Quantitative Sound Speed Imaging of Cortical Bone and Soft Tissue: Results From Observational Data Sets.

This work presents the first quantitative ultrasonic sound speed images of ex vivo limb cross-sections containing both soft tissue and bone using Full Waveform Inversion (FWI) with level set (LS) and travel time regularization. The estimated bulk sound speed of bone and soft tissue are within 10% and 1%, respectively, of ground truth estimates. The sound speed imagery shows muscle, connective tissue and bone features. Typically, ultrasound tomography (UST) using FWI is applied to imaging breast tissue properties (e.g. sound speed and density) that correlate with cancer. With further development, UST systems have the potential to deliver volumetric operator independent tissue property images of limbs with non-ionizing and portable hardware platforms. This work addresses the algorithmic challenges of imaging the sound speed of bone and soft tissue by combining FWI with LS regularization and travel time methods to recover soft tissue and bone sound speed with improved accuracy and reduced soft tissue artifacts when compared to conventional FWI. The value of leveraging LS and travel time methods is realized by evidence of improved bone geometry estimates as well as promising convergence properties and reduced risk of final model errors due to un-modeled shear wave propagation. Ex vivo bulk measurements of sound speed and MRI cross-sections validates the final inversion results.

Elastography for portable ultrasound.

Portable wireless ultrasound has been emerging as a new ultrasound device due to its unique advantages including small size, lightweight, wireless connectivity and affordability. Modern portable ultrasound devices can offer high quality sonogram images and even multiple ultrasound modes such as color Doppler, echocardiography, and endovaginal examination. However, none of them can provide elastography function yet due to the limitations in computational performance and data transfer speed of wireless communication. Also phase-based strain estimator (PSE) that is commonly used for conventional elastography cannot be adopted for portable ultrasound, because ultrasound parameters such as data dumping interval are varied significantly in the practice of portable ultrasound. Therefore, this research aims to propose a new elastography method suitable for portable ultrasound, called the robust phase-based strain estimator (RPSE), which is not only robust to the variation of ultrasound parameters but also computationally effective. Performance and suitability of RPSE were compared with other strain estimators including time-delay, displacement-gradient and phase-based strain estimators (TSE, DSE and PSE, respectively). Three types of raw RF data sets were used for validation tests: two numerical phantom data sets modeled by an open ultrasonic simulation code (Field II) and a commercial FEA (Abaqus), and the one experimentally acquired with a portable ultrasound device from a gelatin phantom. To assess image quality of elastograms, signal-to-noise (SNRe) and contrast-to-noise (CNRe) ratios were measured on the elastograms produced by each strain estimator. The computational efficiency was also estimated and compared. Results from the numerical phantom experiment showed that RPSE could achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 10 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 s per frame for RPSE and TSE, respectively).

Compressed Sensing for Elastography in Portable Ultrasound.

Portable ultrasound is recently emerging as a new medical imaging modality featuring high portability, easy connectivity, and real-time on-site diagnostic ability. However, it does not yet provide ultrasound elastography function that enables the diagnosis of malignant lesions using elastic properties. This is mainly due to the limitations of hardware performance and wireless data transfer speed for processing the large amount of data for elastography. Therefore, data transfer reduction is one of the feasible solutions to overcome these limitations. Recently, compressive sensing (CS) theory has been rigorously studied as a means to break the conventional Nyquist sampling rate and thus can significantly decrease the amount of measurement signals without sacrificing signal quality. In this research, we implemented various CS reconstruction frameworks and comparatively evaluated their reconstruction performance for realizing ultrasound elastography function on portable ultrasound. Combinations of three most common model bases (Fourier transform [FT], discrete cosine transform [DCT], and wave atom [WA]) and two reconstruction algorithms (L1 minimization and block sparse Bayesian learning [BSBL]) were considered for CS frameworks. Echoic and elastography phantoms, were developed to evaluate the performance of CS on B-mode images and elastograms. To assess the reconstruction quality, mean absolute error (MAE), signal-to-noise ratio (SNRe), and contrast-to-noise ratio (CNRe) were measured on the B-mode images and elastograms from CS reconstructions. Results suggest that CS reconstruction adopting BSBL algorithm with DCT model basis can yield the best results for all the measures tested, and the maximum data reduction rate for producing readily discernable elastograms is around 60%.

Application of Eshelby's Solution to Elastography for Diagnosis of Breast Cancer.

Eshelby's solution is the analytical method that can derive the elastic field within and around an ellipsoidal inclusion embedded in a matrix. Since breast tumor can be regarded as an elastic inclusion with different elastic properties from those of surrounding matrix when the deformation is small, we applied Eshelby's solution to predict the stress and strain fields in the breast containing a suspicious lesion. The results were used to investigate the effectiveness of strain ratio (SR) from elastography in representing modulus ratio (MR) that may be the meaningful indicator of the malignancy of the lesion. This study showed that SR significantly underestimates MR and is varied with the shape and the modulus of the lesion. Based on the results from Eshelby's solution and finite element analysis (FEA), we proposed a surface regression model as a polynomial function that can predict the MR of the lesion to the matrix. The model has been applied to gelatin-based phantoms and clinical ultrasound images of human breasts containing different types of lesions. The results suggest the potential of the proposed method to improve the diagnostic performance of breast cancer using elastography.