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Imaging of HIFU-induced lesions provides non-invasive, real-time treatment monitoring and control. This work presents results obtained with HIFU-induced lesion detection algorithms specifically designed for multiple lesion detection. Algorithms sensitive to relative tissue changes during HIFU -measuring signal energy, tissue displacement, entropy, and tissue attenuation are compared for their ability to detect the creation of multiple and adjacent HIFU lesions. In vivo (N=4) canine prostate backscattered RF data was acquired with a custom Sonablate/spl reg/500 HIFU device during 7 treatments. A total of 815 sites were treated, forming the algorithm evaluation dataset. It was found that the algorithm based on signal energy performed best, detecting 82% of all HIFU lesions created, while showing false-alarm rates below 5%. All methods are completely non-invasive, and make use of tissue reference/normalization information obtained before, during, and after the HIFU treatment. Algorithm specifics, data acquisition methodologies, in vivo experimental results, and algorithm comparison results are shown.

A computational model is presented to simulate nonlinear propagation of ultrasound in tissue from array transducers. The model is based on an operator splitting approach with a fractional step marching scheme, whereby the effects of diffraction, attenuation and nonlinearity can be considered independent over small steps. This algorithm avoids the use of the parabolic approximation used by the KZK model, and may be more accurate for focused sources. Simulated transmitted pressure profiles and pulse-echo second-harmonic point spread functions are presented as examples of the capabilities of the algorithm for design and optimization of tissue harmonic imaging systems. Dynamic focusing and pulse inversion are shown to be advantageous for increasing second harmonic levels, and for improving spatial resolution.