Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Optimizing Image Reconstruction in Electrical Impedance Tomography

Tato disertační práce pojednává o optimalizaci algoritmů pro rekonstrukci obrazu neznámé měrné vodivosti z měřených dat pořízených elektrickou impedanční tomografií. Danou problematiku zde věcně vymezuje několik různých prvků, zejména pak stručný matematický popis dopředné a inverzní úlohy řešené různými přístupy, metodika měření a pořizování dat pro rekonstrukci a přehled dostupných numerických nástrojů. Uvedenou charakteristiku rozšiřuje rozbor optimalizací parametrů modelu ovlivňujících přesnost rekonstrukce, způsoby paralelního zpracování algoritmů a souhrn dostupných zařízení pro měření tomografických dat. Na základě získaných poznatků byla navržena optimalizace parametrů matematického modelu, která umožňuje jeho velmi přesný návrh dle měřených dat. V této souvislosti dochází ke snížení nejistoty rekonstrukce rozložení konduktivity. Pro zefektivnění procesu získávání dat bylo navrženo zařízení k automatizaci tomografie s důrazem na cenovou dostupnost a snížení nejistoty měření. V oblasti tvorby numerického modelu byly dále zkoumány možnosti užití otevřených a uzavřených domén pro různé metody regularizace a hrubost sítě, a to s ohledem na velikost chyby rekonstruované konduktivity a výpočetní náročnost. Součástí práce je také paralelizace subalgoritmů rekonstrukce s využitím vícejádrové grafické karty. Předložené výsledky mají přímý vliv na snížení nejistoty rekonstrukce (optimalizací počáteční hodnoty konduktivity, rozmístění elektrod a tvarové deformace domény, regularizačních metod a typu domén) a urychlení výpočtů paralelizací algoritmů, přičemž výzkum byl podpořen vlastním návrhem jednotky pro automatizaci tomografie.The thesis presents, analyzes, and discusses the optimization of algorithms that reconstruct images of unknown specific conductivity from data acquired via electrical impedance tomography. In this context, the author provides a brief mathematical description of the forward and inverse tasks solved by using diverse approaches, characterizes relevant measurement techniques and data acquisition procedures, and discusses available numerical tools. Procedurally, the initial working stages involved analyzing the methods for optimizing those parameters of the model that influence the reconstruction accuracy; demonstrating approaches to the parallel processing of the algorithms; and outlining a survey of available instruments to acquire the tomographic data. The obtained knowledge then yielded a process for optimizing the parameters of the mathematical model, thus allowing the model to be designed precisely, based on the measured data; such a precondition eventually reduced the uncertainty in reconstructing the specific conductivity distribution. When forming the numerical model, the author investigated the possibilities and overall impact of combining the open and closed domains with various regularization methods and mesh element scales, considering both the character of the conductivity reconstruction error and the computational intensity. A complementary task resolved within the broader scheme outlined above lay in parallelizing the reconstruction subalgorithms by using a multi-core graphics card. The results of the thesis are directly reflected in a reduced reconstruction uncertainty (through an optimization of the initial conductivity value, placement of the electrodes, and shape deformation of the domains) and accelerated computation via parallelized algorithms. The actual research benefited from an in-house designed automated tomography unit.

Non-Invasive Electrical Impedance Tomography for Multi-Scale Detection of Liver Fat Content

Introduction: Obesity is associated with an increased risk of nonalcoholic fatty liver disease (NAFLD). While Magnetic Resonance Imaging (MRI) is a non-invasive gold standard to detect fatty liver, we demonstrate a low-cost and portable electrical impedance tomography (EIT) approach with circumferential abdominal electrodes for liver conductivity measurements. Methods and Results: A finite element model (FEM) was established to simulate decremental liver conductivity in response to incremental liver lipid content. To validate the FEM simulation, we performed EIT imaging on an ex vivo porcine liver in a non-conductive tank with 32 circumferentially-embedded electrodes, demonstrating a high-resolution output given a priori information on location and geometry. To further examine EIT capacity in fatty liver detection, we performed EIT measurements in age- and gender-matched New Zealand White rabbits (3 on normal, 3 on high-fat diets). Liver conductivity values were significantly distinct following the high-fat diet (p = 0.003 vs. normal diet, n=3), accompanied by histopathological evidence of hepatic fat accumulation. We further assessed EIT imaging in human subjects with MRI quantification for fat volume fraction based on Dixon procedures, demonstrating average liver conductivity of 0.331 S/m for subjects with low Body-Mass Index (BMI 25 kg/m²). Conclusion: We provide both the theoretical and experimental framework for a multi-scale EIT strategy to detect liver lipid content. Our preliminary studies pave the way to enhance the spatial resolution of EIT as a marker for fatty liver disease and metabolic syndrome

Reconstruction Of Binary Electrical Conductivity Distributions Using Genetic Algorithms

