Genetic Insights into Glycine's Protective Role Against CAD - European and East Asia, 2015 and 2020.

Introduction: The purpose of this study is to examine the potential causal relationship between levels of circulating glycine and coronary artery disease (CAD) using a two-step Mendelian randomization (MR) analysis. Methods: We analyzed data from genome-wide association studies (GWAS) conducted on European and East Asian populations. To assess the causal effects of circulating glycine levels on the risk of CAD. We used the inverse-variance weighting (IVW), weighted median (WM), MR-Egger, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods. Furthermore, we conducted mediation analysis to investigate the contribution of blood pressure and other cardiovascular disease-related traits. Results: The two-step Mendelian randomization analysis revealed that higher levels of glycine in the blood were associated with a reduced risk of CAD in Europeans [odds ratio ( OR)=0.84, 95% confidence interval ( CI): 0.72, -0.98; P=0.029] and East Asians: ( OR=0.76, 95% CI: 0.66, -0.89; P=3.57×10 -4). Sensitivity analysis confirmed the robustness of these findings. Additionally, our results suggest that about 6.06% of the observed causal effect is mediated through genetically predicted systolic blood pressure (SBP) in the European population. Discussion: Our results contribute to the current knowledge regarding the involvement of glycine in the progression of CAD, and provide valuable methodological insights for the prevention and treatment of this condition.

Feasibility of source-free DAS logging for next-generation borehole imaging.

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

Spatial Prediction Filtering for Medical Ultrasound in Aberration and Random Noise.

While medical ultrasound imaging has become one of the most widely used imaging modalities in clinics, it often suffers from suboptimal image quality, especially in technically difficult patients with a large amount of fat content that induces severe phase aberration effects and decreases the signal-to-noise ratio. Several researchers have proposed various techniques, which can be broadly categorized as either a phase aberration correction (PAC) technique or a coherence-based imaging technique, to address the challenges in imaging technically difficult patients. Although both families of techniques have shown some success in improving the image quality in the presence of a mild level of phase aberration and/or random noise, they often fail to achieve meaningful improvements in the image quality and, in some cases, even create severe image artifacts. In this paper, we employ an adaptive filtering technique called frequency-space prediction filtering (FXPF), which we recently introduced in ultrasound imaging, to overcome the weaknesses of existing techniques and achieve image quality improvements more effectively under varying levels of phase aberration and random noise. Using simulated and experimental phantom data with varying levels of phase aberration and random noise, we evaluate and compare the performance of FXPF with the most representative technique for each category: nearest-neighbor cross correlation (NNCC)-based PAC and the generalized coherence factor (GCF). Our simulation, experimental phantom, and in vivo results demonstrate that FXPF is highly robust in varying levels of phase aberration and noise, and always outperforms both NNCC-based PAC and GCF in terms of the contrast-to-noise ratio (CNR) and the contrast when both random noise and phase aberration are present.

Spatial Prediction Filtering of Acoustic Clutter and Random Noise in Medical Ultrasound Imaging.

One of the major challenges in array-based medical ultrasound imaging is the image quality degradation caused by sidelobes and off-axis clutter, which is an inherent limitation of the conventional delay-and-sum (DAS) beamforming operating on a finite aperture. Ultrasound image quality is further degraded in imaging applications involving strong tissue attenuation and/or low transmit power. In order to effectively suppress acoustic clutter from off-axis targets and random noise in a robust manner, we introduce in this paper a new adaptive filtering technique called frequency-space (F-X) prediction filtering or FXPF, which was first developed in seismic imaging for random noise attenuation. Seismologists developed FXPF based on the fact that linear and quasilinear events or wavefronts in the time-space (T-X) domain are manifested as a superposition of harmonics in the frequency-space (F-X) domain, which can be predicted using an auto-regressive (AR) model. We describe the FXPF technique as a spectral estimation or a direction-of-arrival problem, and explain why adaptation of this technique into medical ultrasound imaging is beneficial. We apply our new technique to simulated and tissue-mimicking phantom data. Our results demonstrate that FXPF achieves CNR improvements of 26% in simulated noise-free anechoic cyst, 109% in simulated anechoic cyst contaminated with random noise of 15 dB SNR, and 93% for experimental anechoic cyst from a custom-made tissue-mimicking phantom. Our findings suggest that FXPF is an effective technique to enhance ultrasound image contrast and has potential to improve the visualization of clinically important anatomical structures and diagnosis of diseased conditions.

