In optoacoustic tomography, negatively focused transducers may be used for improving the tangential image resolution while preserving a high signal-to-noise ratio. Commonly, image reconstruction in such scenarios is facilitated by the use of the virtual-detector approach. Although the validity of this approach has been experimentally verified, it is based on an approximation whose effect on optoacoustic image reconstruction has not yet been studied. In this paper, we analyze the response of negatively focused acoustic detectors in 2D in both time and frequency domains. Based on this analysis, tradeoffs between the detector size, curvature, and sensitivity are formulated. In addition, our analysis reveals the geometrical underpinning of the virtual-detector approximation and quantifies its deviation from the exact solution. The error involved in the virtual-detector approximation is studied in image reconstruction simulations and its effect on image quality is shown. The theoretical tools developed in this work may be used in the design of new optoacoustic detection geometries as well as for improved image reconstruction.
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Sci Rep
January 2025
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.
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January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
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January 2025
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall.
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January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
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January 2025
Electrical and Computer Engineering, University of Massachusetts Lowell, Ball Hall, 1 University Ave, Lowell, Massachusetts, 01854, UNITED STATES.
Objective: X-ray photon-counting detectors (PCDs) have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality.
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