Learning Tissue Geometries for Photoacoustic Image Analysis

Photoacoustic imaging (PAI) holds great promise as a novel, non-ionizing imaging modality, allowing insight into both morphological and physiological tissue properties, which are of particular importance in the diagnostics and therapy of various diseases, such as cancer and cardiovascular diseases. However, the estimation of physiological tissue properties with PAI requires the solution of two inverse problems, one of which, in particular, presents challenges in the form of inherent high dimensionality, potential ill-posedness, and non-linearity. Deep learning (DL) approaches show great potential to address these challenges but typically rely on simulated training data providing ground truth labels, as there are no gold standard methods to infer physiological properties in vivo. The current domain gap between simulated and real photoacoustic (PA) images results in poor in vivo performance and a lack of reliability of models trained with simulated data. Consequently, the estimates of these models occasionally fail to match clinical expectations. The work conducted within the scope of this thesis aimed to improve the applicability of DL approaches to PAI-based tissue parameter estimation by systematically exploring novel data-driven methods to enhance the realism of PA simulations (learning-to-simulate). This thesis is part of a larger research effort, where different factors contributing to PA image formation are disentangled and individually approached with data-driven methods. The specific research focus was placed on generating tissue geometries covering a variety of different tissue types and morphologies, which represent a key component in most PA simulation approaches. Based on in vivo PA measurements (N = 288) obtained in a healthy volunteer study, three data-driven methods were investigated leveraging (1) semantic segmentation, (2) Generative Adversarial Networks (GANs), and (3) scene graphs that encode prior knowledge about the general tissue composition of an image, respectively. The feasibility of all three approaches was successfully demonstrated. First, as a basis for the more advanced approaches, it was shown that tissue geometries can be automatically extracted from PA images through the use of semantic segmentation with two types of discriminative networks and supervised training with manual reference annotations. While this method may replace manual annotation in the future, it does not allow the generation of any number of tissue geometries. In contrast, the GAN-based approach constitutes a generative model that allows the generation of new tissue geometries that closely follow the training data distribution. The plausibility of the generated geometries was successfully demonstrated in a comparative assessment of the performance of a downstream quantification task. A generative model based on scene graphs was developed to gain a deeper understanding of important underlying geometric quantities. Unlike the GAN-based approach, it incorporates prior knowledge about the hierarchical composition of the modeled scene. However, it allowed the generation of plausible tissue geometries and, in parallel, the explicit matching of the distributions of the generated and the target geometric quantities. The training was performed either in analogy to the GAN approach, with target reference annotations, or directly with target PA images, circumventing the need for annotations. While this approach has so far been exclusively conducted in silico, its inherent versatility presents a compelling prospect for the generation of tissue geometries with in vivo reference PA images. In summary, each of the three approaches for generating tissue geometry exhibits distinct strengths and limitations, making their suitability contingent upon the specific application at hand. By opening a new research direction in the form of learning-to-simulate approaches and significantly improving the realistic modeling of tissue geometries and, thus, ultimately, PA simulations, this work lays a crucial foundation for the future use of DL-based quantitative PAI in the clinical setting

Overcoming conventional modeling limitations using image- driven lattice-boltzmann method simulations for biophysical applications

The challenges involved in modeling biological systems are significant and push the boundaries of conventional modeling. This is because biological systems are distinctly complex, and their emergent properties are results of the interplay of numerous components/processes. Unfortunately, conventional modeling approaches are often limited by their inability to capture all these complexities. By using in vivo data derived from biomedical imaging, image-based modeling is able to overcome this limitation. In this work, a combination of imaging data with the Lattice-Boltzmann Method for computational fluid dynamics (CFD) is applied to tissue engineering and thrombogenesis. Using this approach, some of the unanswered questions in both application areas are resolved. In the first application, numerical differences between two types of boundary conditions: “wall boundary condition” (WBC) and “periodic boundary condition” (PBC), which are commonly utilized for approximating shear stresses in tissue engineering scaffold simulations is investigated. Surface stresses in 3D scaffold reconstructions, obtained from high resolution microcomputed tomography images are calculated for both boundary condition types and compared with the actual whole scaffold values via image-based CFD simulations. It is found that, both boundary conditions follow the same spatial surface stress patterns as the whole scaffold simulations. However, they under-predict the absolute stress values approximately by a factor of two. Moreover, it is found that the error grows with higher scaffold porosity. Additionally, it is found that the PBC always resulted in a lower error than the WBC. In a second tissue engineering study, the dependence of culture time on the distribution and magnitude of fluid shear in tissue scaffolds cultured under flow perfusion is investigated. In the study, constructs are destructively evaluated with assays for cellularity and calcium deposition, imaged using µCT and reconstructed for CFD simulations. It is found that both the shear stress distributions within scaffolds consistently increase with culture time and correlate with increasing levels of mineralized tissues within the scaffold constructs as seen in calcium deposition data and µCT reconstructions. In the thrombogenesis application, detailed analysis of time lapse microscopy images showing yielding of thrombi in live mouse microvasculature is performed. Using these images, image-based CFD modeling is performed to calculate the fluid-induced shear stresses imposed on the thrombi’s surfaces by the surrounding blood flow. From the results, estimates of the yield stress (A critical parameter for quantifying the extent to which thrombi material can resist deformation and breakage) are obtained for different blood vessels. Further, it is shown that the yielding observed in thrombi occurs mostly in the outer shell region while the inner core remains intact. This suggests that the core material is different from the shell. To that end, we propose an alternative mechanism of thrombogenesis which could help explain this difference. Overall, the findings from this work reveal that image-based modeling is a versatile approach which can be applied to different biomedical application areas while overcoming the difficulties associated with conventional modeling

