# US 2014/0153807 A1 · Apparatus and method for acquiring multi-parametric images in magnetic resonance imaging device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0153807 A1 - Document ID: US20140153807A1 - Status: Published application - Kind: A1 - Application number: 14/094,019 - Filed: 2013-12-02 - Published: 2014-06-05 - Earliest claimed: 2012-11-30 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Yeong-Kyeong SEONG; Jong-Ha LEE; Won-Hee CHOE - Web page: https://patentgrep.com/patent/US20140153807A1 ## CPC - G06T 7/0016: {involving temporal comparison} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)} - G01R 33/5602: {by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse} - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/30004: Biomedical image processing - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) ## Abstract There are provided an apparatus and a method for acquiring multi-parametric images from an MRI device. In one general aspect, the apparatus for acquiring multi-parametric images includes an image analyzer configured to determine a significance level of each of a plurality of multi-parametric images relating to a disease, and to determine an acquisition order of the multi-parametric images relating to the disease; and a model constructer configured to construct an acquisition model of the multi-parametric images based on the acquisition order and the multi-parametric images to be used in diagnosing the disease. ## Claims ### Claim 1 An apparatus for acquiring multi-parametric images, the apparatus comprising: an image analyzer configured to determine a significance level of each of a plurality of multi-parametric images relating to a disease, and to determine an acquisition order of the multi-parametric images and the multi-parametric images to be used in diagnosing the disease; and a model constructer configured to construct an acquisition model of the multi-parametric images based on the acquisition order and the multi-parametric images to be used in diagnosing the disease. ### Claim 2 (depends on 1) The apparatus of claim 1, further comprising: an image acquirer configured to acquire the multi-parametric images using the constructed acquisition model of the multi-parametric images. ### Claim 3 (depends on 1) The apparatus of claim 1, further comprising a model store configured to store the acquisition model of the multi-parametric images. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein: the image analyzer comprises a Region of Interest (ROI) extractor configured to extract an ROI from each of the plurality of multi-parametric images, and a feature extractor configured to extract a feature from the ROI; and the image analyzer is further configured to determine the significance level of each of the plurality of multi-parametric images based on the extracted features. ### Claim 5 (depends on 3) The apparatus of claim 3, further comprising a feature analyzer configured to determine whether the extracted feature is significant. ### Claim 6 (depends on 4) The apparatus of claim 4, further comprising: an image optimizer configured to normalize the ROI; and the feature extractor extracts the feature from the normalized ROI. ### Claim 7 (depends on 6) The apparatus of claim 6, wherein the image optimizer is further configured to remove noise from the normalized ROI, and the feature extractor extracts the feature from the normalized ROI where the noise is removed. ### Claim 8 (depends on 4) The apparatus of claim 4, wherein the features comprises one or more pieces of information about shape, brightness, or boundary. ### Claim 9 (depends on 5) The apparatus of claim 5, wherein the image analyzer determines the significance level of each of the plurality of multi-parametric images based on a number of significant features in each multi-parametric image for diagnosing the disease. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the number of significant features is determined based on frequency of the features in multi-parametric images of patients suffering from a same disease. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein the image analyzer determines that the multi-parametric image is to be used in diagnosing the disease when the number of significant features in the multi-parametric image exceeds a threshold. ### Claim 12 (depends on 4) The apparatus of claim 4, further comprising an image store configured to store the multi-parametric images. ### Claim 13 A method for acquiring multi-parametric images, the method comprising: determining, at an image analyzer, a significance level of each of a plurality of multi-parametric images relating to a disease; determining, based on the determined significance levels, an acquisition order of the multi-parametric images and the multi-parametric images to be used in diagnosing the disease; and constructing an acquisition model of the multi-parametric images based on the acquisition order and the multi-parametric images to be used in diagnosing the disease. ### Claim 14 (depends on 13) The method of claim 13, further comprising: acquiring the multi-parametric images for diagnosing the disease, by using the constructed acquisition model of the multi-parametric images. ### Claim 15 (depends on 13) The method of claim 13, wherein the determining of the significance level comprises: extracting a ROI from each of the multi-parametric images; extracting a feature from the ROI; and determining the significance level of each of the multi-parametric images based on the extracted feature. ### Claim 16 (depends on 15) The method of claim 15, wherein the determining of the significance level comprises normalizing the ROI, and extracting the feature from the normalized ROI. ### Claim 17 (depends on 16) The method of claim 16, wherein the determining of the significance level further comprises removing noise from the normalized ROI, and extracting the feature from the normalized ROI where noise is removed. ### Claim 18 (depends on 15) The method of claim 15, wherein the features comprises one or more pieces of information about shape, brightness, or boundary. ### Claim 19 (depends on 15) The method of claim 15, wherein the determining of a significance level comprises determining a significance level of each of the plurality of multi-parametric images based on a number of significant features in each multi-parametric image. ### Claim 20 (depends on 19) The method of claim 19, wherein the number of significant features is determined based on frequency of the features in multi-parametric image of multiple patients suffering from a same disease. ### Claim 21 (depends on 19) The method of claim 19, wherein the determining of the significance level comprises, using each multi-parametric image in diagnosing the disease when the number of significant features in the multi-parametric image exceeds a threshold. ### Claim 22 (depends on 19) The method of claim 19, wherein the determining an acquisition order further comprises: assigning a weighted value to each of the multi-parametric images; determining the acquisition order of the multi-parametric images by multiplying the weighted value by the number of significant features in the multi-parametric image. ### Claim 23 An apparatus for acquiring multi-parametric images, the apparatus comprising: an image analyzer configured to determine an acquisition order of the multi-parametric images and the multi-parametric images to be used in diagnosing the disease; a model constructer configured to construct an acquisition model of the multi-parametric images based on the acquisition order and the multi-parametric images to be used in diagnosing the disease; and an image acquirer configured to acquire the multi-parametric images based on the acquisition model. ### Claim 24 (depends on 23) The apparatus of claim 23, further comprising a model store configured to store the acquisition model of the multi-parametric images. ### Claim 25 (depends on 23) The apparatus of claim 23, wherein the image analyzer comprises: a Region of Interest (ROI) extractor configured to extract an ROI from each of the plurality of multi-parametric images; a feature extractor configured to extract a feature from the ROI; and a feature analyzer configured to determine whether the extracted feature is significant. ### Claim 26 (depends on 25) The apparatus of claim 25, wherein: the image analyzer is configured to determine a significance level of each of the plurality of multi-parametric images based on the based on a number of significant features in each multi-parametric image; and the image analyzer is configured to determine the acquisition order and the multi-parametric images to be used in diagnosing the disease based on the significance level of each of the plurality of multi-parametric images. ### Claim 27 (depends on 25) The apparatus of claim 25, further comprising: an image optimizer configured to normalize the ROI; and the feature extractor extracts the feature from the normalized ROI.