# US 2025/0095241 A1 · Accessible neural network image processing workflow

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- Number: US 2025/0095241 A1
- Document ID: US20250095241A1
- Status: Published application
- Kind: A1
- Application number: 18/726,338
- Filed: 2022-07-08
- Published: 2025-03-20
- Earliest claimed: 2021-07-09
- Assignees: Carl Zeiss X-ray Microscopy, Inc.
- Applicants: Carl Zeiss X-ray Microscopy, Inc.
- Inventors: Matthew ANDREW; Lars OMLOR; Andriy ANDREYEV; Christoph Hilmar GRAF VOM HAGEN
- Web page: https://patentgrep.com/patent/US20250095241A1

## CPC

- G06T 12/20: Inverse problem, i.e. transformations from projection space into object space
- G06F 18/211: Selection of the most significant subset of features
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2163: {Partitioning the feature space}
- G06F 18/2178: {based on feedback of a supervisor}
- G06N 3/0475: Generative networks
- G06N 3/08: Learning methods
- G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn
- G06T 2211/432: Truncation
- G06T 2211/441: AI-based methods, deep learning or artificial neural networks
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

## Claims

### Claim 1

A method comprising:
receiving training data of a sample, wherein the training data is acquired by an X-ray imager using training parameters;
receiving an improvement selection;
partitioning the training data into a plurality of training subsets using the improvement selection;
generating a computed tomography (CT) training reconstructed volume for at least two of the plurality of training subsets;
training a neural network using each of the CT training reconstructed volumes;
receiving additional imaging data, wherein i) the additional imaging data is of the sample acquired using additional parameters different than the training parameters, or ii) the additional imaging data is of an additional sample acquired using the training parameters; and
generating a CT reconstructed volume using the additional imaging data and the trained neural network.

### Claim 2 (depends on 1)

The method of claim 1, wherein the additional imaging data is of the sample acquired using the additional parameters, and wherein the additional parameters are selected to achieve a greater throughput than the training parameters.

### Claim 3 (depends on 1)

The method of claim 1, wherein the training parameters are associated with a training number of projections, wherein the additional parameters are associated with an additional number of projections that is smaller than the training number of projections; and wherein the method further comprises determining the additional number of projections using the training number of projections and the improvement selection.

### Claim 4 (depends on 3)

The method of claim 3, wherein the improvement selection is an improvement factor, and wherein determining the additional number of projections includes dividing the training number of projections by the improvement factor.

### Claim 5 (depends on 1)

The method of claim 1, wherein partitioning the training data into the plurality of training subsets using the improvement selection includes partitioning the training data into a number of training subsets, wherein the number of training subsets is selected using the improvement selection.

### Claim 6 (depends on 5)

The method of claim 5, wherein the training data includes imaging data for a plurality of acquisitions, and wherein partitioning the training data into the number of training subsets using the improvement selection includes associating imaging data for sequential acquisitions of the plurality of acquisitions to alternate training subsets of the plurality of training subsets.

### Claim 7 (depends on 6)

The method of claim 6, wherein sequential acquisitions of the plurality of acquisitions are acquired at different angles with respect to the sample.

### Claim 8 (depends on 7)

The method of claim 7, wherein each sequential acquisition is angularly offset from a previous acquisition by an angle determined using golden ratio angle determination techniques.

### Claim 9 (depends on 6)

The method of claim 6, wherein groups of sequential acquisitions of the plurality of acquisitions are acquired at common angles, and wherein each sequential acquisition of a group of sequential acquisitions is associated with a respective one of the plurality of training subsets.

### Claim 10 (depends on 1)

The method of claim 1, further comprising receiving a region of interest (ROI) selection, wherein partitioning the training data into the plurality of training subsets includes using the ROI selection.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
receiving category identification information associated with the sample; and
retrieving a pre-trained neural network using the category identification information, wherein the pre-trained neural network is trained on a different sample, wherein training the neural network includes further training the pre-trained neural network, and wherein generating the CT reconstructed volume using the additional imaging data and the trained neural network includes using the further trained pre-trained neural network.

### Claim 12 (depends on 1)

The method of claim 1, further comprising:
applying imaging corrections to the training data prior to generating the CT training reconstructed volumes; and
applying the imaging corrections to the additional imaging data prior to generating the CT reconstructed volume.

### Claim 13 (depends on 1)

The method of claim 1, further comprising:
determining that the trained neural network is insufficient;
updating the improvement selection when the trained neural network is determined to be insufficient;
repartitioning the training data into an updated plurality of training subsets using the updated improvement selection;
generating an updated CT training reconstructed volume for the at least two of the updated plurality of training subsets;
retraining the neural network using each of the updated CT training reconstructed volumes; and
generating an updated CT reconstructed volume using the additional imaging data and the retrained neural network.

### Claim 14 (depends on 13)

The method of claim 13, wherein determining that the trained neural network is insufficient includes:
presenting i) cost function value information; ii) the CT reconstructed volume; iii) a trial CT reconstructed volume generated using the trained neural network and the training data; or iv) any combination of i-iii; and
receiving user input indicative that the trained neural network is insufficient.

### Claim 15 (depends on 13)

The method of claim 13, wherein determining that the trained neural network is insufficient is performed automatically using a machine learning classifier.

### Claim 16 (depends on 1)

The method of claim 1, further comprising applying an angle-dependent weighting mask to the training data.

### Claim 17 (depends on 1)

The method of claim 1, further comprising truncating each of the CT training reconstructed volumes in a Z direction prior to training the neural network.

### Claim 18 (depends on 1)

The method of claim 1, further comprising:
receiving category identification information associated with the sample; and
storing the trained neural network in associated with the category identification information.

### Claim 19 (depends on 18)

The method of claim 18, further comprising:
transmitting the trained neural network via a network interface, wherein transmitting the trained neural network includes transmitting the category identification information;
receiving a collaboratively trained neural network via the network interface, wherein the collaboratively trained neural network is based on the trained neural network and one or more additional trained neural networks associated with the category identification information; and
storing the collaboratively trained neural network as a pre-trained neural network.

### Claim 20 (depends on 1)

A system comprising:
a control system including one or more processors; and
a memory having stored thereon machine readable instructions;
wherein the control system is coupled to the memory, and the method of claim 1 is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

### Claim at position 21

21-45. (canceled)