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

Characterisation and phononic image reconstruction of gold nanorods

The optical and mechanical properties of metal nanoparticles depend mostly on their sizes and shapes. However, a non-destructive size characterisation technique for metal nanoparticles which can work in any external environment with high precision is currently unavailable. It is well known that optical microscopy is diffraction limited which means that two close objects are not resolvable because of the bending of light waves while passing through small apertures. The resolution of an optical system is limited to a few hundred nanometres for visible wavelengths, even using the highest available numerical aperture (1.4). This PhD research focused on both these problems: the unavailability of a precise and non-destructive size characterisation technique for metal nanoparticles applicable for all environments and the resolution limit of optical microscopy. In this PhD work, size, orientation, and location of gold nanorods were determined to reconstruct their acoustic or phononic images in both air and water media by using time-resolved pump-probe picosecond ultrasonics. Metal nanoparticles are of sub-optical dimensions and a large number of them can be placed inside the same optical point spread function. Using acoustic frequencies and orientation differences of gold nanorods, it was possible to separate and image them, even inside the same optical point spread function. The approach involved the exploitation of gold nanorods as optoacoustic transducers when they were excited by focused circularly polarised femtosecond optical pump pulses. They became hot and vibrated due to the created stress, producing acoustic waves in the GHz range. Circularly polarised focused probe pulses were transmitted through them, and the modulation of the scattered probe light intensity caused by the vibration was used to determine their vibrational frequencies. These vibrational frequencies were then converted into sizes by using an established analytical model developed by Hu et al. (2003). A polarisation-controlled detection system was developed to change the polarisation of the probe light to determine the orientation of the rod. The nanorods were scanned spatially during the experiments and later an amplitude map of the scanned area at the frequency of the rod was extracted. Then, the centroid algorithm was applied to find the location of the rod. Experimental parameters such as pump and probe wavelengths were estimated by simulating the optical and mechanical responses of gold nanorods. A non-commercial Matlab code package SMARTIES and a commercial finite element model tool COMSOL Multiphysics were used to simulate the optical response of gold nanorods in specific external environments. The mechanical responses of gold nanorods were simulated by using an established analytical model developed by Hu et al. (2003) and finite element models designed by using COMSOL Multiphysics. In this thesis, it was found that the characterised sizes of gold nanorods in both air and water media were close to the sizes measured from the scanning electron microscopy (SEM) images. Obtained worst-case length and width precisions were approximately 1 nm and 0.3 nm compared to SEM measurements, respectively. The size characterisation results presented in this thesis showed that the worst case SEM precision was ±36 nm. However, the SEM measurement was a function of human error because measurements were done manually from the pixels of the SEM images. In addition, the precision of SEM measurements also depends on the setting of the machine, noise, magnification, contrast, and aberration, among others. The reconstructed acoustic images of gold nanorods also matched reasonably well with the SEM images. Obtained minimum location and angle precisions were 2 nm and 0.4°, respectively. The result presented in this thesis showed that the minimum angle precision was ±7° from SEM. The results showed that the proposed technique was in good agreement with the SEM. The proposed technique can work with high precision in any external medium without requiring a vacuum environment and conductive surface. Its applicability to the water environment also suggests that the technique can be used in bio-environments. Hence, the technique is simple, non-destructive and ideal for imaging living specimens. The motivation behind this PhD research was to help make progress towards a phonon-based super-resolution imaging technique in biology. Although imaging biological nanostructures using gold nanorods was out of the scope of this PhD work, the achievements of the present work are significant steps towards offering a phonon based super-resolution imaging technique that can image biological nanostructures

Acoustic Waves

The concept of acoustic wave is a pervasive one, which emerges in any type of medium, from solids to plasmas, at length and time scales ranging from sub-micrometric layers in microdevices to seismic waves in the Sun's interior. This book presents several aspects of the active research ongoing in this field. Theoretical efforts are leading to a deeper understanding of phenomena, also in complicated environments like the solar surface boundary. Acoustic waves are a flexible probe to investigate the properties of very different systems, from thin inorganic layers to ripening cheese to biological systems. Acoustic waves are also a tool to manipulate matter, from the delicate evaporation of biomolecules to be analysed, to the phase transitions induced by intense shock waves. And a whole class of widespread microdevices, including filters and sensors, is based on the behaviour of acoustic waves propagating in thin layers. The search for better performances is driving to new materials for these devices, and to more refined tools for their analysis