TR-MUSIC inversion of the density and compressibility contrasts of point scatterers.

Time-reversal imaging with multiple signal classification (TR-MUSIC) is a super-resolution ultrasound imaging method for detecting point scatterers. This algorithm assumes that there is no contrast between the density of the point targets and that of the background medium, and that ultrasound scattering is caused only by the compressibility contrast. We modify the TR-MUSIC algorithm to account for ultrasound scattering from point targets with both density and compressibility contrasts. In addition, we develop an inversion method for estimating the density and compressibility contrasts of point scatterers with known locations. This approach is an extension of the inversion method previously developed by Devaney et al. for estimating the scattering strengths of point targets that have no density contrasts relative to the background medium. We use numerical phantom data to demonstrate that our new TR-MUSIC inversion algorithm can reliably estimate the density and compressibility contrasts of point scatterers. The estimates of these properties could be used for distinguishing breast calcifications from other tissue scatterers.

Super-resolution ultrasound imaging using a phase-coherent MUSIC method with compensation for the phase response of transducer elements.

Time-reversal with multiple signal classification (TR-MUSIC) is an imaging method for locating point-like targets beyond the classic resolution limit. In the presence of noise, however, the super-resolution capability of TR-MUSIC is diminished. Recently a new method, phase-coherent MUSIC (PC-MUSIC), was developed. This algorithm modifies TR-MUSIC to make use of phase information from multiple frequencies to reduce noise effects and preserve the super resolution. PC-MUSIC however, ignores the phase response of the transducer elements. In this paper, we account for the phase response of the transducer elements in the derivation of the PC-MUSIC algorithm. Unfortunately, the phase response of the transducer elements may not be known beforehand. We develop an experimental method to estimate this response using measured signals scattered from a glass microsphere embedded in a tissue-mimicking phantom with a homogeneous background medium of a known sound speed. We use numerical simulations to illustrate that the maximum resolution achieved with PC-MUSIC is limited by the transducer bandwidth and the signal-to-noise ratio. We perform experiments on tissue-mimicking phantoms and compare images obtained with different imaging modalities, including X-ray mammography, synthetic-aperture ultrasound imaging, TR-MUSIC, and PC-MUSIC. We demonstrate the significantly improved resolving power of PC-MUSIC.

Ultrasound time-reversal MUSIC imaging with diffraction and attenuation compensation.

Time-reversal imaging with multiple signal classification (TR-MUSIC) is an algorithm for imaging point-like scatterers embedded in a homogeneous and non-attenuative medium. We generalize this algorithm to account for the attenuation in the medium and the diffraction effects caused by the finite size of the transducer elements. The generalized algorithm yields higher-resolution images than those obtained with the original TR-MUSIC algorithm. We evaluate the axial and lateral resolutions of the images obtained with the generalized algorithm when noise corrupts the recorded signals and show that the axial resolution is degraded more than the lateral resolution. The TR-MUSIC algorithm is valid only when the number of point-like targets in the imaging plane is fewer than the number of transducer elements used to interrogate the medium. We remedy this shortcoming by dividing the imaging plane into subregions and applying the TR-MUSIC algorithm to the windowed backscattered signals corresponding to each subregion. The images of all subregions are then combined to form the total image. Imaging results of numerical and phantom data show that when the number of scatterers within each subregion is much smaller than the number of transducer elements, the windowing method yields super-resolution images with accurate scatterer localization. We use computer simulations and tissue-mimicking phantom data acquired with a real-time synthetic-aperture ultrasound system to illustrate the algorithms presented in the paper.

Ultrasound time-reversal MUSIC imaging of extended targets.

