The use of ultrasound in non-invasive medical procedures is a rapidly expanding area of medicine. The success of these treatments often depends on complex ultrasound simulations that require significant computing power, time, and associated calculation costs. To solve the differential equations associated with these simulations, the pseudo-spectral method with Fourier basis functions is employed. Thus, a significant part of the simulation time is spent computing the Fast Fourier Transform. This paper presents an approach that has the potential to reduce computation time and, consequently, the calculation costs of ultrasound wave propagation simulations used in the pre-planning phase of non-invasive treatments by involving the pruned Fast Fourier Transform algorithm (pruned FFT). The paper employs spectrum filtration using a binary map to emulate the behaviour of the pruned FFT. This allows for the evaluation of the impact of the pruned FFT on the number of computed elements in the spectral domain and the accuracy of the simulation. Results on real data have shown that it is possible to replace the Fast Fourier Transform (FFT) applied to acoustic pressure and velocity with the pruned version of the algorithm while obtaining results that are suitable for pre-planning purposes, thereby reducing computation time of the treatment planning. Involving the pruned FFT can also enable the execution of simulations in higher resolution domains with much faster execution times. In some cases, we were able to achieve around 90% accuracy on the single edge of the 2D domain.

Noninvasive ultrasound surgeries represent a rapidly growing field in medical applications. Preoperative planning often relies on computationally expensive ultrasound simulations. This paper explores methods to accelerate these simulations by reducing the computation time of the Fourier transform, which is an integral part of the simulation in the k-Wave toolbox. Two experiments and their results will be presented. The first investigates substituting the standard Fast Fourier Transform (FFT) with a Sparse Fourier Transform (SFT). The second approach utilises filtering of the frequency spectrum, inspired by image compression algorithms. The aim of both experiments is to find a suitable method for accelerating the Fourier transform while utilising the sparsity of the spectrum in acoustic pressure. Our findings show that filtering offers significantly better results in terms of computation error, leading to a substantial reduction in overall simulation runtime.