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2010Elektriksel empedans görüntüleme, son yıllarda artan bir oranla kullanım alanı bulan girişimsel olmayan bir görüntüleme tekniğidir. Bu tekniğinin geniş uygulama alanlarında kabul görmesinin başlıca nedenleri yöntemin güvenliği, kendine özgü taşınabilirliği ve yeterince ucuz veri toplama donanımına bağımlı olmasıdır. Ancak, görüntü oluşturma problemi; ölçülen veri ve bilinmeyen iletkenlik parametreleri arasındaki doğrusal olmayan ilişki nedeniyle son derece kötü koşullu bir problemdir. Bu tezde, elektriksel empedans görüntüleme yöntemi kullanılarak iki boyutlu ve ikili iletkenlik dağılımlarının yeniden oluşturulmasını sağlayan iyileştirilmiş bir genetik algoritma geliştirilmiştir. Kullanılan elektriksel empedans görüntüleme yöntemi; ölçülen ve hesaplanan elektrot gerilim değerlerinin farklılıklarının en küçük kareler yaklaşımıyla minimizasyonuna dayanmaktadır. Hesaplanan elektrot gerilimleri iki boyutlu sonlu elemanlar modeli kullanılarak elde edilmiştir. İletkenlik dağılımının merkez bölgesindeki hassaslık sorununun çözümü olarak yeni bir ağırlık fonksiyonu geliştirildi. Görüntü oluşturma probleminin çözümü için geliştirilen genetik algoritma, her bir aşaması farklı hedeflere ve farklı genetik operatörlere sahip olmak üzere iki aşamadan oluşmaktadır. Tez çalışması kapsamında dört yeni mutasyon operatörü ve iyileştirilmiş sıra orantılı seçilim operatörü geliştirilmiştir. Genetik algoritmanın önemli parametreleri, popülasyonun çeşitliliğini verimli bir düzeyde korunmak için uyarlamalı olarak kontrol edildi. Genetik algoritmanın değişik şartlardaki başarımının gözlemlenmesi için denemeler gerçekleştirildi. Bu denemelerin büyük çoğunluğunda genetik algoritma tam iletkenlik dağılımına ulaşarak oldukça iyi bir performans gösterdi.Electrical impedance imaging is a noninvasive technique that has been increasingly used in recent years. The wide acceptance of this imaging technique is mainly due to its safety, unique portability, and its dependence on sufficiently inexpensive data acquisition hardware. However, the problem of image reconstruction is extremely ill conditioned due to the nonlinear relationship between the measured data and the unknown conductivity parameters. In this thesis, an improved genetic algorithm is developed for the reconstruction of two-dimensional and binary conductivity distributions in electrical impedance imaging method. The electrical impedance imaging method used in this thesis is based on the minimization of the discrepancies between measured and computed electrode voltages in a least-square sense. The computed electrode voltages are obtained from the model developed using the finite element method. To overcome the sensitivity problem near the center of conductivity distribution, a special weight function is introduced. The genetic algorithm for the image reconstruction problem consists of two stages, each with different objectives and different genetic operators. Four new mutation operators and an improved ranked proportionate selection operator are introduced in this thesis. Important parameters of the genetic algorithm are controlled adaptively to maintain the diversity of the population at an efficient level. A series of tests is conducted to observe the genetic algorithms performance on various conditions. The genetic algorithm performed well by reaching the exact conductivity distribution in most of the tests.Yüksek LisansM.Sc

Investigation of 3D electrical impedance mammography systems for breast cancer detection

Breast cancer is a major disease in women worldwide with a high rate of mortality, second only to lung cancer. Hence, there is considerable interest in developing non-invasive breast cancer detection methods with the aim of identifying breast cancer at an early stage, when it is most treatable. Electrical impedance mammography (EIM) is a relatively new medical imaging method for breast cancer detection. It is a safe, painless, non-invasive, non-ionizing imaging modality, which visualizes the internal conductivity distribution of the breast under investigation. Currently some EIM systems are in clinical trials but not commercialized, as there are still many challenges with sensitivity, spatial resolution and detectability. The research in this thesis aims to enhance and optimize EIM systems in order to address the current challenges. An enhanced image reconstruction algorithm using the duo-mesh method is developed. Both in simulations and real cases of phantoms and patients, the enhanced algorithm has proven more accurate and sensitive than the former algorithm and effective in improving vertical resolution for the EIM system with a planar electrode array. To evaluate the performance of the EIM system and the image reconstruction algorithms, an image processing based error analysis method is developed, which can provide an intuitive and accurate method to evaluate the reconstructed image and outline the shape of the object of interest. Two novel EIM systems are studied, which aim to improve the spatial resolution and the detectability of a tumour deep in the breast volume. These are: rotary planar-electrode-array EIM (RPEIM) system and combined electrode array EIM (CEIM) system. The RPEIM system permits the planar electrode array to rotate in the horizontal plane, which can dramatically increase the number of independent measurements, hence improving the spatial resolution. To support the rotation of the planner electrode array, a synchronous mesh method is developed. The CEIM system has a planar electrode array and a ring electrode array operated independently or together. It has three operational modes. This design provides enhanced detectability of a tumour deep within the tissue, as required for a large volume breast. The studies of the RPEIM system and the CEIM system are based on close-to-realistic digital breast phantoms, which comprise of skin, nipple, ducts, acini, fat and tumour. This approach makes simulations very close to a clinical trial of the technology

Swarm Intelligence and Evolutionary Techniques for Real World Applications

This work provides a comprehensive list of real-world applications of swarm intelligence and evolutionary computation technique

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI