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Register a new userHigh resolution transcranial imaging based on the optoacoustic memory effect
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Acoustic impedance mismatches between soft tissues and bones are known to result in strong aberrations in optoacoustic and ultrasound images. Of particular importance are the severe distortions introduced by the human skull, impeding transcranial brain imaging with these modalities. While modelling of ultrasound propagation through the skull may in principle help correcting for some of the skull-induced aberrations, these approaches are commonly challenged by the highly heterogeneous and dispersive acoustic properties of the skull and lack of exact knowledge on its geometry and internal structure. Here we demonstrate that the spatio-temporal properties of the acoustic distortions induced by the skull are preserved for signal sources generated at neighboring intracranial locations by means of optoacoustic excitation. This optoacoustic memory effect is exploited for building a three-dimensional model accurately describing the generation, propagation and detection of time-resolved broadband optoacoustic waveforms traversing the skull. The memory-based model-based inversion is then shown to accurately recover the optical absorption distribution inside the skull with spatial resolution and image quality comparable to those attained in skull-free medium.
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Label-free imaging approaches seek to simplify and augment histopathologic assessment by replacing the current practice of staining by dyes to visualize tissue morphology with quantitative optical measurements. Quantitative phase imaging (QPI) operates with visible/UV light and thus provides a resolution matched to current practice. Here we introduce and demonstrate confocal QPI for label-free imaging of tissue sections and assess its utility for manual histopathologic inspection. Imaging cancerous and normal adjacent human breast and prostate, we show that tissue structural organization can be resolved with high spatial detail comparable to conventional H&E stains. Our confocal QPI images are found to be free of halo, solving this common problem in QPI. We further describe and apply a virtual imaging system based on Finite-Difference Time-Domain (FDTD) calculations to quantitatively compare confocal with wide-field QPI methods and explore performance limits using numerical tissue phantoms.
Acoustic-resolution optoacoustic microscopy (AR-OAM) retrieves anatomical and functional contrast from living tissues at depths not reachable with optical microscopy. The imaging performance of AR-OAM has been advanced with image reconstruction algorithms providing high lateral resolution ultimately limited by acoustic diffraction. In this work, we suggest a new model-based framework efficiently exploiting scanning symmetries for high-resolution reconstruction of AR-OAM images. The model accurately accounts for the spatial impulse response and large detection bandwidth of a spherical polyvinylidene difluoride sensor, which facilitates significantly outperforming synthetic aperture focusing technique commonly employed in AR-OAM image reconstruction in terms of image contrast and resolution. Furthermore, reconstructions based on L1-norm regularization enabled resolving structures indistinguishable with other methods, which was confirmed by numerical simulations as well as phantom and in vivo experiments. The achieved performance demonstrates the applicability of AR-OAM as a super-resolution imaging method capable of breaking through the limits imposed by acoustic diffraction, thus opening unprecedented capabilities for the microscopic interrogation of optically opaque tissues in preclinical and clinical studies.
Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR), which results in unsatisfactory image spatial resolution. In this work, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated using total-body dynamic PET image data and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging contrast. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a novel method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.
Localization-based imaging has revolutionized fluorescence optical microscopy and has also enabled unprecedented ultrasound images of microvascular structures in deep tissues. Herein, we introduce a new concept of localization optoacoustic tomography (LOAT) that employs rapid sequential acquisition of three-dimensional optoacoustic images from flowing absorbing particles. We show that the new method enables breaking through the spatial resolution barrier of acoustic diffraction while further enhancing the visibility of structures under limited-view tomographic conditions. Given the intrinsic sensitivity of optoacoustics to multiple hemodynamic and oxygenation parameters, LOAT may enable new level of performance in studying functional and anatomical alterations of microcirculation.
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