Ultrasound computed tomography (USCT) is an emerging medical imaging modality that holds great promise for improving human health. Full-waveform inversion (FWI)-based image reconstruction methods ...account for the relevant wave physics to produce high spatial resolution images of the acoustic properties of the breast tissues. A practical USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, and volumetric imaging is achieved by translating the ring-array orthogonally to the imaging plane. In commonly deployed slice-by-slice (SBS) reconstruction approaches, the 3-D volume is reconstructed by stacking together 2-D images reconstructed for each position of the ring-array. A limitation of the SBS reconstruction approach is that it does not account for 3-D wave propagation physics and the focusing properties of the transducers, which can result in significant image artifacts and inaccuracies. To perform 3-D image reconstruction when elevation-focused transducers are employed, a numerical description of the focusing properties of the transducers should be included in the forward model. To address this, a 3-D computational model of an elevation-focused transducer is developed to enable 3-D FWI-based reconstruction methods to be deployed in ring-array-based USCT. The focusing is achieved by applying a spatially varying temporal delay to the ultrasound pulse (emitter mode) and recorded signal (receiver mode). The proposed numerical transducer model is quantitatively validated and employed in computer simulation studies that demonstrate its use in image reconstruction for ring-array USCT.
In the classic formulation of photoacoustic tomography (PAT), two distinct descriptions of the imaging model have been employed for developing reconstruction algorithms. We demonstrate that the ...numerical and statistical properties of unweighted least-squares reconstruction algorithms associated with each imaging model are generally very different. Specifically, some PAT reconstruction algorithms, including many of the iterative algorithms previously explored, do not work directly with the raw measured pressure wavefields, but rather with an integrated data function that is obtained by temporally integrating the photoacoustic wavefield. The integration modifies the statistical distribution of the data, introducing statistical correlations among samples. This change is highly significant for iterative algorithms, many of which explicitly or implicitly seek to minimize a statistical cost function. In this work, we demonstrate that iterative reconstruction by least-squares minimization yields better resolution-noise tradeoffs when working with the raw pressure data than with the integrated data commonly employed. In addition, we demonstrate that the raw-data based approach is less sensitive to certain deterministic errors, such as dc offset errors.
Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced ...initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT of the brain is to compensate for aberrations and attenuation in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method can be employed that can model the heterogeneous elastic properties of the medium. In this study, an optimization-based image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward-adjoint operator pair based on a finite-difference time-domain discretization of the 3D elastic wave equation is utilized to compute penalized least squares estimates of the initial pressure distribution. Computer-simulation and experimental studies are conducted to investigate the robustness of the reconstruction method to model mismatch and its ability to effectively resolve cortical and superficial brain structures.
Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models-denoising diffusion probabilistic models ...(DDPMs)-demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To date, these claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity. However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as 'spatial context' in this work. To address this, a systematic assessment of the ability of DDPMs to learn spatial context relevant to medical imaging applications is reported for the first time. A key aspect of the studies is the use of stochastic context models (SCMs) to produce training data. In this way, the ability of the DDPMs to reliably reproduce spatial context can be quantitatively assessed by use of post-hoc image analyses. Error-rates in DDPM-generated ensembles are reported, and compared to those corresponding to other modern DGMs. The studies reveal new and important insights regarding the capacity of DDPMs to learn spatial context. Notably, the results demonstrate that DDPMs hold significant capacity for generating contextually correct images that are 'interpolated' between training samples, which may benefit data-augmentation tasks in ways that GANs cannot.
The effects of different amounts and frequencies of stunning sine wave alternating current were investigated under field conditions. Seven hundred and fifty broilers were stunned in an electrical ...water bath with an average root mean square (RMS) current of 150, 200, and 250 mA and frequencies of 200, 400, 600, 800, and 1,200 Hz. The occurrence of corneal reflex, spontaneous eye blinking, and a positive response to a painful stimulus were monitored and recorded immediately after the stunning and at 20 s post-stun. Statistical analysis showed that the electrical stunning frequency (P = 0.0004), the stunning RMS current (P < 0.0001) and the interaction between stunning frequency and stunning current (P < 0.0001) had a significant effect on the occurrence of animals experiencing an abolition of corneal reflex at 20 s post-stun.
At a current of 150 mA, the probability of a successful stun was over 90% at 200 Hz, approximately 40% at 400 Hz, and below 5% for frequencies greater than 600 Hz. So, stunning at frequencies greater than 600 Hz cannot be recommended when a RMS current of 150 mA is applied. The maximum probability of a successful stun was obtained for a current level of 200 mA at 400 Hz and for a current level of 250 mA at 400 and 600 Hz, whereas the stunning treatments at 1,200 Hz provided the lowest probability of a successful stun. Assessment of spontaneous eye blinking and responses to comb pinching confirmed the indications coming from the analysis of corneal reflex.
•GRK2 expression is dependent on NFκB transcriptional activity in inflammation.•LPS induces GRK2 accumulation into mitochondria in a time-dependent fashion.•βARK-ct accumulates GRK2 into mitochondria ...to ameliorate mitochondrial functions.•Subcellular localization of GRK2 determines its function within the cell.•Regulation of protein subcellular localization is a potential therapeutic approach.
G-protein-coupled receptor kinase 2 (GRK2) levels are elevated in inflammation but its role is not clear yet. Here we show that GRK2 expression is dependent on NFκB transcriptional activity. In macrophages, LPS induces GRK2 accumulation in mitochondria increasing biogenesis. The overexpression of the carboxy-terminal domain of GRK2 (βARK-ct), known to displace GRK2 from plasma membranes, induces earlier localization of GRK2 to mitochondria in response to LPS leading to increased mt-DNA transcription and reduced ROS production and cytokine expression. Our study shows the relevance of GRK2 subcellular localization in macrophage biology and its potential therapeutic properties in inflammation.
Objective: To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction. Methods: We proposed a formulation that synergizes a ...low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of "contrast-weighted" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion. Results: We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. Conclusion: The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction. Significance: Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.
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