest evidence yet that an intraoperative-capable impedance probe can be used to distinguish benign from malignant prostate tissues. An in vivo study with a large cohort will be necessary to definitively determine the preferred approach and to show the clinical effectiveness of using this technology for margin assessment.

Absolute Reconstructions Using Rotational Electrical Impedance Tomography for Breast Cancer Imaging.

A rotational Electrical Impedance Tomography (rEIT) methodology is described and shown to produce spatially accurate absolute reconstructions with improved image contrast and an improved ability to distinguish closely spaced inclusions compared to traditional EIT on data recorded from cylindrical and breast-shaped tanks. Rotations of the tank without altering the interior conductivity distribution are used to produce the rEIT data. Quantitatively, rEIT was able to distinguish two inclusions that were 1.5 cm closer together than traditional EIT could achieve for inclusions placed 2 to 3 cm from the center for the cylindrical tank, and rEIT was able to distinguish two tumor-like inclusions where traditional EIT could not reliably do so. Mathematical analysis showed that rEIT improves the number of stable singular vectors by up to 4.2 and 4.7 times than that of traditional EIT for the cylindrical and breast-shaped tanks, respectively, which is an indication of improved resolution. Direct investigations into measurements revealed minimum rotation angles that should yield data uncorrupted by noise. Two inverse approaches (one that inverts then fuses the data (I/DF) and one that fuses the data then inverts (DF/I)) and two mesh modeling approaches were considered. It was found that DF/I produces far better results compared to I/DF and a rotated-mesh approach produces further improvements. The ability to obtain improved absolute reconstructions using rEIT on a practical clinical scenario (breast-shaped tank experiment) is an important step towards using rEIT to improve previous EIT results in medical applications.

Prostate Cancer Detection Using Composite Impedance Metric.

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)- positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p < 0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues.

A Novel Regularization Technique for Microendoscopic Electrical Impedance Tomography.

A novel regularization technique is developed for end-fired microendoscopic electrical impedance tomography using the dual-mesh method. The new regularization technique coupled with appropriate forward modeling and inverse mesh design is shown to produce dramatically improved reconstructions over previous methods. 3D absolute and difference reconstructions from measured saline tank and ex vivo adipose and muscle tissue experiments are used to validate the approach. The ex vivo experiments are used as a surrogate for prostate tissue, which is the primary clinical application for the probe. Inclusion center of mass errors were less than 0.47 mm for tank experiments with inclusion depths and radial offsets ranging less than 3 mm and 1.5 mm, respectively. Absolute 3D reconstructions on the tissue show quantitatively good accuracy and the ability to spatially distinguish small tissue features (adipose strands of approximately 2.5 mm in width). The reconstruction algorithm developed provides strong evidence for the promise of surgical margin detection using microendoscopic EIT.

3D Microendoscopic Electrical Impedance Tomography for Margin Assessment During Robot-Assisted Laparoscopic Prostatectomy.

Radially configured microendoscopic electrical impedance probes intended for intraoperative surgical margin assessment during robot-assisted laparoscopic prostatectomy (RALP) were examined through simulation, bench-top experimentation, and ex vivo tissue studies. Three probe designs with 8, 9, and 17 electrodes, respectively, were analyzed through finite element method based simulations. One mm diameter spherical inclusions ( σinclusion = 1 S/m) are positioned at various locations within a hemispherical background ( σbackground = 0.1 S/m) of radius 5 mm. An 8-electrode configuration is not able to localize the inclusion at these positions while 9 and 17-electrode configurations are able to accurately reconstruct the inclusion at maximum depth of 1 mm and 3 mm, respectively. All three probe designs were constructed and evaluated using saline phantoms and ex vivo porcine and human prostate tissues. The 17-electrode probe performed best in saline phantom studies, accurately reconstructing high contrast, 1-mm-diameter metal cylindrical inclusions in a saline bath ( σsaline = 0.1 S/m) with a position and area error of 0.46 mm and 0.84 mm2, respectively. Additionally, the 17-electrode probe was able to adequately distinguish cancerous from benign tissues in three ex vivo human prostates. Simulations, bench-top saline experiments, and ex vivo tissue sampling suggest that for intraoperative surgical margin assessment during RALP, the 17-electrode probe (as compared to an 8 and 9 electrode probe) will be necessary to provide sufficient accuracy and sensitivity.