Mr. Saaid H. Arshad Profile

Saaid H. Arshad

Ph.D. Candidate at Thayer School of Engineering at Dartmouth

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Proceedings Article | 12 March 2018 Paper

Automated segmentation and feature extraction in cardiac electrical impedance tomography images

Saaid Arshad, Ethan Murphy, Ryan Halter

Proceedings Volume 10578, 105781Q (2018) https://doi.org/10.1117/12.2293917

KEYWORDS: Image segmentation, Heart, Feature extraction, Data modeling, Tomography, Reconstruction algorithms, Chest, Picture Archiving and Communication System, Electrodes, Finite element methods

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A non-invasive and accurate modality that can continuously monitor stroke volume (SV) for extended periods of time is desired to allow for more proactive care of an increasing population of patients living with heart failure. Electrical impedance tomography (EIT) has been proposed as a method for accurate, non-invasive, continuous, and long-term SV monitoring. While cardiac EIT has been explored, clinical translation has yet to occur and a standardized method for evaluation and comparison of cardiac EIT images is desired. This work explores an automated process for segmenting and extracting features from the images that allow for evaluation and comparison. A simulation study was conducted using the 4D XCAT model to evaluate the proposed method’s ability to automatically segment and extract features from images reconstructed at various phases of the cardiac cycle. The same procedure was then applied to EIT reconstructions on data collected from five healthy volunteers. The automated segmentation is able to accurately capture the heart region-of-interest (ROI) in various images and extract features, which allows comparison of desired signals across reconstructions. ROI mean conductivity, ROI area, sum of conductivities within the ROI, and ROI maximum conductivity were chosen as promising features from the simulation study, with R² values of 0.61, 0.73, 0.75, and 0.66 for a single heart-cycle, and minimum SV distinguishability of 25.54, 12.16, 12.16, and 17.22 ml. In experimental data, the area feature showed the least variation across individual reconstructions while the sum feature showed the highest variation.

Proceedings Article | 29 March 2016 Paper

Respiratory-gated electrical impedance tomography: a potential technique for quantifying stroke volume

Saaid Arshad, Ethan Murphy, Ryan Halter

Proceedings Volume 9788, 97882D (2016) https://doi.org/10.1117/12.2216595

KEYWORDS: Tomography, Heart, Cardiac imaging, Chest, Data modeling, Electrodes, 3D modeling, Finite element methods, Reconstruction algorithms

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Telemonitoring is becoming increasingly important as the proportion of the population living with cardiovascular disease (CVD) increases. Currently used health parameters in the suite of telemonitoring tools lack the sensitivity and specificity to accurately predict heart failure events, forcing physicians to play a reactive versus proactive role in patient care. A novel cardiac output (CO) monitoring device is proposed that leverages a custom smart phone application and a wearable electrical impedance tomography (EIT) system. The purpose of this work is to explore the potential of using respiratory-gated EIT to quantify stroke volume (SV) and assess its feasibility using real data. Simulations were carried out using the 4D XCAT model to create anatomically realistic meshes and electrical conductivity profiles representing the human thorax and the intrathoracic tissue. A single 5-second period respiration cycle with chest/lung expansion was modeled with end-diastole (ED) and end-systole (ES) heart volumes to evaluate how effective EIT-based conductivity changes represent clinically significant differences in SV. After establishing a correlation between conductivity changes and SV, the applicability of the respiratory-gated EIT was refined using data from the PhysioNet database to estimate the number of useful end-diastole (ED) and end-systole (ES) heart events attained over a 3.3 minute period. The area associated with conductivity changes was found to correlate to SV with a correlation coefficient of 0.92. A window of 12.5% around peak exhalation was found to be the optimal phase of the respiratory cycle from which to record EIT data. Within this window, ~47 useable ED and ES were found with a standard deviation of 28 using 3.3 minutes of data for 20 patients.

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