Reducing noise in CT images using synthetic data

1 . An image processing system comprising:

a processor communicably coupled to a non-transitory memory storing a neural network, the memory including instructions that when executed cause the processor to:

receive a plurality of synthetic computed tomography (CT) images, each synthetic CT image generated by performing a tissue segmentation of a reference image of a plurality of reference images of a subject and assigning CT image values to types of tissues identified by the tissue segmentation;

for each synthetic CT image of the plurality of synthetic CT images, add noise to the synthetic CT image to create a noisy version of the synthetic CT image;

create a respective plurality of image pairs, each image pair including a synthetic CT image as a target, ground truth image, and a corresponding noisy version of the synthetic CT image as an input image;

train the neural network using the image pairs;

deploy the trained neural network to generate noise-reduced images from CT images acquired from a scanned subject; and

display the noise-reduced images on a display device of the image processing system;

wherein the plurality of reference images include a composite image generated from a combination of a plurality of magnetic resonance (MR) images, each MR image of the plurality of MR images acquired from a same slice of a scan of a patient using a different pulse sequence.

2 . The image processing system of claim 1 , wherein further instructions are stored in the memory that when executed, cause the processor to:

select, from the plurality of MR images acquired from the same slice, a set of MR images having a resolution greater than a threshold resolution; and

generate the composite image from the set of MR images.

3 . The image processing system of claim 1 , wherein the plurality of reference images includes histological images taken from anatomical samples.

4 . The image processing system of claim 1 , wherein the noise is added using projection-based noise insertion.

5 . A method for an image processing system, comprising:

acquiring a reference image of an anatomical region of a subject;

segmenting tissues of the reference image;

assigning voxel intensity values to different tissue types of the reference image to generate a first synthetic computed tomography (CT) image corresponding to the reference image;

adding noise to the first synthetic CT image to generate a second, noisy synthetic CT image;

creating a training image pair including the second synthetic CT image as an input image, and the first synthetic CT image as a ground truth image;

training a convolutional neural network (CNN) to remove noise from synthetic CT images, using training data including the training image pair;

using the trained CNN to remove noise from a real CT image acquired from a scanned subject; and

displaying a noise-reduced version of the real CT image on a display device of the image processing system and/or storing the noise-reduced version in a memory of the image processing system;

wherein the reference image is a composite image generated from a combination of a plurality of magnetic resonance (MR) images, each MR image of the plurality of MR images acquired from a same slice of a scan of a patient using a different pulse sequence.

6 . The method of claim 5 , wherein segmenting the tissues of the reference image further comprises manually segmenting the tissues of the reference image.

7 . The method of claim 5 , wherein the reference image is one of a plurality of reference images used to create training data for training the CNN, and the plurality of reference images further includes images acquired from a CT spectral imaging scan, a positron emission tomography (PET) scan, and a CT spectral imaging scan.

8 . The method of claim 7 , wherein the plurality of reference images further includes images acquired from histology samples.

9 . The image processing system of claim 3 , wherein the histological images include a first histological image obtained with a first stain, and a second histological image obtained with a second stain.

10 . The image processing system of claim 3 , wherein the histological images include a first histological image obtained with a first illumination spectrum, and a second histological image obtained with a second illumination spectrum.

11 . The image processing system of claim 1 , wherein the plurality of reference images includes positron emission tomography (PET) images.

12 . The image processing system of claim 11 , wherein the PET images include a first PET image obtained with a first tracer agent, and a second PET image obtained with a second tracer agent.

13 . The image processing system of claim 1 , wherein the plurality of reference images includes spectral CT images.

14 . The image processing system of claim 1 , wherein the plurality of reference images includes single photon emission computerized tomography (SPECT) images.