IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Dec 2021
XDoppler: cross-correlation of orthogonal apertures for 3D blood flow imaging.
Row column addressing (RCA) transducers have the potential to provide volumetric imaging at ultrafast frame rate using a low channel count over a large field of view. In previous works we have shown that vascular imaging of large arteries as well as functional neuroimaging of the rat brain were feasible using RCA orthogonal plane wave imaging (OPW), but these applications required to transmit many plane waves, significantly reducing the frame rate. ⋯ Then, we demonstrate both in vitro and in vivo in the human carotid artery and in the rat brain that XDoppler provides a significant gain in contrast-to-noise ratio (CNR) (between 3 and 6 dB depending on the application) compared to OPW. This improvement also leads to a sensitivity increase in the rat brain as more blood vessels are detected by XDoppler imaging.
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IEEE Trans Med Imaging · Jan 2021
Regional Lung Perfusion Analysis in Experimental ARDS by Electrical Impedance and Computed Tomography.
Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. ⋯ Spatial perfusion was estimated based on first-pass indicator dilution for both electrical impedance and multidetector computed tomography and compared by Pearson correlation and Bland-Altman analysis. Strong correlation was found in dorsoventral (r = 0.92) and in right-to-left directions (r = 0.85) with good limits of agreement of 8.74% in eight lung segments. With a robust electrical impedance tomography perfusion estimation method, we found strong agreement between multidetector computed and electrical impedance tomography perfusion in healthy and regionally injured lungs and demonstrated feasibility of electrical impedance tomography perfusion imaging.
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IEEE Trans Med Imaging · Dec 2020
Tomographic Field Free Line Magnetic Particle Imaging With an Open-Sided Scanner Configuration.
Superparamagnetic iron oxide nanoparticles (SPIONs) have a high potential for use in clinical diagnostic and therapeutic applications. In vivo distribution of SPIONs can be imaged with the Magnetic Particle Imaging (MPI) method, which uses an inhomogeneous magnetic field with a field free region (FFR). The spatial distribution of the SPIONs are obtained by scanning the FFR inside the field of view (FOV) and sensing SPION related magnetic field disturbance. ⋯ We used a measurement based system matrix image reconstruction method that minimizes l1 -norm and total variation in the images. Furthermore, we present 2D imaging results of two 4 mm-diameter vessel phantoms with 0% and 75% stenosis. The experiments show high quality imaging results with a resolution down to 2.5 mm for a relatively low gradient field of 0.6 T/m.
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IEEE Trans Med Imaging · Aug 2020
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.
Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. ⋯ Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.
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IEEE Trans Med Imaging · Aug 2020
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). ⋯ In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.