# US 12,183,003 B2 · PET quantitative localization system and operation method thereof

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- Number: US 12,183,003 B2
- Document ID: US12183003B2
- Status: Granted
- Kind: B2
- Application number: 17/452,238
- Filed: 2021-10-26
- Published: 2022-12-15
- Granted: 2024-12-31
- Earliest claimed: 2021-06-15
- Assignees: Taipei Medical University (TMU); Taipei Veterans General Hospital
- Applicants: Taipei Medical University (TMU); Taipei Veterans General Hospital
- Inventors: Syu-Jyun Peng; Hsiang-Yu Yu; Yen-Cheng Shih; Tse-Hao Lee
- Web page: https://patentgrep.com/patent/US12183003B2

## CPC

- G06T 7/0014: {using an image reference approach}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)}
- A61B 6/037: {Emission tomography}
- A61B 6/4417: {related to combined acquisition of different diagnostic modalities}
- A61B 6/501: {for diagnosis of the head, e.g. neuroimaging or craniography}
- A61B 6/5205: {involving processing of raw data to produce diagnostic data}
- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical 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}
- G06T 5/50: using two or more images, e.g. averaging or subtraction
- G06T 5/70: Denoising; Smoothing
- G06T 7/11: Region-based segmentation
- G06T 7/136: involving thresholding
- G06T 7/30: Determination of transform parameters for the alignment of images, i.e. image registration
- A61B 5/0035: {adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography}
- A61B 5/0042: {for the brain}
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10104: Positron emission tomography [PET]
- G06T 2207/20081: Training; Learning
- G06T 2207/20128: Atlas-based segmentation
- G06T 2207/30016: Brain
- 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

The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.

## Claims

### Claim 1

An PET (positron emission tomography) quantitative localization system, comprising:
a memory circuit configured to store at least one instruction; and
a processor coupled to the memory circuit, and the processor configured to access and execute the at least one instruction for:
acquiring a PET image and a MRI (magnetic resonance imaging) of at least one patient;
performing a nonlinear deformation on the MRI and a T1 template to generate a deformation information parameter;
deforming an AAL (automated anatomical labeling) atlas to an individual brain space of the at least one patient through the deformation information parameter, so as to generate an individual brain space AAL atlas, wherein the AAL atlas and the T1 template are in a same space;
calculating a plurality of lateralization indexes of a plurality of ROIs (regions of interest) of the individual brain space AAL atlas corresponding to the PET image processed through an intensity normalization;
inputting the lateralization indexes into at least one machine learning model, so as to analyze a result of determining a target;
linearly co-registering the PET image to the MRI, so as to generate a co-registered PET image;
smoothing the co-registered PET image to generate a smoothed PET image;
performing a brain tissue segmentation on the MRI to obtain a whole brain gray matter density image;
binarizing the whole brain gray matter density image by a threshold value to generate a whole brain gray matter mask, wherein the threshold value is 0.5; and
performing the intensity normalization on the smoothed PET image to obtain the PET image processed through the intensity normalization based on an average of PET standardized uptake values corresponding to the whole brain gray matter mask.

### Claim 2 (depends on 1)

The PET quantitative localization system of claim 1, wherein the ROIs comprises a plurality of left brain ROIs and a plurality of right brain ROIs, and the processor accesses and executes the at least one instruction for:
selecting a plurality of left-side standardized uptake values corresponding to the left brain ROIs and a plurality of right-side standardized uptake values corresponding to the right brain ROIs from the PET image processed through the intensity normalization; and
calculating the lateralization indexes based on a difference between the left standardized uptake values and the right standardized uptake values.

### Claim 3 (depends on 2)

The PET quantitative localization system of claim 2, wherein each of the lateralization indexes satisfies a following calculation:
2×(the left standardized uptake value−the right standardized uptake value)÷(the left standardized uptake value+the right standardized uptake value).

### Claim 4 (depends on 1)

The PET quantitative localization system of claim 1, wherein the PET image and the MRI of the at least one patient comprises a plurality of PET images and a plurality of MRIs of a plurality of patients, the at least one machine learning model comprises a plurality of different machine learning models, and the processor accesses and executes the at least one instruction for:
inputting the lateralization indexes of each of the patients into the different machine learning models, and selecting one machine learning model having a highest accuracy of determining the target from the different machine learning models.