Ultrasound time-reversal imaging with multiple signal classification (TR-MUSIC) can produce images with subwavelength spatial resolution when the targets are point scatterers. In this experimental study, we evaluate the performance of the TR-MUSIC algorithm when the interrogated medium contains extended targets that cannot be considered as point scatterers, i.e., the size of the targets is on the order of the ultrasound wavelength or larger. We construct four tissue-mimicking phantoms, each of which contains glass spheres of a given size. We show that the quality of the phantom images obtained using the TR-MUSIC algorithm decreases with increasing sphere size. However, significant improvement is achieved when the image plane is divided into subregions, where each subregion is imaged separately. In this method, the TR-MUSIC calculations are performed on the windowed backscattered signals originating from each subregion. Our study demonstrates that the TR-MUSIC algorithm with time windowing can accurately locate extended targets but cannot provide the shape and reflectivity of the targets. We scan an inhomogeneous commercial tissue-mimicking phantom using an investigational synthetic-aperture ultrasound system, and show that the TR-MUSIC algorithm is capable of detecting small targets with high spatial resolution in inhomogeneous media.

In vivo breast sound-speed imaging with ultrasound tomography.

We discuss a bent-ray ultrasound tomography algorithm with total-variation (TV) regularization. We have applied this algorithm to 61 in vivo breast datasets collected with our in-house clinical prototype for imaging sound-speed distributions in the breast. Our analysis showed that TV regularization could preserve sharper lesion edges than the classic Tikhonov regularization. Furthermore, the image quality of our TV bent-ray sound-speed tomograms was superior to that of the straight-ray counterparts for all types of breasts within BI-RADS density categories 1 through 4. Our analysis showed that the improvements for average sharpness (in the unit of (m x s)(-1)) of lesion edges in our TV bent-ray tomograms are between 2.1 to 3.4-fold compared with the straight ray tomograms. Reconstructed sound-speed tomograms illustrated that our algorithm could successfully image fatty and glandular tissues within the breast. We calculated the mean sound-speed values for fatty tissue and breast parenchyma as 1422 +/- 9 m/s (mean +/- SD) and 1487 +/- 21 m/s, respectively. Based on 32 lesions in a cohort of 61 patients, we also found that the mean sound-speed for malignant breast lesions (1548 +/- 17 m/s) was higher, on average, than that of benign ones (1513 +/- 27 m/s) (one-sided p<0.001). These results suggest that, clinically, sound-speed tomograms can be used to assess breast density (and therefore, breast cancer risk), as well as detect and help differentiate breast lesions. Finally, our sound-speed tomograms may also be a useful tool to monitor the clinical response of breast cancer patients to neo-adjuvant chemotherapy.

An improved automatic time-of-flight picker for medical ultrasound tomography.

OBJECTIVE AND MOTIVATION: Time-of-flight (TOF) tomography used by a clinical ultrasound tomography device can efficiently and reliably produce sound-speed images of the breast for cancer diagnosis. Accurate picking of TOFs of transmitted ultrasound signals is extremely important to ensure high-resolution and high-quality ultrasound sound-speed tomograms. Since manually picking is time-consuming for large datasets, we developed an improved automatic TOF picker based on the Akaike information criterion (AIC), as described in this paper. METHODS: We make use of an approach termed multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. By using multi-model inference, our picking method incorporates all the information near the TOF of ultrasound signals. Median filtering and reciprocal pair comparison are also incorporated in our AIC picker to effectively remove outliers. RESULTS: We validate our AIC picker using synthetic ultrasound waveforms, and demonstrate that our automatic TOF picker can accurately pick TOFs in the presence of random noise with absolute amplitudes up to 80% of the maximum absolute signal amplitude. We apply the new method to 1160 in vivo breast ultrasound waveforms, and compare the picked TOFs with manual picks and amplitude threshold picks. The mean value and standard deviation between our TOF picker and manual picking are 0.4 micros and 0.29 micros, while for amplitude threshold picker the values are 1.02 micros and 0.9 micros, respectively. Tomograms for in vivo breast data with high signal-to-noise ratio (SNR) ( approximately 25 dB) and low SNR ( approximately 18 dB) clearly demonstrate that our AIC picker is much less sensitive to the SNRs of the data, compared to the amplitude threshold picker. DISCUSSION AND CONCLUSIONS: The picking routine developed here is aimed at determining reliable quantitative values, necessary for adding diagnostic information to our clinical ultrasound tomography device--CURE. It has been successfully adopted into CURE, and allows us to generate such values reliably. We demonstrate that in vivo sound-speed tomograms with our TOF picks significantly improve the reconstruction accuracy and reduce image artifacts.