# US 2021/0241458 A1 · Contrast Dose Reduction for Medical Imaging Using Deep Learning

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- Number: US 2021/0241458 A1
- Document ID: US20210241458A1
- Status: Published application
- Kind: A1
- Application number: 17/239,898
- Filed: 2021-04-26
- Published: 2021-08-05
- Earliest claimed: 2017-10-09
- Assignees: The Board of Trustees of the Leland Stanford Junior University
- Applicants: The Board of Trustees of the Leland Stanford Junior University
- Inventors: Greg Zaharchuk; Enhao Gong; John M. Pauly
- Web page: https://patentgrep.com/patent/US20210241458A1

## CPC

- G06T 5/50: using two or more images, e.g. averaging or subtraction
- G06T 7/0012: {Biomedical image inspection}
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06T 3/60: Rotation of whole images or parts thereof
- G06T 5/60: using machine learning, e.g. neural networks
- G06T 5/70: Denoising; Smoothing
- G06T 7/50: Depth or shape recovery
- G16H 30/40: for processing medical images, e.g. editing
- G06N 3/048: Activation functions
- G06T 2207/10072: Tomographic images
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10116: X-ray image
- G06T 2207/10121: Fluoroscopy
- G06T 2207/10132: Ultrasound image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/20221: Image fusion; Image merging
- G06T 2207/30004: Biomedical image processing
- G06T 2207/30016: Brain
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [114] that has been trained using zero-contrast [100] and low-contrast [102] images as input to the DLN and full-contrast images [104] as reference ground truth images. Prior to training, the images are pre-processed [106, 110, 118] to co-register and normalize them. The trained DLN [114] is then used to predict a synthesized full-dose contrast agent image [116] from acquired zero-dose and low-dose images.

## Claims

### Claim 1

A method for performing medical diagnostic imaging of a subject with reduced contrast agent dose, the method comprising:
a) acquiring a first image with a first image acquisition sequence;
b) acquiring a second image with a second image acquisition sequence distinct from the first image acquisition sequence, wherein the first image and the second image are acquired using a common imaging modality and with a zero contrast agent dose or low contrast agent dose administered to the subject, where the low contrast agent dose is less than a full contrast agent dose;
c) processing the first image and the second image to co-register and normalize the images to adjust for acquisition and scaling differences; and
d) applying the first image and the second image as input to a deep learning network (DLN) to generate as output of the DLN an image of the subject with an enhanced quality.

### Claim 2 (depends on 1)

The method of claim 1 wherein the DLN is trained using a training dataset including zero-contrast agent dose images acquired with different imaging sequences and full-contrast agent dose images acquired with a full contrast agent dose administered as reference ground-truth images.

### Claim 3 (depends on 1)

The method of claim 1 wherein the DLN is trained using a training dataset including zero-contrast agent dose images acquired with zero contrast agent dose, low-contrast agent dose images acquired with low contrast agent dose and full-contrast agent dose images acquired with a full contrast agent dose administered as reference ground-truth images.

### Claim 4 (depends on 1)

The method of claim 1 wherein the zero-contrast agent dose images are acquired with different imaging sequences.

### Claim 5 (depends on 1)

The method of claim 1 wherein the low contrast agent dose is less than 10% of a full contrast agent dose.

### Claim 6 (depends on 1)

The method of claim 1 wherein the common imaging modality is selected from the group consisting of angiography, fluoroscopy, computed tomography (CT), ultrasound, and magnetic resonance imaging.

### Claim 7 (depends on 1)

The method of claim 1 wherein the common imaging modality is magnetic resonance imaging, and a full contrast agent dose is at most 0.1 mmol/kg Gadolinium MRI contrast.

### Claim 8 (depends on 1)

The method of claim 1 wherein the DLN is an encoder-decoder convolutional neural network (CNN) including bypass concatenate connections and residual connections.

### Claim 9 (depends on 1)

The method of claim 1 wherein the first image acquisition sequence is selected from a T1w, T2w, FLAIR, or DWI sequence.

### Claim 10 (depends on 1)

The method of claim 1 wherein the second image acquisition sequence is selected from a T1w, T2w, FLAIR, or DWI sequence.

### Claim 11 (depends on 1)

The method of claim 1 wherein processing the first image and the second image to co-register and normalize the images comprises normalizing the intensity within each contrast via histogram matching on median.

### Claim 12 (depends on 1)

The method of claim 1 wherein processing the first image and the second image comprises including a bias field correction to correct the bias field distortion via a N4ITK algorithm.

### Claim 13 (depends on 1)

The method of claim 1 wherein processing the first image and the second image comprises using a trained classifier (VGG16) to filter out abnormal slices.