Matlab's phased array toolbox provides a comprehensive set of functions to perform beamforming for ultrasound imaging. However, depending on the size of the array and the complexity of the beamforming algorithm, the processing time may become a bottleneck. In this case, you may want to consider alternative methods for implementing beamforming that are faster and more efficient.
One approach you can try is to use parallel processing to speed up the computation. You can use the Parallel Computing Toolbox in Matlab to distribute the processing across multiple cores or machines, which can significantly reduce the processing time. The 'parfor' loop in Matlab is a parallel version of the regular for loop that can be used to perform operations in parallel. For example, you can use 'parfor' to parallelize the nested for-loop that you are currently using to estimate the signal at each lateral and axial position. You can refer to this documentation to lean more about 'parfor' - https://in.mathworks.com/help/parallel-computing/parfor.html
Another approach is to use GPU computing to accelerate the computation. Matlab's Parallel Computing Toolbox provides functions that allow you to perform computations on a GPU, which can be much faster than using the CPU. You can use the 'gpuArray' function to move the data to the GPU, and then use built-in functions such as 'gather', 'bsxfun', and 'arrayfun' to perform the computation. You can refer to this documentation for 'gpuarray' - https://in.mathworks.com/help/parallel-computing/gpuarray.html
