Impedance-optical Dual-modal Cell Culture Imaging with Learning-based Information Fusion

While Electrical Impedance Tomography (EIT) has found many biomedicine applications, a better resolution is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper proposes an impedance-optical dual-modal imaging framework, which is mainly aimed at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could significantly improve image quality, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously

FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging

Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.Comment: 11 pages

Advanced digital electrical impedance tomography system for biomedical imaging

Electrical Impedance Tomography (EIT) images the spatial conductivity distribution in an electrode-bounded sensing domain by non-intrusively generating an electric field and measuring the induced boundary voltage. Since its emergence, it has attracted ample interest in the field of biomedical imaging owing to its fast, cost efficient, label-free and non-intrusive sensing ability. Well-investigated biomedical applications of the EIT include lung ventilation monitoring, breast cancer imaging, and brain function imaging. This thesis probes an emerging biomedical application of EIT in three dimensional (3D) cell culture imaging to study non-destructively the biological behaviour of a 3D cell culture system, on which occasion real-time qualitative and quantitative imaging are becoming increasingly desirable. Focused on this topic, the contribution of the thesis can be summarised from the perspectives of biomedical-designed EIT system, fast and effective image reconstruction algorithms, miniature EIT sensors and experimental studies on cell imaging and cell-drug response monitoring, as follows. First of all, in order to facilitate fast, broadband and real-time 3D conductivity imaging for biomedical applications, the design and evaluation of a novel multi-frequency EIT (mfEIT) system was presented. The system integrated 32 electrode interfaces and its working frequency ranged from 10 kHz to 1 MHz. Novel features of the system included: a) a fully adjustable multi-frequency current source with current monitoring function was designed; b) a flexible switching scheme together with a semi-parallel data acquisition architecture was developed for high-frame-rate data acquisition; c) multi-frequency simultaneous digital quadrature demodulation was accomplished, and d) a 3D imaging software, i.e. Visual Tomography, was developed to perform real-time two dimensional (2D) and 3D image reconstruction, visualisation and analysis. The mfEIT system was systematically tested and evaluated on the basis of the Signal to Noise Ratio (SNR), frame rate, and 2D and 3D multi-frequency phantom imaging. The highest SNR achieved by the system was 82.82 dB on a 16-electrode EIT sensor. The frame rate was up to 546 frames per second (fps) at serial mode and 1014 fps at semi-parallel mode. The evaluation results indicate that the presented mfEIT system is a powerful tool for real-time 2D and 3D biomedical imaging. The quality of tomographic images is of great significance for performing qualitative or quantitative analysis in biomedical applications. To realise high quality conductivity imaging, two novel image reconstruction algorithms using adaptive group sparsity constraint were proposed. The proposed algorithms considered both the underlying structure of the conductivity distribution and sparsity priors in order to reduce the degree of freedom and pursue solutions with the group sparsity structure. The global characteristic of inclusion boundaries was studied as well by imposing the total variation constraint on the whole image. In addition, two adaptive pixel grouping methods were also presented to extract the structure information without requiring any a priori knowledge. The proposed algorithms were evaluated comparatively through numerical simulation and phantom experiments. Compared with the state-of-the-art algorithms such as l1 regularisation, the proposed algorithms demonstrated superior spatial resolution and preferable noise reduction performance in the reconstructed images. These features were demanded urgently in biomedical imaging. Further, a planar miniature EIT sensor amenable to the standard 3D cell culture format was designed and a 3D forward model was developed for 3D imaging. A novel 3D-Laplacian and sparsity joint regularisation algorithm was proposed for enhanced 3D image reconstruction. Simulated phantoms with spheres located at different vertical and horizontal positions were imaged for 3D imaging performance evaluation. Image reconstructions of MCF-7 human breast cancer cell spheroids and triangular breast cancer cell pellets were carried out for experimental verification. The results confirmed that robust impedance measurement on the highly conductive cell culture medium was feasible and, greatly improved image quality was obtained by using the proposed regularisation method. Finally, a series of cancer cell spheroid imaging tests and real-time cell-drug response monitoring experiments by using the developed mfEIT system (Chapter 3), the designed miniature EIT sensors (Chapter 6) and the proposed image reconstruction algorithms (Chapter 4, 5 and 6) were carried out followed by comparative analysis. The stability of long-term impedance measurement on the highly conductive cell culture medium was verified firstly. Subsequently, by using the proposed algorithms in Chapter 4 and Chapter 5, high quality cancer cell spheroid imaging on a miniature sensor with 2D electrode configuration was achieved. Further, preliminary experiments on real-time monitoring of human breast cancer cell and anti-cancer drug response were performed and analysed. Promising results were obtained from these experiments. In summary, the work demonstrated in this thesis validated the feasibility of using the developed mfEIT system, the proposed image reconstruction algorithms, as well as the designed miniature EIT sensors to visualise 3D cell culture systems such as cell spheroids or artificial tissues and organs. The established work would expedite the real-time qualitative and quantitative imaging of 3D cell culture systems for the rapid assessment of cellular dynamics