# Lung Ultrasound Imaging, a Technical Review
## 2 Ultrasound Spectroscopy
Ultrasound images generated with standard ultrasound imaging, as obtained with phantom type 1 and 2, are also shown together with the results of the frequency analysis of B-lines as obtained with the multi-frequency imaging method. This graph indicates the frequency for which the maximum of the received power spectrum was found, while spanning the frequency range from 3 to 6 MHz. As it can be observed, this parameter allowed to distinguish between these two phantom types. This was the first study demonstrating how the native frequency of B-lines could be applied to characterize bubbly structures. In principle, this concept could be clinically applied to quantitatively evaluate the state of the lung surface, and indirectly grade the lung condition. Interestingly, other recent studies already proved that B-lines visualization depends (also on clinical data) on the transmitted ultrasound frequencies and introduced some basic models that can be applied to better understand the formation of these artifacts [46]. Figure 2: Microscope images of the generated microbubbles, together with a phantom schema and a picture of a lung mimicking phantom (on the left). Ultrasound images generated with standard ultrasound imaging, as obtained with phantom type 1 and 2, together with the results of the frequency analysis of B-lines as obtained with the multi-frequency imaging method. As an example, Figure 3 shows ultrasound images as obtained with a clinical scanner. During the investigation, the imaging frequency was varied from 3 MHz to 6 MHz. As it can be seen, B-lines were visualized only at 6 MHz. These results show that, with the currently available hardware, a frequency characterization of these vertical artifacts is possible. The question is, what can this characterization provide us with, in terms of the capability to grade and distinguish between lung pathologies? Another important aspect concerns the possibility to implement, in real-time, a system able to detect and localize these artifacts. This could aid the clinician in the visualization of B-lines, and most importantly, would be extremely helpful in order to limit the computational load needed for the additional signal processing operations required for the characterization of the B-lines. Recent work has shown how a simple convolution neural network can be trained for this purpose [47]. In this work, B-line detection and localization was performed in a weakly-supervised fashion through class activation mapping. The method was applied both on phantom and patient data. Moreover, images acquired with both research ultrasound platforms and standard clinical ultrasound scanners have been used. An accuracy, sensitivity, specificity, negative and positive predictive value equal to 0.917, 0.915, 0.918, 0.950, and 0.864 were achieved in-vitro, respectively. In-vivo, these statistics were 0.892, 0.871, 0.930, 0.798, and 0.958, respectively. Most importantly the proposed method allowed B-line detection in real-time, reaching an inference rate of 276 frames/second when exploiting GPU acceleration (Titan Xp, NVIDIA). Figure 4 shows, as an example, two ultrasound images obtained during patient examinations, together with the results obtained with the method described above. The heat map overlaid to the ultrasound image indicated the estimated location of the B-line artifact. Results are shown from the data generated with an open research platform, as well as from a clinical ultrasound scanner. Figure 3: Ultrasound images obtained during patients investigation using a clinical scanner. The imaging frequency was varied from 3 MHz (**left**) to 6 MHz (**right**).