# US 10,945,685 B2 · System and method for normalizing standardized uptake values in brain positron emission tomography (PET) images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,945,685 B2 - Document ID: US10945685B2 - Status: Granted - Kind: B2 - Application number: 16/521,261 - Filed: 2019-07-24 - Published: 2020-01-30 - Granted: 2021-03-16 - Earliest claimed: 2018-07-25 - Assignees: New York University - Applicants: New York University - Inventors: Yi Li; Jingyun Chen; Mony De Leon - Web page: https://patentgrep.com/patent/US10945685B2 ## CPC - A61B 6/037: {Emission tomography} - G06T 5/90: Dynamic range modification of images or parts thereof - G01T 1/2985: {In depth localisation, e.g. using positron emitters; Tomographic imaging (longitudinal and transverse section imaging; apparatus for radiation diagnosis sequentially in different planes, steroscopic radiation diagnosis); (using external radiation sources A61B6/02)} - G06T 5/40: using histogram techniques - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 7/0012: {Biomedical image inspection} - A61B 6/501: {for diagnosis of the head, e.g. neuroimaging or craniography} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/30016: Brain - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01T: MEASUREMENT OF NUCLEAR OR X-RADIATION (radiation analysis of materials, mass spectrometry G01N23/00; tubes for determining the presence, intensity, density or energy of radiation or particles H01J47/00) ## Abstract Systems and methods are for analyzing Positron Emission Tomography (PET) image data. The methods may include generating a set of standardized uptake values (SUVs) of global or localized PET data for voxels within a selected region of interest (ROI), normalizing the set of SUVs by generating a set of SUVPs where each corresponding SUVP for each SUV is obtained using the formula: SUVP=(SUV−M)/S, wherein M corresponds to a peak value for the set of SUVs, and S corresponds to a spread for the set of SUVs, and generating a normalized image based on the set of SUVPs for the ROI. The systems may include any suitable device for PET image analysis performing the methods. ## Claims ### Claim 1 A method for analyzing Positron Emission Tomography (PET) image data comprising: localizing the PET data with at least one image mask to generate localized PET data, wherein the PET data is co-registered to correspond to anatomical structures represented by the image mask; generating a set of image intensity values (Ps) based on the localized PET data for voxels within a selected region of interest (ROI), each of the image intensity values corresponding to an intensity of the PET data for a corresponding one of the voxels; normalizing the set of image intensity values (Ps) by generating a set of normalized values (Ns) where each corresponding N for each P is obtained using the formula: N=(P−M)/S, wherein M corresponds to a peak value for the set of image intensity values (Ps), and S corresponds to a spread for the set of image intensity values (Ps); and generating a normalized image based on the set of normalized values (Ns). ### Claim 2 (depends on 1) The method of claim 1, further comprising fitting a Gaussian curve to a histogram of the set of image intensity values (Ps), wherein M is a mean of the fitted Gaussian curve, and S is a standard deviation of the fitted Gaussian curve. ### Claim 3 (depends on 1) The method of claim 1, wherein the ROI corresponds to a predetermined portion of a patient's body. ### Claim 4 (depends on 3) The method of claim 3, wherein the predetermined portion is an entire brain. ### Claim 5 (depends on 1) The method of claim 1, wherein the image intensity values (Ps) are standardized uptake values (SUVs). ### Claim 6 (depends on 5) The method of claim 5, wherein M is a peak of the set of SUVs, and S is a spread the SUVs. ### Claim 7 (depends on 1) The method of claim 1, wherein the image mask comprises anatomical image data. ### Claim 8 (depends on 1) A non-transitory computer-readable storage medium including a set of instructions executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform steps of claim 1. ### Claim 9 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the set of instruction further comprises instructions, when executed by the processor, causes the processor to control an imaging device configured to detect radioactive emissions from a patient for a predetermined period of time, and causes the processor to generate the PET data based on the radioactive emissions detected by the device. ### Claim 10 A Positron Emission Tomography (PET) image system, comprising: an imaging device configured to detect radioactive emissions from a patient for a predetermined period of time, the device further configured to generate PET data based on the detected radioactive emissions; a memory storage device configured to store the PET data; and a processing arrangement configured to localize the PET data with at least one image mask to generate localized PET data, the PET data be co-registered to correspond to anatomical structures represented by the image mask, the processing arrangement further configured to generate a set of image intensity values (Ps) based on the localized PET data for voxels within a selected region of interest (ROI), each of the image intensity values corresponding to an intensity of the PET data for a corresponding one of the voxels, normalize the set of image intensity values (Ps) by generating a set of normalized values (Ns) where each corresponding N for each P is obtained using the formula: N=(P−M)/S, wherein M corresponds to a peak value for the set of image intensity values (Ps), and S corresponds to a spread for the set of image intensity values (Ps), and the processing arrangement further configured to generate a normalized PET image based on the set of normalized values (Ns).