Reducing noise and aliasing artefacts in 4D medical images

Abstract

A method 100 is disclosed for training an unsupervised Machine Learning (ML) model to reduce noise and aliasing artefacts in a reconstructed 4D medical image of a patient. The method comprises obtaining a projection set comprising a plurality of 2D projections of a training patient volume 110, wherein the 2D projections represent a plurality of phases of a respiratory cycle of the training patient and comprise measurement noise. The method further comprises repeating, for a plurality of iterations, the steps of selecting two non-empty, mutually disjoint subsets that form a partition of the projection set, wherein each of the two selected subsets contains 2D projections that are respiratory uncorrelated 120 for each of the two selected subsets, using a linear reconstruction algorithm to reconstruct a volumetric image of the training patient from the 2D projections contained in the subset 130, and adding the reconstructed volumetric images of the patient to a training data set as an input volume and corresponding target output volume training pair 140. The method further comprises using training pairs from the training data set to update values of the trainable parameters of the ML model 170.

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