Abstract: In a computer-implemented method of training a reconstruction neural network algorithm used to reconstruct a Magnetic Resonance Imaging (MRI) image, a prediction of training MRI image is determined based on training MRI raw data and using the reconstruction neural network algorithm. A prediction of a presence or absence of the object in the training MRI image is determined based on the prediction of the training MRI image and using an object-detection algorithm. A loss value is determined based on a first difference between the ground truth of the training MRI image and the prediction of the training MRI image, and further based on a second difference between the ground truth of the presence or absence of the object and the prediction of the presence or absence of the object. Weights of the reconstruction neural network algorithm are adjusted based on the loss value and using a training process.

Abstract: A device for the detection of undesirable echoes which have significant correlation in azimuth which is applicable to surveillance or tracking radars. The device works through autocorrelation function estimation over a pair of pulses received by averaging, over N range cells, the complex product of the received signal Z.sub.k (t.sub.1), relevant to the K-th range bin and time instant t.sub.1, with the complex conjugate value Z.sub.k (t.sub.1 +T), still relevant to the K-th range bin, at instant t.sub.1 +T (where T is the pulse repetition period, PRT).