Reducing noise and aliasing artefacts in 4d medical images

A computer implemented method for reducing noise and aliasing artefacts in a reconstructed four-dimensional, 4D, medical image of a patient, the method comprising:

• obtaining a projection set comprising a plurality of two-dimensional (2D) projections of a patient volume, wherein the obtained 2D projections represent a plurality of phases of a respiratory cycle of the patient, and wherein the obtained 2D projections comprise measurement noise;
• dividing the plurality of 2D projections in the projection set into a plurality of phase sets, wherein each phase set comprises 2D projections that are respiratory correlated, corresponding to a single phase of the respiratory cycle of the patient;
• for each phase of the respiratory cycle of the patient, generating a reconstructed volumetric image of the patient from the 2D projections contained in the corresponding phase set;
• and
• inputting the generated reconstruction to an unsupervised Machine Learning (ML) model, wherein the ML model is operable to process the input generated reconstructed volumetric images according to trained parameters of the ML model and to output volumetric images of the patient having reduced noise and aliasing artefacts, and wherein the ML model is trained using reconstructed volumetric images that are respiratory uncorrelated.

A radiotherapy treatment apparatus comprising at least one of:

• a) a training node for training an unsupervised Machine Learning (ML) model to reduce noise and aliasing artefacts in a reconstructed four-dimensional (4D) medical image of a patient, wherein the ML model comprises a plurality of trainable parameters, the training node comprising processing circuitry configured to cause the training node to:
• i. obtain a projection set comprising a plurality of two-dimensional, 2D, projections of a training patient volume, wherein the obtained 2D projections represent a plurality of phases of a respiratory cycle of the training patient, and wherein the obtained 2D projections comprise measurement noise;
• ii. repeat, for a plurality of iterations:
• 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;
• 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;
• 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;
• and
• wherein the processing circuitry is further configured to cause the training node to:
• iii. use training pairs from the training data set to update values of the trainable parameters of the ML model;
• or
• b) a processing node for reducing noise and aliasing artefacts in a reconstructed four-dimensional, 4D, medical image of a patient, the processing node comprising processing circuitry configured to cause the processing node to:
• obtain a projection set comprising a plurality of two-dimensional (2D) projections of the patient volume, wherein the obtained 2D projections represent a plurality of phases of a respiratory cycle of the patient, and wherein the obtained 2D projections comprise measurement noise;
• divide the plurality of 2D projections in the projection set into a plurality of phase sets, wherein each phase set comprises 2D projections that are respiratory correlated, corresponding to a single phase of the respiratory cycle of the patient;
• for each phase of the respiratory cycle of the patient, generate a reconstructed volumetric image of the patient from the 2D projections contained in the corresponding phase set;
• and
• input the generated reconstruction to an unsupervised Machine Learning (ML) model, wherein the ML model is operable to process the input generated reconstructed volumetric images according to trained parameters of the ML model, and to output volumetric images of the patient having reduced noise and aliasing artefacts, wherein the ML model is trained using reconstructed volumetric images that are respiratory uncorrelated.