Advanced regularization and discretization methods in diffuse optical tomography

Diffuse optical tomography (DOT) is an emerging technique that utilizes light in the near infrared spectral region (650−900nm) to measure the optical properties of physiological tissue. Comparing with other imaging modalities, DOT modality is non-invasive and non-ionising. Because of the relatively lower absorption of haemoglobin, water and lipid at the near infrared spectral region, the light is able to propagate several centimeters inside of the tissue without being absolutely absorbed. The transmitted near infrared light is then combined with the image reconstruction algorithm to recover the clinical relevant information inside of the tissue. Image reconstruction in DOT is a critical problem. The accuracy and precision of diffuse optical imaging rely on the accuracy of image reconstruction. Therefore, it is of great importance to design efficient and effective algorithms for image reconstruction. Image reconstruction has two processes. The process of modelling light propagation in tissues is called the forward problem. A large number of models can be used to predict light propagation within tissues, including stochastic, analytical and numerical models. The process of recovering optical parameters inside of the tissue using the transmitted measurements is called the inverse problem. In this thesis, a number of advanced regularization and discretization methods in diffuse optical tomography are proposed and evaluated on simulated and real experimental data in reconstruction accuracy and efficiency. In DOT, the number of measurements is significantly fewer than the number of optical parameters to be recovered. Therefore the inverse problem is an ill-posed problem which would suffer from the local minimum trap. Regularization methods are necessary to alleviate the ill-posedness and help to constrain the inverse problem to achieve a plausible solution. In order to alleviate the over-smoothing effect of the popular used Tikhonov regularization, L1-norm regularization based nonlinear DOT reconstruction for spectrally constrained diffuse optical tomography is proposed. This proposed regularization can reduce crosstalk between chromophores and scatter parameters and maintain image contrast by inducing sparsity. This work investigates multiple algorithms to find the most computational efficient one for solving the proposed regularization methods. In order to recover non-sparse images where multiple activations or complex injuries happen in the brain, a more general total variation regularization is introduced. The proposed total variation is shown to be able to alleviate the over-smoothing effect of Tikhonov regularization and localize the anomaly by inducing sparsity of the gradient of the solution. A new numerical method called graph-based numerical method is introduced to model unstructured geometries of DOT objects. The new numerical method (discretization method) is compared with the widely used finite element-based (FEM) numerical method and it turns out that the graph-based numerical method is more stable and robust to changes in mesh resolution. With the advantages discovered on the graph-based numerical method, graph-based numerical method is further applied to model the light propagation inside of the tissue. In this work, two measurement systems are considered: continuous wave (CW) and frequency domain (FD). New formulations of the forward model for CW/FD DOT are proposed and the concepts of differential operators are defined under the nonlocal vector calculus. Extensive numerical experiments on simulated and realistic experimental data validated that the proposed forward models are able to accurately model the light propagation in the medium and are quantitatively comparable with both analytical and FEM forward models. In addition, it is more computational efficient and allows identical implementation for geometries in any dimension

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

The Retinal Microvasculature in Secondary Progressive Multiple Sclerosis

In light of new data regarding pathology of multiple sclerosis (MS), more research is needed into the vascular aspects of the disease. Demyelination caused by inflammation is historically thought of as the main cause of disability in the disease. Recent studies, however, have suggested that MS is in fact a spectrum of overlapping phenotypes consisting of inflammation, oxidative damage and hypoperfusion. The microvasculature plays an important role in all of these pathogenic processes and its dysfunction may therefore be of crucial importance to the development and progression of the disease. This thesis focuses on investigating the microvasculature of the retina as a surrogate for the brain by assessing the vascular structure, blood flow dynamics and oxygen transfer of the retinal blood vessels in secondary progressive multiple sclerosis (SPMS). Studying the retinal microvasculature using a multimodal imaging approach has allowed us to develop a more detailed understanding of blood flow in MS and to identify new imaging markers for trials into neuroprotective drugs in MS. The work done in this thesis demonstrated; i) a higher rate of retinal microvascular abnormalities in MS which progresses with disease severity, ii) evidence of retinal vascular remodelling in SPMS and iii) changes in blood velocity and flow in the retina in SPMS. These observations pave the way for future investigations into the mechanisms of vascular alterations and vascular dysfunction in MS, and provide a set of imaging markers to further explore other cerebrovascular diseases through the retina

Influence of Early Bilingual Exposure in the Developing Human Brain.

190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion

Information theoretic regularization in diffuse optical tomography

Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution. A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process. In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process. We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration