  import sirf

def truncate_image(image, diameter):
    # Truncate the image to a cylinder of the specified diameter
    pass

def main(engine, data_file, data_path, num_steps, local_opt, verbosity, show_plots):
    # Import the specified engine module
    engine_module = sirf.engines.get_engine(engine)

    # Process the command-line options
    options = sirf.options.Options()
    options.set_option("reconstruction_engine", engine_module)
    options.set_option("raw_data_file", data_file)
    options.set_option("data_path", data_path)
    options.set_option("num_steps", num_steps)
    options.set_option("local_opt", local_opt)
    options.set_option("verbosity", verbosity)
    options.set_option("show_plots", show_plots)

    # Create an acquisition model
    acquisition_model = sirf.models.AcquisitionModel(options)

    # Read PET acquisition data from the specified file
    acquisition_data = sirf.data.read_acquisition_data(data_file, acquisition_model)

    # Create a filter that zeroes the image outside a cylinder of the same diameter as the image xy-section size
    filter = sirf.filters.CylinderFilter(acquisition_data.image_size, diameter)

    # Create an initial image estimate
    initial_image = sirf.image.Image(acquisition_data.image_size, acquisition_data.image_origin, acquisition_data.image_spacing)

    # Create an objective function of Poisson logarithmic likelihood type compatible with the acquisition data type
    objective_function = sirf.objectives.PoissonLogLikelihoodObjectiveFunction(acquisition_data, initial_image)

    # Perform the steepest descent steps
    try:
        final_image = sirf.reconstruction.steepest_descent(acquisition_data, objective_function, num_steps, local_opt, verbosity, show_plots)
    except sirf.reconstruction.SteepestDescentError as e:
        print(e)

if __name__ == "__main__":
    main()