Shadab Khan

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.

Geometric correction of deformed chromosomes for automatic Karyotyping

Automatic Karyotyping is the process of classifying chromosomes from an unordered karyogram into their respective classes to create an ordered karyogram. Automatic karyotyping algorithms typically perform geometrical correction of deformed chromosomes for feature extraction; these features are used by classifier algorithms for classifying the chromosomes. Karyograms of bone marrow cells are known to have poor image quality. An example of such karyograms is the Lisbon-K1 (LK1) dataset that is used in our work. Thus, to correct the geometrical deformation of chromosomes from LK1, a robust method to obtain the medial axis of the chromosome was necessary. To address this problem, we developed an algorithm that uses the seed points to make a primary prediction. Subsequently, the algorithm computes the distance of boundary from the predicted point, and the gradients at algorithm-specified points on the boundary to compute two auxiliary predictions. Primary prediction is then corrected using auxiliary predictions, and a final prediction is obtained to be included in the seed region. A medial axis is obtained this way, which is further used for geometrical correction of the chromosomes. This algorithm was found capable of correcting geometrical deformations in even highly distorted chromosomes with forked ends.

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 1 MHz), 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.

Towards intraoperative surgical margin assessment and visualization using bioimpedance properties of the tissue

Prostate cancer (PCa) has a high 10-year recurrence rate, making PCa the second leading cause of cancer-specific mortality among men in the USA. PCa recurrences are often predicted by assessing the status of surgical margins (SM) with positive surgical margins (PSM) increasing the chances of biochemical recurrence by 2-4 times. To this end, an SM assessment system using Electrical Impedance Spectroscopy (EIS) was developed with a microendoscopic probe. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a Composite Impedance Metric (CIM). CIM can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from excised prostates, which were obtained from men undergoing radical prostatectomy. The data revealed statistically significant (p<0.05) differences in the impedance properties of the benign and tumorous tissues, and between different tissue morphologies. To visualize the results of SM-assessment, a visualization tool using da Vinci stereo laparoscope is being developed. Together with the visualization tool, the EIS-based SM assessment system can be potentially used to intraoperatively classify tissues and display the results on the surgical console with a video feed of the surgical site, thereby augmenting a surgeon’s view of the site and providing a potential solution to the intraoperative SM assessment needs.

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 surgeons 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.

A multimodal imaging framework for enhanced robot-assisted partial nephrectomy guidance

Robot-assisted laparoscopic partial nephrectomies (RALPN) are performed to treat patients with locally confined renal carcinoma. There are well-documented benefits to performing partial (opposed to radical) kidney resections and to using robot-assisted laparoscopic (opposed to open) approaches. However, there are challenges in identifying tumor margins and critical benign structures including blood vessels and collecting systems during current RALPN procedures. The primary objective of this effort is to couple multiple image and data streams together to augment visual information currently provided to surgeons performing RALPN and ultimately ensure complete tumor resection and minimal damage to functional structures (i.e. renal vasculature and collecting systems). To meet this challenge we have developed a framework and performed initial feasibility experiments to couple pre-operative high-resolution anatomic images with intraoperative MRI, ultrasound (US) and optical-based surface mapping and kidney tracking. With these registered images and data streams, we aim to overlay the high-resolution contrast-enhanced anatomic (CT or MR) images onto the surgeon’s view screen for enhanced guidance. To date we have integrated the following components of our framework: 1) a method for tracking an intraoperative US probe to extract the kidney surface and a set of embedded kidney markers, 2) a method for co-registering intraoperative US scans with pre-operative MR scans, and 3) a method for deforming pre-op scans to match intraoperative scans. These components have been evaluated through phantom studies to demonstrate protocol feasibility.

Electrical impedance map (EIM) for margin assessment during robot-assisted laparoscopic prostatectomy (RALP) using a microendoscopic probe

Positive surgical margins (PSMs) found following prostate cancer surgery are a significant risk factor for post-operative disease recurrence. Noxious adjuvant radiation and chemical-based therapies are typically offered to men with PSMs. Unfortunately, no real-time intraoperative technology is currently available to guide surgeons to regions of suspicion during the initial prostatectomy where immediate surgical excisions could be used to reduce the chance of PSMs. A microendoscopic electrical impedance sensing probe was developed with the intention of providing real-time feedback regarding margin status to surgeons during robot-assisted laparoscopic prostatectomy (RALP) procedures. A radially configured 17-electrode microendoscopic probe was designed, constructed, and initially evaluated through use of gelatin-based phantoms and an ex vivo human prostate specimen. Impedance measurements are recorded at 10 frequencies (10 kHz - 100 kHz) using a high-speed FPGA-based electrical impedance tomography (EIT) system. Tetrapolar impedances are recorded from a number of different electrode configurations strategically chosen to sense tissue in a pre-defined sector underlying the probe face. A circular electrical impedance map (EIM) with several color-coded pie-shaped sectors is created to represent the impedance values of the probed tissue. Gelatin phantom experiments show an obvious distinction in the impedance maps between high and low impedance regions. Similarly, the EIM generated from the ex vivo prostate case shows distinguishing features between cancerous and benign regions. Based on successful development of this probe and these promising initial results, EIMs of additional prostate specimens are being collected to further evaluate this approach for intraoperative surgical margin assessment during RALP procedures.

A Framework for multimodal image guidance for robot-assisted partial nephrectomy

Robot-assisted laparoscopic partial nephrectomies (RALPN) are performed to treat patients with locally confined renal carcinoma. However, there are challenges in identifying tumor margins and critical benign structures in this time-critical procedure. The primary objective of this effort is to couple multiple image and data streams together to augment visual information currently provided to surgeons performing RALPN and ultimately ensure complete tumor resection and minimal damage to functional structures. Our framework involves registering high-resolution anatomic images with intra-operative ultrasound (US) and optical-based surface mapping using EM tracking. We have currently implemented methods for surface reconstructions from MR, US, and the stereo-endoscope, US-to- MR registration, and deformation registration using control points. These components have been evaluated through phantom studies to demonstrate protocol feasibility.