# US 12,118,649 B2 · Deep learning based three-dimensional reconstruction method for low-dose PET imaging

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,118,649 B2
- Document ID: US12118649B2
- Status: Granted
- Kind: B2
- Application number: 17/616,161
- Filed: 2021-01-23
- Published: 2022-12-01
- Granted: 2024-10-15
- Earliest claimed: 2020-02-11
- Assignees: Zhejiang Lab; Minfound Medical Systems Co., Ltd
- Applicants: Zhejiang Lab; Minfound Medical Systems Co., Ltd
- Inventors: Wentao Zhu; Bao Yang; Long Zhou; Hongwei Ye; Ling Chen; Fan Rao; Yaofa Wang
- Web page: https://patentgrep.com/patent/US12118649B2

## CPC

- G06T 12/20: Inverse problem, i.e. transformations from projection space into object space
- G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction
- G06T 2211/421: Filtered back projection [FBP]
- G06T 2211/441: AI-based methods, deep learning or artificial neural networks
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

Disclosed is a three-dimensional low-dose PET reconstruction method based on deep learning. The method comprises the following steps: back projecting low-dose PET raw data to the image domain to maintain enough information from the raw data; selecting an appropriate three-dimensional deep neural network structure to fit the mapping between the back projection of the low-dose PET and a standard-dose PET image; after learning from the training samples the network parameters are fixed, realizing three-dimensional PET image reconstruction starting from low-dose PET raw data, thereby obtaining a low-dose PET reconstructed image which has a lower noise and a higher resolution compared with the traditional reconstruction algorithm and image domain noise reduction processing.

## Claims

### Claim 1

A three-dimensional low-dose PET reconstruction method based on deep learning, comprising the following steps:
(1) performing a back projection method for low-dose PET raw data, which specifically comprises the following sub-steps:
(1.1) performing attenuation correction processing on the low-dose PET raw data, and obtaining lpp_ac, a back projection of the low-dose PET data after attenuation correction through being subjected to the transposed system matrix of a PET scanner;
(1.2) obtaining lrs, a back projection of random and scatter data by subjecting the random and scatter data of the low-dose PET raw data to a transposed system matrix of the PET scanner;
(1.3) simulating an all-1 PET image, subjecting the all-1 PET image to a system matrix of the PET scanner to obtain a three-dimensional projection, and then back projecting a result of the three-dimensional projection to the image domain to obtain l1, a back projection of the all-1 PET image;
(1.4) subtracting lrs_ac, the back projection of the random and scatter data obtained in step (1.2) from lpp_wc, the back projection of the low-dose PET data after attenuation correction obtained in step (1.1) to implement random and scatter correction, and then dividing by l1, the back projection of the all-1 PET image to obtain a corrected and regularized three-dimensional low-dose PET back projection lbp:
l
b
⁢
p
=
l
pp_ac
-
l
r
⁢
s
l
1
(
1
)
(2) taking lbp, the corrected and regularized three-dimensional low-dose PET back projection obtained in step (1.4), as an input of a deep neural network, taking a reconstructed standard-dose PET image as a label of the network, updating parameters of the deep neural network through Adam optimization algorithm, minimizing a target loss function, and completing training of the deep neural network, wherein the target loss function of training the deep neural network is as follows:
L
=
1
N
x
⁢
N
y
⁢
N
z
⁢
∑
i
=
1
N
x
∑
j
=
1
N
y
∑
k
=
1
N
z
❘
"\[LeftBracketingBar]"
C
⁢
(
l
bp
⁢
(
i
,
j
,
k
)
)
-
f
full
⁢
(
i
,
j
,
k
)
❘
"\[RightBracketingBar]"
(
2
)
where Ny, Ny, and Nz represent total numbers of pixels of the low-dose PET back projection or the standard-dose PET image in horizontal, vertical and axial directions, respectively, C(·) represents mapping from the low-dose PET back projection lbp to the standard-dose PET reconstructed image ffull fitted by a three-dimensional deep neural network, and (i,j,k) represents pixels in the image; and
(3) performing the back projection method in step 1 on newly acquired low-dose PET raw data, and inputting the data into the deep neural network trained in step 2 to obtain a corresponding low-dose PET reconstructed image.