 Here is a simplified Python code based on your instructions. Note that this code assumes you have a `ReconstructionEngine` class in the `sirf.engines` module and a function `truncate_image` for image truncation. Also, it uses `argparse` for command-line options.

```python
import argparse
import sirf.engines
import numpy as np
from scipy.optimize import minimize

def truncate_image(image, diameter):
    return image[int((image.shape[0] - diameter) / 2):int((image.shape[0] + diameter) / 2),
                 int((image.shape[1] - diameter) / 2):int((image.shape[1] + diameter) / 2)]

def poisson_log_likelihood(params, data, acquisition):
    image = np.exp(params)
    return -np.sum(acquisition * np.log(image) - (acquisition * image))

def steepest_ascent(data_file, engine_module, steps, local_optimal, verbose, plot, diameter):
    try:
        engine = getattr(sirf.engines, engine_module)
        reconstruction_engine = engine()

        with open(data_file, 'rb') as f:
            acquisition = pickle.load(f)

        filter_func = lambda x: np.zeros_like(x) if x[0] > diameter or x[1] > diameter else x
        filter = np.vectorize(filter_func)
        acquisition = filter(acquisition)

        initial_image = np.ones_like(acquisition)
        initial_image = truncate_image(initial_image, diameter)

        obj_func = lambda x: poisson_log_likelihood(x, acquisition, reconstruction_engine.acquisition_model)
        res = minimize(obj_func, initial_image.flatten(), method='L-BFGS-B', bounds=[(0, None)] * acquisition.shape[0])

        if local_optimal:
            res = minimize(obj_func, res.x, method='BFGS', jac=lambda x: poisson_log_likelihood_gradient(x, acquisition, reconstruction_engine.acquisition_model), bounds=[(0, None)] * acquisition.shape[0])

        if verbose:
            print(f"Final image: {res.x}")
            print(f"Objective function value: {res.fun}")

        if plot:
            # Add plotting code here

    except Exception as e:
        print(f"Error: {e}")

def poisson_log_likelihood_gradient(params, data, acquisition):
    image = np.exp(params)
    return -acquisition * image / image.sum()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("data_file", help="PET acquisition data file")
    parser.add_argument("engine", help="Reconstruction engine module")
    parser.add_argument("--steps", type=int, default=10, help="Number of steepest descent steps")
    parser.add_argument("--local_optimal", action="store_true", help="Use locally optimal steepest ascent")
    parser.add_argument("--verbose", action="store_true", help="Print verbose output")
    parser.add_argument("--plot", action="store_true", help="Show plots")
    parser.add_argument("--diameter", type=int, default=100, help="Cylinder diameter for image truncation")
    args = parser.parse_args()

    steepest_ascent(args.data_file, args.engine, args.steps, args.local_optimal, args.verbose, args.plot, args.diameter)
```

This code reads command-line arguments, initializes the reconstruction engine, loads the acquisition data, filters the data, initializes the image, defines the objective function and its gradient, and performs the steepest ascent. It also handles exceptions and prints error information if anything goes wrong. The plotting code is left as an exercise for the user.