Electrical Impedance Imaging Laboratory
EIT reconstruction algorithms and hardware systems for clinical applications
We are an applied mathematics group focused on the research and development of novel medical imaging techniques.
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A comparison of techniques to improve pulmonary EIT image resolution using a database of simulated EIT images
Kyler Howard, Chris Rocheleau, Trevor Overton, Joel Barraza Nava, Mason Faldet, Kristina Moen, Summer Soller, Tyler Stephens, Esther van de Lagemaat, Natalie Wijesinghe, Kaylee Wong Dollo, Nilton Barbosa da Rosa, Jennifer L. Mueller.
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Inherently low spatial resolution is a well-known challenge in electrical impedance tomography image reconstruction. Various approaches such as the use of spatial priors and post-processing techniques have been proposed to improve the resolution, but in the literature, comparisons using a common dataset representative of clinical images have not been considered. Here, we consider a database of 81,710 simulated EIT datasets constructed from pulmonary CT scans of 89 infants. Four techniques for improved image resolution and several combinations thereof are proposed and compared quantitatively on 16,341 known test cases reserved from the database. The techniques include an end-to-end deep learning reconstruction approach, post-processing of real-time one-step Gauss–Newton (GN) reconstructions using machine learning, post-processing using the Schur complement method, the use of an initial guess for the one-step GN method derived from the image database, and a method that makes use of the eigenfunctions of the principal component analysis of image vectors in the database. All methods resulted in improved metrics of error measurement compared to the Newton one-step error reconstruction method used as the basis for comparison.
Ahmed Abdelwahab, Nilton Barbosa da Rosa Jr, Christopher Wilcox, Omid Rajabi Shishvan, David Isaacson, Jonathan C Newell, Jennifer L Mueller, Gary J Saulnier.
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Objective: This paper introduces a new method of simultaneous acquisition of electrocardiogram (ECG) signals and electrical impedance tomography (EIT) measurements on active electrodes, i.e. electrodes that are applying current, in an EIT system. Methods: Howland noise reduction followed by an integrate-and-dump filter and adaptive filtering are used to extract the ECG signal without loss of amplitude. Results: Results of simultaneous ECG signals and EIT reconstructions on human subjects are shown. It is also demonstrated that the recovered ECG waveforms from 32 electrodes can be used to reconstruct the heart current source vectors during a cardiac cycle, yielding an approximate solution to the inverse problem of electrocardiography. Significance: Acquiring ECG signals from all electrodes attached to the body simultaneously with EIT data acquisition eliminates the need for a separate system to collect ECG signals. Time-aligned ECG signals are helpful in interpreting pulsatile perfusion images by providing exact timing of each image in relation to the cardiac cycle. Additionally, collecting ECG signals from many or all of the EIT electrodes enables reconstruction of the heart’s moving dipole moment at the same time as the EIT images.Roberto C Ceccato, Andre V Pigatto, Richard C Aster, Chi-Nan Pai, Jennifer L Mueller, Sergio S Furuie
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Objective: Quantitative time of flight in transmission mode ultrasound computed tomography (TFTM USCT) is a promising, cost-effective, and non-invasive modality, particularly suited for functional imaging. However, TFTM USCT encounters resolution challenges due to path information concentration in specific medium regions and uncertainty in transducer positioning. This study proposes a method to enhance resolution and robustness, focusing on low-frequency TFTM USCT for pulmonary imaging. Methods: The proposed technique improves the orientation of steepest descent algorithm steps, preventing resolution degradation due to path information concentration, while allowing for a posteriori sensor positioning retrieval. Total variation regularization is employed to stabilize the inverse problem, and a modified Barzilai-Borwein method determined the step size in the steepest descent algorithm. The proposed method was validated through simulations of data on healthy and abnormal cross-sections of a human chest using MATLAB’s k-Wave toolbox. Additionally, experimental data were collected using a Verasonics Vantage 64 low-frequency system and a ballistic gel torso-mimicking phantom to assess robustness under a more realistic environment, closer to that of a clinical situation. Results: The results showed that the proposed method significantly improved image quality and successfully retrieved sensor locations from imprecise positioning. Significance: This study is the first to address transducer location uncertainty on a transducer belt in TFTM USCT and to apply an estimated gradient approach. Additionally, low-frequency USCT for lung imaging is quite novel, and this work addresses practical questions that will be important for translational development.Katelyn G. Enzer, Nilton Barbosa da Rosa, Christopher Rocheleau, Gary Saulnier, Jennifer L. Mueller & Christopher D. Baker
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Electrical impedance tomography (EIT) is an emerging non-invasive and non-ionizing imaging technique. It may be particularly useful for preterm infants, who have heterogeneous lung disease with ventilation and perfusion abnormalities. Preterm infants with severe bronchopulmonary dysplasia (sBPD) requiring invasive mechanical ventilation are sometimes supported with specific ventilation strategies aimed to address their heterogeneous disease [1]. We used EIT to characterize a preterm infant with established sBPD supported by varying methods of invasive and non-invasive mechanical ventilation throughout her postnatal course. Starting at 42 weeks post-menstrual age (PMA), EIT was performed every 3–5 weeks for 5 timepoints on a preterm infant born at 27 weeks gestation with severe fetal growth restriction (birth weight 450 g), sBPD, pulmonary hypertension, Scimitar Syndrome, mesocardia, and chronic respiratory failure ultimately requiring tracheostomy and mechanical ventilation
Albert C. Yates Endowed Chair in Mathematics
Professor
Department of Mathematics
Professor (Joint Appointment)
School of Biomedical Engineering, Department of Electrical and Computer Engineering
Courses: MATH 535, MATH 633