Neural network synthesis of spin echo multiecho sequences.

Spin echo multiecho sequences are not frequently used in clinical practice, because they allow the observation of one single slice, imaged at different echo times, for each acquisition. To limit examination time, multislice sequences that include only images derived from one or two echoes are usually acquired. Nevertheless, the strong T2 dependence of multiecho sequences can be used effectively to enhance the contrast between tissues with different T2 and to gather useful diagnostic information. Artificial neural networks can offer new interesting facilities to the radiologist. In fact, the learning capabilities of neural networks allow them to extract the prototypical behavior of a system from a set of examples. After learning, artificial neural networks can emulate the system behavior even in the presence of new inputs, as far as these are not too different from those included in the training set. A conveniently trained neural network can synthesize a multiecho sequence for each slice of a multislice sequence, requiring only two images for each slice to achieve reliable results. When compared with a true multiecho sequence, the images generated by the network preserve the contrast characteristics of the original ones and have a better signal-to-noise (SNR) ratio. In this paper we report the results achieved by using a neural network to reconstruct synthetic spin echo multiecho images of the brain.