6 research outputs found
DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
Multispectral optoacoustic tomography (MSOT) is a high-resolution functional
imaging modality that can non-invasively access a broad range of
pathophysiological phenomena by quantifying the contrast of endogenous
chromophores in tissue. Real-time imaging is imperative to translate MSOT into
clinical imaging, visualize dynamic pathophysiological changes associated with
disease progression, and enable in situ diagnoses. Model-based reconstruction
affords state-of-the-art optoacoustic images; however, the image quality
provided by model-based reconstruction remains inaccessible during real-time
imaging because the algorithm is iterative and computationally demanding. Deep
learning affords faster reconstruction, but the lack of ground truth training
data can lead to reduced image quality for in vivo data. We introduce a
framework, termed DeepMB, that achieves accurate optoacoustic image
reconstruction for arbitrary input data in 31 ms per image by expressing
model-based reconstruction with a deep neural network. DeepMB facilitates
accurate generalization to experimental test data through training on signals
synthesized from real-world images and ground truth images generated by
model-based reconstruction. The framework affords in-focus images for a broad
range of anatomical locations because it supports dynamic adjustment of the
reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible
with the data rates and image sizes of modern multispectral optoacoustic
tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images
and demonstrate that the framework reconstructs images 1000 times faster than
the iterative model-based reference method while affording near-identical image
qualities. Accurate and real-time image reconstructions with DeepMB can enable
full access to the high-resolution and multispectral contrast of handheld
optoacoustic tomography
Multi-Component Imaging of the Fermi Gamma-ray Sky in the Spatio-spectral Domain
We perform two distinct spatio-spectral reconstructions of the gamma-ray sky
in the range of 0.56-316 GeV based on Fermi Large Area Telescope (LAT) data.
Both describe the sky brightness to be composed of a diffuse-emission and a
point-source component. The first model requires minimal assumptions and
provides a template-free reconstruction as a reference. It makes use of spatial
and spectral correlations to distinguish between the different components. The
second model is physics-informed and further differentiates between diffuse
emission of hadronic and leptonic origin. For this, we assume parametric, but
spatially varying energy spectra to distinguish between the processes and use
thermal Galactic dust observations to indicate the preferred sites of hadronic
interactions. To account for instrumental effects we model the point-spread,
the energy dispersion, and the exposure of the telescope throughout the
observation. The reconstruction problem is formulated as a Bayesian inference
task, that is solved by variational inference. We show decompositions of the
Gamma-ray flux into diffuse and point-like emissions, and of the diffuse
emissions into multiple physically motivated components. The diffuse
decomposition provides an unprecedented view of the Galactic leptonic diffuse
emission. It shows the Fermi bubbles and their spectral variations in high
fidelity and other areas exhibiting strong cosmic ray electron contents, such
as a thick disk in the inner Galaxy and outflow regions. Furthermore, we report
a hard spectrum gamma ray arc in the northern outer bubble co-spatial with the
reported X-ray arc by the eROSITA collaboration. All our spatio-spectral sky
reconstructions and their uncertainty quantification are publicly available
Towards in vivo characterization of thyroid nodules suspicious for malignancy using multispectral optoacoustic tomography
Purpose: Patient-tailored management of thyroid nodules requires improved risk of malignancy stratification by accurate preoperative nodule assessment, aiming to personalize decisions concerning diagnostics and treatment. Here, we perform an exploratory pilot study to identify possible patterns on multispectral optoacoustic tomography (MSOT) for thyroid malignancy stratification. For the first time, we directly correlate MSOT images with histopathology data on a detailed level. Methods: We use recently enhanced data processing and image reconstruction methods for MSOT to provide next-level image quality by means of improved spatial resolution and spectral contrast. We examine optoacoustic features in thyroid nodules associated with vascular patterns and correlate these directly with reference histopathology. Results: Our methods show the ability to resolve blood vessels with diameters of 250 μm at depths of up to 2 cm. The vessel diameters derived on MSOT showed an excellent correlation (R2-score of 0.9426) with the vessel diameters on histopathology. Subsequently, we identify features of malignancy observable in MSOT, such as intranodular microvascularity and extrathyroidal extension verified by histopathology. Despite these promising features in selected patients, we could not determine statistically relevant differences between benign and malignant thyroid nodules based on mean oxygen saturation in thyroid nodules. Thus, we illustrate general imaging artifacts of the whole field of optoacoustic imaging that reduce image fidelity and distort spectral contrast, which impedes quantification of chromophore presence based on mean concentrations. Conclusion: We recommend examining optoacoustic features in addition to chromophore quantification to rank malignancy risk. We present optoacoustic images of thyroid nodules with the highest spatial resolution and spectral contrast to date, directly correlated to histopathology, pushing the clinical translation of MSOT.</p
Three-dimensional optoacoustic imaging of nailfold capillaries in systemic sclerosis and its potential for disease differentiation using deep learning
From Springer Nature via Jisc Publications RouterHistory: received 2020-05-07, accepted 2020-09-09, registration 2020-09-16, pub-electronic 2020-10-05, online 2020-10-05, collection 2020-12Publication status: PublishedFunder: Helmholtz Association; doi: http://dx.doi.org/10.13039/501100009318; Grant(s): i3 (ExNet-0022-Phase2-3)Funder: University of Manchester; doi: http://dx.doi.org/10.13039/501100000770; Grant(s): MC_PC_16053Funder: Manchester Biomedical Research Centre; doi: http://dx.doi.org/10.13039/100014653Funder: H2020 European Research Council; doi: http://dx.doi.org/10.13039/100010663; Grant(s): No 694968 (PREMSOT)Funder: Arthritis Research UK,United Kingdom; Grant(s): 19465Abstract: The autoimmune disease systemic sclerosis (SSc) causes microvascular changes that can be easily observed cutaneously at the finger nailfold. Optoacoustic imaging (OAI), a combination of optical and ultrasound imaging, specifically raster-scanning optoacoustic mesoscopy (RSOM), offers a non-invasive high-resolution 3D visualization of capillaries allowing for a better view of microvascular changes and an extraction of volumetric measures. In this study, nailfold capillaries of patients with SSc and healthy controls are imaged and compared with each other for the first time using OAI. The nailfolds of 23 patients with SSc and 19 controls were imaged using RSOM. The acquired images were qualitatively compared to images from state-of-the-art imaging tools for SSc, dermoscopy and high magnification capillaroscopy. The vascular volume in the nailfold capillaries were computed from the RSOM images. The vascular volumes differ significantly between both cohorts (0.216 ± 0.085 mm3 and 0.337 ± 0.110 mm3; p < 0.0005). In addition, an artificial neural network was trained to automatically differentiate nailfold images from both cohorts to further assess whether OAI is sensitive enough to visualize anatomical differences in the capillaries between the two cohorts. Using transfer learning, the model classifies images with an area under the ROC curve of 0.897, and a sensitivity of 0.783 and specificity of 0.895. In conclusion, this study demonstrates the capabilities of RSOM as an imaging tool for SSc and establishes it as a modality that facilitates more in-depth studies into the disease mechanisms and progression