We use multiple scattering of ultrasound waves to characterize the lung micro-architecture in order to differentiate between a healthy lung and a lung suffering from Alveolar Interstitial Lung Diseases. The experimental setup consists of a linear transducer array with an 8 MHz central frequency placed in direct contact of the lung to be assessed. The diffusion constant D and scattering mean free path L* of the lung parenchyma are estimated by separating the incoherent and the coherent intensities in the near field. 2D FDTD numerical simulations were carried out on rabbit histology images with varying degrees of lung collapse. Phantom experiments were conducted in melamine sponges to study the variations in D and L* with varying air volume fraction. Significant correlations were observed between air volume fraction and L* in simulation (r = -0.9542, p<0.0117) and sponge phantom experiments (r = -0.9932, p<0.0068). Finally, in vivo measurements were conducted in healthy and edematous rat lungs. In the control rat lung, L* was found equal to 83 μm ( + /-14.9), whereas in the edematous lung, it was found equal to 260 μm ( + /-27). These results are extremely promising for the assessment of lung pathologies using ultrasound.

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Most solid tumors are characterized by highly dense, isotropic vessel networks. Characterization of such features has shown promise for early cancer diagnosis. Ultrasound diffusion has been used to characterize the micro-architecture of complex media, such as bone and the lungs. In this work, we examine a non-invasive diffusion-based ultrasound technique to assess neo-vascularization. Because the diffusion constant reflects the density of scatterers in heterogeneous media, we hypothesize that by injecting microbubbles into the vasculature, ultrasound diffusivity can reflect vascular density (VD), thus differentiating the microvascular patterns between tumors and healthy tissue. The diffusion constant and its anisotropy are shown to be significantly different between fibrosarcoma tumors (n = 16) and control tissue (n = 18) in a rat animal model in vivo. The diffusion constant values for control and tumor were found to be 1.38 ± 0.51 mm 2 μs −1 and 0.65 ± 0.27 mm 2 μs −1 , respectively. These results are corroborated with VD from acoustic angiography (AA) data, confirming increased vessel density in tumors compared to controls. The diffusion constant offers a promising way to quantitatively assess vascular networks when combined with contrast agents, which may allow early tumor detection and characterization.

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as pore diameter (ϕ), pore density (ρ) and porosity (ν) of cortical bone from ultrasound frequency dependent attenuation using an artificial neural network (ANN). First, heterogeneous structures with controlled pore diameters and pore densities (mono-disperse) were generated, to mimic simplified structure of cortical bone. Then, more realistic structures were obtained from high resolution CT scans of human cortical bone. 2-D dimensional finite-difference time-domain simulations were conducted to calculate the frequencydependent attenuation in the 1-8MHz range. An ANN was then trained with the ultrasonic attenuation at different frequencies as the input feature vectors while the output was set as the micro-architectural parameters (pore diameter, pore density and porosity). The ANN is composed of three fully connected dense layers with 24, 12 and 6 neurons, connected to the output layer. The dataset was trained over 6000 epochs with a batch size of 16. The trained ANN exhibits the ability to predict the micro-architectural parameters with high accuracy and low losses. ANN approaches could potentially be used as a tool to help inform physics-based modelling of ultrasound propagation in complex media such as cortical bone. This will lead to the solution of inverse-problems to retrieve bone micro-architectural parameters from ultrasound measurements for the non-invasive diagnosis and monitoring osteoporosis.