# US 2022/0284643 A1 · Methods of estimation-based segmentation and transmission-less attenuation and scatter compensation in nuclear medicine imaging

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- Number: US 2022/0284643 A1
- Document ID: US20220284643A1
- Status: Published application
- Kind: A1
- Application number: 17/187,727
- Filed: 2021-02-26
- Published: 2022-09-08
- Earliest claimed: 2021-02-26
- Assignees: Washington University
- Inventors: Abhinav Kumar Jha; Ziping Liu; Hae Sol Moon; Zitong Yu; Md Ashequr Rahman
- Web page: https://patentgrep.com/patent/US20220284643A1

## CPC

- G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction
- A61B 6/037: {Emission tomography}
- A61B 6/5205: {involving processing of raw data to produce diagnostic data}
- A61B 6/5247: {combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound}
- A61B 6/5282: {due to scatter}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 12/30: Image post-processing, e.g. metal artefact correction
- G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V 20/64: Three-dimensional [3D] objects
- G06V 20/698: {Matching; Classification}
- G06T 2207/10104: Positron emission tomography [PET]
- G06T 2207/10108: Single photon emission computed tomography [SPECT]
- G06T 2207/20081: Training; Learning
- G06T 2207/30016: Brain
- G06T 2207/30096: Tumor; Lesion
- G06T 2211/424: Iterative
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Among the various aspects of the present disclosure is the provision of methods for estimation-based segmentation of nuclear medicine images, as well as methods of transmission-less attenuation and scatter compensation of nuclear medicine images.

## Claims

### Claim 1

A computer-implemented method for segmenting a nuclear medicine image, the method comprising transforming, using a computing device, a nuclear medicine image dataset into a segmented nuclear medicine image dataset using a deep learning network, wherein the segmented nuclear medicine image dataset comprises a plurality of voxels, each voxel associated with at least one voxel volume fraction, each voxel volume fraction indicative of a fraction classified as one tissue type within each voxel.

### Claim 2 (depends on 1)

The method of claim 1, wherein the deep learning network is configured to minimize a binary cross-entropy (BCE) of a Bayesian cost function to estimate the posterior mean of the one tissue type within each voxel.

### Claim 3 (depends on 2)

The method of claim 2, wherein the deep learning network comprises an autoencoder-decoder architecture.

### Claim 4 (depends on 3)

The method of claim 3, further comprising training, using the computing device, the deep learning network using a training dataset comprising a plurality of segmented MRI images and a corresponding plurality of segmented nuclear medicine images.

### Claim 5 (depends on 4)

The method of claim 4, wherein the nuclear medicine image is selected from the group consisting of a PET image and a SPECT image.

### Claim 6

A computer-implemented method for performing transmission-less attenuation and scatter compensation (ASC) on a nuclear medicine image, the method comprising:
a. reconstructing, using a computing device, a scatter window dataset corresponding to the nuclear medicine image to obtain a preliminary attenuation map;
b. transforming, using the computing device, the preliminary attenuation map to a final estimated attenuation map by segmenting the preliminary attenuation map using a deep learning network; and
c. reconstructing, using the computing device, the nuclear medicine image based on a photopeak window associated with the nuclear medicine image in combination with the final estimated attenuation map.

### Claim 7 (depends on 6)

The method of claim 6, wherein the deep learning network is configured to minimize a binary cross-entropy (BCE) of a Bayesian cost function to estimate the posterior mean of the one tissue type within each voxel of the preliminary attenuation map.

### Claim 8 (depends on 7)

The method of claim 7, wherein the deep learning network comprises an autoencoder-decoder architecture.

### Claim 9 (depends on 8)

The method of claim 8, further comprising training, using the computing device, the deep learning network using a training dataset comprising a plurality of segmented MRI images and a corresponding plurality of segmented nuclear medicine images.

### Claim 10 (depends on 9)

The method of claim 9, wherein the nuclear medicine image is selected from the group consisting of a PET image and a SPECT image.