Volumetric Ultrasound via 3D Null Subtraction Imaging with Circular and Spiral Apertures

Volumetric ultrasound imaging faces a fundamental trade-off among image quality, frame rate, and hardware complexity. This study introduces three-dimensional Null Subtraction Imaging (3D NSI), a nonlinear beamforming framework that addresses this trade-off by combining computationally efficient null-subtraction process with multiplexing-aware sparse aperture designs on matrix arrays. We evaluate three apodization configurations: a fully addressed circular aperture and two Fermat's spiral sparse apertures. To overcome channel-sharing constraints common in matrix arrays multiplexed with low-channel-count ultrasound systems, we propose a spiral "no-reuse" apodization that enforces non-overlapping element sets across transmit-receive events. This design resolves multiplexing conflicts and enables up to a 16-fold increase in acquisition volume rate using only 240 active elements on a 1024-element probe. In computer simulations and tissue-mimicking phantom experiments, 3D NSI achieved an average improvement of 36% in azimuthal and elevational resolutions, along with an approximately 20% higher contrast ratio, compared to the conventional Delay-and-Sum (DAS) beamformer under matched transmit/receive configurations. When implemented with the spiral no-reuse aperture, the 3D NSI framework achieved over 1000 volumes per second with a computational load less than three times that of DAS, making it a practical solution for real-time 4D imaging.

Denoising ECG by Adaptive Filter with Empirical Mode Decomposition

Electrocardiogram (ECG) signal is an important physiological signal which contains cardiac information and is the basis to diagnosis cardiac related diseases. In this paper, several innovative and efficient methods based on adaptive filter and empirical mode decomposition (EMD) to denoise ECG signal contaminated by various kinds of noise, including baseline wander (BW), power line interference (PLI), electrode motion artifact (EM) and muscle artifact (MA), are proposed. We first present a novel method based on EMD and adaptive filter for the removal of BW and PLI in ECG signal. We then extend the method to the complex scenario where four most common noises, PLI, BW, EM and MA are present. The proposed Parallel EMD adaptive filter structure yields the best SNR improvement on the MIT-BIH arrhythmia database, corrupted by the four types of noises.