Acoustic meta-stethoscope for cardiac auscultation

Straight cylindrical stethoscopes serve as an important alternative to conventional stethoscopes whose application in the treatment of infectious diseases might be limited by the use of protective clothing. Yet their miniaturization is challenging due to the low-frequency of bioacoustics signal. Here, we design and experimentally implement a meta-stethoscope with subwavelength size, simple fabrication, easy assembly yet high sensitivity, which simply comprises multiple round perforated plate units and a cylindrical shell. We elucidate our proposed mechanism by analytically deriving the frequency response equation, which proves that the equivalent acoustic propagation path is substantially increased by the high-index metamaterial, enabling downscaling of the meta-stethoscope to subwavelength footprint. The acoustic performance of meta-stethoscope is experimentally characterized by monitoring the cardiac auscultation on clothed human body. The simulated and measured results agree well, with both showing the expected enhancement of sensitivity of our proposed meta-stethoscope (~ 10 dB) within the predicted working frequency range from 80 to 130 Hz despite its compactness and simplicity. Our designed portable, detachable yet effective meta-stethoscope opens a route to metamaterial-enabled stethoscope paradigm, with potential applications in diverse scenarios such as medical diagnosis and acoustic sensing.Comment: 14 pages, 3 figure

Deep unfolding as iterative regularization for imaging inverse problems

Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems. Unlike general end-to-end DNNs, unfolding methods have better interpretability and performance. However, to our knowledge, their accuracy and stability in solving inverse problems cannot be fully guaranteed. To bridge this gap, we modified the training procedure and proved that the unfolding method is an iterative regularization method. More precisely, we jointly learn a convex penalty function adversarially by an input-convex neural network (ICNN) to characterize the distance to a real data manifold and train a DNN unfolded from the proximal gradient descent algorithm with this learned penalty. Suppose the real data manifold intersects the inverse problem solutions with only the unique real solution. We prove that the unfolded DNN will converge to it stably. Furthermore, we demonstrate with an example of MRI reconstruction that the proposed method outperforms conventional unfolding methods and traditional regularization methods in terms of reconstruction quality, stability and convergence speed

Inpatient care burden due to cancers in Anhui, China: a cross-sectional household survey

Raw dataset of inpatient cancer care costs and related variables studied. (XLSX 32 kb