Robust Short-Lag Spatial Coherence Imaging

Abstract

Short-lag spatial coherence (SLSC) imaging displays the spatial coherence between backscattered ultrasound echoes instead of their signal amplitudes and is more robust to noise and clutter artifacts when compared with traditional delay-and-sum (DAS) B-mode imaging. However, SLSC imaging does not consider the content of images formed with different lags, and thus does not exploit the differences in tissue texture at each short-lag value. Our proposed method improves SLSC imaging by weighting the addition of lag values (i.e., M-weighting) and by applying robust principal component analysis (RPCA) to search for a low-dimensional subspace for projecting coherence images created with different lag values. The RPCA-based projections are considered to be denoised versions of the originals that are then weighted and added across lags to yield a final robust SLSC (R-SLSC) image. Our approach was tested on simulation, phantom, and in vivo liver data. Relative to DAS B-mode images, the mean contrast, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) improvements with R-SLSC images are 21.22 dB, 2.54, and 2.36, respectively, when averaged over simulated, phantom, and in vivo data and over all lags considered, which corresponds to mean improvements of 96.4%, 121.2%, and 120.5%, respectively. When compared with SLSC images, the corresponding mean improvements with R-SLSC images were 7.38 dB, 1.52, and 1.30, respectively (i.e., mean improvements of 14.5%, 50.5%, and 43.2%, respectively). Results show great promise for smoothing out the tissue texture of SLSC images and enhancing anechoic or hypoechoic target visibility at higher lag values, which could be useful in clinical tasks such as breast cyst visualization, liver vessel tracking, and obese patient imaging.

Combined Ultrasound and Photoacoustic Image Guidance of Spinal Pedicle Cannulation Demonstrated With Intact ex vivo Specimens

“…The main advantage of LW-SLSC relies on the adaptive selection of lower lags in kernels surrounding isoechoic regions, which enhances contrast, and higher lags otherwise, which enhances resolution. The selective combination of higher and lower lags is known to reduce the noise commonly observed in SLSC images created with higher lags [41]. The original formulation in (3) can be simplified using the framework of Barbero et al [42] for Total Variance alternatives.…”

“…The segmented masks from the DAS ultrasound image includes undesirable soft tissue and a single bony structure, while coherence methods identify at least 3 bony structures. Similarly, SLSC images created with greater M values have an increased number of outliers (i.e., pixels with coherence values that differ significantly from their surroundings and from their values at other lags [41]) and decreased SNR and CNR [39], which caused some otherwise continuous bony structures to appear disconnected, affecting the estimation of center of mass and resulting in redundant landmarks. This effect is mitigated with LW-SLSC.…”

GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions

“…Finally, the negative SLSC values were set to zero, 60 and the SLSC image was then normalized and log-compressed. The maximum term for normalization was computed using logarithmic reduction strategies.…”

CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming

“…While the current study focused on learning the coherence function only, DNN architectures like CohereNet could be extended to learn more complex operations found in other advanced beamforming algorithms, such as R-SLSC [20] or LW-SLSC [38], which may otherwise be challenging or not feasible to implement in parallel on a GPU. These additional applications will be the focus of future work.…”