Michael L. Oelze

Biological tissues exhibit power-law frequency dependence of the attenuation coefficient. We derive closed-form weak-wave expressions for fundamental, second-, and third-harmonic pressure amplitudes of monochromatic acoustic plane waves in homogeneous media with arbitrary power-law attenuation to extend classical expressions for quadratic attenuation. For linear frequency dependence of attenuation, one-dimensional simulations using the k-Wave k-space pseudospectral solver yielded 35%–42% (classical) and 0%–2% (proposed) normalized root mean square errors for fundamental-band depletion. This extension supports improved fundamental-band B/A estimation for quantitative ultrasound tissue characterization. A proof-of-concept through-transmission application in phantoms with nearly linear attenuation yielded experimental B/A estimation errors of 23.7%–52.7% (classical) and 4.0%–5.9% (proposed).

Several applications of medical ultrasound can benefit from a larger field of view (FOV). This study is aimed at increasing the FOV of linear array probes by increasing the element width. Coupled elements were used to imitate a larger element width. Through Fourier analysis, theoretical pressure amplitudes, and bandwidth estimates, coupled elements are shown to be close approximations of large elements. The effects of coupling on resolution, contrast, and speckle signal-to-noise ratio are investigated through phantom images and in vivo images of a rabbit tumor reconstructed with plane wave compounding. Furthermore, a positioning system was used to acquire data from a virtual large aperture with 120 mm FOV and 128 elements, collected in sections with a single probe. The null subtraction imaging (NSI), sign coherence factor, and minimum variance (MV) beamformers are compared for regaining resolution lost by an increased F-number. The NSI beamformer decreased full-width at half-max estimates of wire targets by 79% with coupling by 2 compared to uncoupled DAS. The MV beamformer was best for maintaining speckle statistics while improving resolution. Our results demonstrate how increased element width can increase FOV with no increase to element count.

Quantitative ultrasound (QUS) techniques provide tissue characterization and have applications in precision diagnostics and therapy monitoring. A common QUS approach estimates backscatter coefficients (BSCs) using the external reference phantom method, which does not adequately address attenuation and transmission losses. To address this issue, we previously utilized a 2 mm titanium bead as an in situ calibration reference with conventional focused wave (FW) imaging. Plane wave compounding (PWC), which transmits multiple angled plane waves, enables rapid image acquisition while maintaining quality, making it widely useful in Doppler and elastographic imaging. However, its role in BSC estimation with an in situ bead had not been investigated. We hypothesized that PWC would outperform FW, as diffraction effects vary more slowly away from the bead depth when using PWC. We tested this hypothesis by comparing BSC estimation in phantoms and in vivo rabbit mammary tumors. Results indicated that PWC with in situ calibration better compensated for attenuation loss and reduced variability in scatterer property estimates compared to FW. Specifically, the scatterer diameter variance was 15.2 μm2 when using PWC versus 45.4 μm2 when using FW where the focus was offset from the bead depth. These findings demonstrate the superiority of using PWC with an in situ calibration target for accurate, efficient BSC estimation.

This study assessed the level of agreement between quantitative ultrasound (QUS) feature estimates derived from ultrasound images of breast tumors in women with locally advanced breast cancer (LABC) produced using a cart-based and a handheld ultrasound system. Thirty LABC patients receiving neoadjuvant chemotherapy were imaged at two separate times: a pre-treatment ‘baseline’ time point, and four weeks after the start of chemotherapy. Three sets of QUS features were produced using the reference phantom technique, one for each imaging time and a third set calculated by taking the differences in feature estimates between times. Cross-system statistical testing using the Wilcoxon signed-rank test was performed for each feature set to assess the level of feature estimate agreement between ultrasound systems. The Bland–Altman method was employed to graphically assess feature sets for systematic skew. The range of p-values was 4.50 × 10⁻¹¹ to 0.277 for the baseline features, 2.77 × 10⁻⁵ to 0.865 for the week 4 features, and 2.03 × 10⁻⁹ to 1 for the feature differences. For the feature differences, all five of the primary QUS features (MBF, SS, SI, ASD, AAC) were found to be in agreement between the two scanner types at the 5% confidence level. For the baseline feature set and week 4 feature set, 0 out of 5 and 3 out of 5 of the primary features were found to be in agreement, respectively. Of the 20 QUS texture features examined, the number and proportion of the total for each feature set which were found to have statistically significant similarity in their sample medians at the 5% confidence level were as follows: 2 out of 20 (10%) for the baseline features; 17 out of 20 (85%) for the week 4 features; and 12 out of 20 (60%) for the feature differences. The specific texture features found to be in agreement varied between QUS-specific feature sets. Overall, a moderate level of agreement between sets of feature differences produced using the two systems was demonstrated.