### Claim 5

An operation method of a PET quantitative localization system, and the operation method comprising steps of:
acquiring a PET image and a MRI of at least one patient;
performing a nonlinear deformation on the MRI and a T1 template to generate a deformation information parameter;
deforming an AAL atlas to an individual brain space of the at least one patient through the deformation information parameter, so as to generate an individual brain space AAL atlas, wherein the AAL atlas and the T1 template are in a same space;
calculating a plurality of lateralization indexes of a plurality of ROIs of the individual brain space AAL atlas corresponding to the PET image processed through an intensity normalization;
inputting the lateralization indexes into at least one machine learning model, so as to analyze a result of determining a target;
linearly co-registering the PET image to the MRI, so as to generate a co-registered PET image;
smoothing the co-registered PET image to generate a smoothed PET image;
performing a brain tissue segmentation on the MRI to obtain a whole brain gray matter density image;
binarizing the whole brain gray matter density image by a threshold value to generate a whole brain gray matter mask, wherein the threshold value is 0.5; and
performing the intensity normalization on the smoothed PET image to obtain the PET image processed through the intensity normalization based on an average of PET standardized uptake values corresponding to the whole brain gray matter mask.

### Claim 6 (depends on 5)

The operation method of claim 5, wherein the ROIs comprises a plurality of left brain ROIs and a plurality of right brain ROIs, and the step of calculating the lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image processed through the intensity normalization comprises:
selecting a plurality of left-side standardized uptake values corresponding to the left brain ROIs and a plurality of right-side standardized uptake values corresponding to the right brain ROIs from the PET image processed through the intensity normalization; and
calculating the lateralization indexes based on a difference between the left standardized uptake values and the right standardized uptake values.

### Claim 7 (depends on 6)

The operation method of claim 6, each of the lateralization indexes satisfies a following calculation:
2×(the left standardized uptake value−the right standardized uptake value)÷(the left standardized uptake value+the right standardized uptake value).

### Claim 8 (depends on 5)

The operation method of claim 5, wherein the PET image and the MRI of the at least one patient comprises a plurality of PET images and a plurality of MRIs of a plurality of patients, the at least one machine learning model comprises a plurality of different machine learning models, and the step of inputting the lateralization indexes into the at least one machine learning model comprises:
inputting the lateralization indexes of each of the patients into the different machine learning models, and selecting one machine learning model having a highest accuracy of determining the target from the different machine learning models.

### Claim 9

A non-transitory computer readable medium to store a plurality of instructions for commanding a computer to execute an operation method, and the operation method comprising steps of:
acquiring a PET image and a MRI of at least one patient;
performing a nonlinear deformation on the MRI and a T1 template to generate a deformation information parameter;
deforming an AAL atlas to an individual brain space of the at least one patient through the deformation information parameter, so as to generate an individual brain space AAL atlas, wherein the AAL atlas and the T1 template are in a same space;
calculating a plurality of lateralization indexes of a plurality of ROIs of the individual brain space AAL atlas corresponding to the PET image processed through an intensity normalization;
inputting the lateralization indexes into at least one machine learning model, so as to analyze a result of determining a target;
linearly co-registering the PET image to the MRI, so as to generate a co-registered PET image;
smoothing the co-registered PET image to generate a smoothed PET image;
performing a brain tissue segmentation on the MRI to obtain a whole brain gray matter density image;
binarizing the whole brain gray matter density image by a threshold value to generate a whole brain gray matter mask, wherein the threshold value is 0.5; and
performing the intensity normalization on the smoothed PET image to obtain the PET image processed through the intensity normalization based on an average of PET standardized uptake values corresponding to the whole brain gray matter mask.

### Claim 10 (depends on 9)

The non-transitory computer readable medium of claim 9, wherein the ROIs comprises a plurality of left brain ROIs and a plurality of right brain ROIs, and the step of calculating the lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image processed through the intensity normalization comprises:
selecting a plurality of left-side standardized uptake values corresponding to the left brain ROIs and a plurality of right-side standardized uptake values corresponding to the right brain ROIs from the PET image processed through the intensity normalization; and
calculating the lateralization indexes based on a difference between the left standardized uptake values and the right standardized uptake values.

### Claim 11 (depends on 10)

The non-transitory computer readable medium of claim 10, each of the lateralization indexes satisfies a following calculation:
2×(the left standardized uptake value−the right standardized uptake value)÷(the left standardized uptake value+the right standardized uptake value).

### Claim 12 (depends on 9)

The non-transitory computer readable medium of claim 9, wherein the PET image and the MRI of the at least one patient comprises a plurality of PET images and a plurality of MRIs of a plurality of patients, the at least one machine learning model comprises a plurality of different machine learning models, and the step of inputting the lateralization indexes into the at least one machine learning model comprises:
inputting the lateralization indexes of each of the patients into the different machine learning models, and selecting one machine learning model having a highest accuracy of determining the target from the different machine learning models.