Accurate displacement estimation is central to ultrasound strain imaging, which provides a full description of a 2D/3D deformation field to infer elasticity and detect tissue abnormalities. However, lateral displacement estimation has been the major bottleneck, especially with high frame rate requirements, due to inferior spatial resolution, low sampling, and the lack of phase information in that direction. This study focuses on enhancing lateral displacement estimation by improving the lateral resolution of ultrasound multiangle plane-wave imaging through a nonlinear beamformer, Incoherent Compounding Null Subtraction Imaging (IC-NSI). While IC-NSI effectively reduces both main lobe width and secondary lobe levels, its limited penetration depth and reduced speckle coherence are detrimental to tissue motion estimation. We hereby propose to improve the signal-to-noise ratio (SNR) and thus the penetration depth by integrating cascaded dual-polarity waves (CDW) into IC-NSI. We evaluate the feasibility and performance of IC-NSI, with and without CDW, for tissue displacement estimation through Field II and COMSOL simulations of deforming tubes, phantom experiments, and an in vivo study of the human common carotid artery. Both in silico and in vivo carotid ultrasound images obtained by the proposed method exhibited ∼40% improvement in lateral resolution compared with those by coherent plane wave compounding (CPWC). In phantom experiments, the sonographic SNR improved by 14∼17 dB across depths of 20 to 40 mm and had a 32% reduction in mean absolute error in lateral motion estimation in a controlled linear translation scenario. In simulations, our method achieved 13% higher estimation accuracy in lateral displacement under varying strain conditions compared to CPWC. These results demonstrated the conceptual and computational simplicity of the proposed method for high quality ultrasound strain imaging of thin biological structures.

Breast cancer detection remains a significant challenge, with traditional mammography presenting barriers such as discomfort, radiation exposure, high false-positive rates, and financial burden. Moreover, younger women frequently fall outside routine mammographic screening guidelines, leaving critical gaps in early detection. Objectives: This study investigates the potential of quantitative transmission breast acoustic computed tomography scanner imaging (QT3D) as an innovative, non-invasive imaging modality for characterizing and evaluating breast masses. Methods: A comparative analysis between QT3D imaging and magnetic resonance imaging (MRI) was conducted in a cohort of patients with biopsy-proven benign or malignant breast lesions, comparing key metrics in quantifying breast masses for the purposes of breast mass characterization. Results: The findings in this study highlight its capability in identifying relatively small tumors, multiple lesions, satellite lesions, intraductal extensions, and calcifications, in addition to offering valuable diagnostic insights. Conclusions: This work is a first step toward studies essential for confirming its clinical feasibility, establishing its role in breast cancer tumor characterization, and potentially improving patient outcomes.

Ultrasonic attenuation can be used to characterize tissue properties of the human breast. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can provide attenuation estimation. However, limitations have been identified for both approaches. In QUS, the generation of attenuation maps involves separating the whole image into different data blocks. The optimal size of the data block is around 15–30 pulse lengths, which dramatically decreases the spatial resolution for attenuation imaging. In USCT, the attenuation is often estimated with a full wave inversion (FWI) method, which is affected by background noise. To achieve a high-resolution attenuation image with low variance, a deep learning (DL)-based method was proposed. In the approach, RF data from 60 angle views from the QTI Breast Acoustic CT (BACT) scanner were acquired as the input and attenuation images as the output. To improve image quality for the DL method, the spatial correlation between speed of sound (SOS) and attenuation were used as a constraint in the model. The results indicated that by including the SOS structural information, the performance of the model was improved. With a higher spatial resolution attenuation image, further segmentation of the breast can be achieved. The structural information and actual attenuation values provided by DL-generated attenuation images were validated with the values from the literature and the SOS-based segmentation map. The information provided by DL-generated attenuation images can be used as an additional biomarker for breast cancer diagnosis.

Quantitative ultrasound using spectral‐based techniques, like the backscatter coefficient (BSC), have demonstrated capabilities for tumor characterization and therapy monitoring. The incorporation of an in situ calibration target, that is, a small titanium bead, can provide more consistent BSC estimates. For analyzing tumors, BSC estimation traditionally relies on manual tumor segmentation and calibration bead detection, a time‐consuming and skill‐dependent task. This study utilizes a U‐Net model for automatic BSC estimation by integrating identification of a titanium calibration target embedded in rabbit mammary tumors with automatic segmentation, enabling real‐time applications. The U‐Net model demonstrated strong segmentation performance, achieving a Dice score of 0.86. Performance metrics demonstrated reliable BSC parameter estimation, with relative errors of 17.87% for effective scatter diameter (ESD) and 9.95% for effective attenuation concentration (EAC) when comparing automated segmentation to manual segmented tumors, highlighting its potential for accurate, real‐time tumor diagnostics and therapy monitoring in clinical practice.