# US 2021/0333346 A1 · MRI data transforming device including an transforming network using an MRI data and additional information and an MRI data transforming method using the same > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0333346 A1 - Document ID: US20210333346A1 - Status: Published application - Kind: A1 - Application number: 16/792,932 - Filed: 2020-04-27 - Published: 2021-10-28 - Earliest claimed: 2020-04-27 - Assignees: Seoul National University R&Db Foundation - Applicants: Seoul National University R&Db Foundation - Inventors: Jongho LEE; Woojin JUNG - Web page: https://patentgrep.com/patent/US20210333346A1 ## CPC - G01R 33/546: {Interface between the MR system and the user, e.g. for controlling the operation of the MR system or for the design of pulse sequences} - 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 33/543: {Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription (G01R33/546 takes precedence)} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Provided is an MRI data transform device including a transform network unit configured to output prescribed transform information on the basis of MRI data output from an MRI scanner. The MRI data and information about device characteristics of the MRI scanner are input to the transform network unit. ## Claims ### Claim 1 An MRI data transform device comprising: a transform network unit configured to output prescribed transformed information on a basis of MRI data output from an MRI scanner, wherein, in order to output the transformed information, the MRI data and information about device characteristics of the MRI scanner are provided to the transform network unit. ### Claim 2 (depends on 1) The MRI data transform device of claim 1, wherein the information about the device characteristics comprises a data characteristic value calculated from the MRI data in a predetermined manner. ### Claim 3 (depends on 1) The MRI data transform device of claim 1, wherein the information about the device characteristics comprises a user setting parameter of the MRI scanner. ### Claim 4 (depends on 1) The MRI data transform device of claim 1, wherein the information about the device characteristics comprises a structure parameter representing a specification of a hardware or a software constituting the MRI scanner. ### Claim 5 (depends on 1) The MRI data transform device of claim 1, wherein the transform network unit comprises a multilayer neural network, and the information about the device characteristics is input to a hidden layer of the neural network. ### Claim 6 (depends on 5) The MRI data transform device of claim 5, wherein the information about the device characteristics is input a set of nodes forming the hidden layer, and the information input to the set of nodes comprises only information about the device characteristics. ### Claim 7 (depends on 6) The MRI data transform device of claim 6, wherein a number of nodes constituting a third layer connected to an outbound link of an arbitrary node among the one set of nodes is smaller than a number of nodes constituting an input layer of the neural network. ### Claim 8 (depends on 2) The MRI data transform device of claim 2, wherein the MRI data is image data, and the data characteristic value comprises one or more among an SNR, a contrast, and an aliasing pattern of the image data. ### Claim 9 (depends on 1) The MRI data transform device of claim 1, further comprising: a parameter conversion unit, wherein the transform network unit comprises a multilayer neural network, and the parameter conversion unit is configured to acquire a user setting parameter of the MRI scanner, to convert the acquired user setting parameter, and to provide the converted user setting parameter to a hidden layer of the neural network. ### Claim 10 (depends on 1) The MRI data transform device of claim 1, wherein, the information about the device characteristics comprises a user setting parameter of the MRI scanner, and the MRI data transform device further comprises: a dimension conversion unit configured to convert a dimension of the user setting parameter to output a first dimension conversion value; and a physical characteristic application unit configured to convert the first dimension conversion value on a basis of a first conversion function determined according to a physical meaning of the user setting parameter to output a first conversion parameter. ### Claim 11 (depends on 3) The MRI data transform device of claim 3, wherein the user setting parameter comprises a scan parameter of the MRI scanner. ### Claim 12 An MRI data transform method comprising: determining, by a user setting parameter determination unit of an MRI data transform device, user setting parameters of an MRI scanner; scanning, by the MRI scanner of the MRI data transform device, a given scan target to output MRI data; extracting, a characteristic extraction unit of the MRI data transform device, a data characteristic value of the MRI data; and inputting, by the MRI data transform device, not only the outputted MRI data but also one or more among the determined user setting parameters and the extracted data characteristic value to a trained transform network unit, and outputting prescribed transformed information from the trained transform network unit. ### Claim 13 (depends on 12) The MRI data transform method of claim 12, wherein the MRI data transform device has been trained by a training method, wherein the training method comprises: determining, by the user setting parameter determination unit of the MRI data transform device, the user setting parameters of the MRI scanner; scanning, by the MRI scanner of the MRI data transform device, the given scan target to output the MRI data; extracting, by the characteristic extraction unit of the MRI data transform device, the data characteristic value of the MRI data; and supervised-training, by the transform network unit of the MRI data transform device, using the output MRI data and a previously prepared label corresponding to the outputted MRI data, wherein input training data for the supervised-training comprises not only the outputted MRI data, but also one or more among the determined user setting parameters and the extracted data characteristic value. ### Claim 14 An MRI data transform device comprising: a transform network unit configured to output prescribed transform information transformed from first MRI data on a basis of the first MRI data, which is output by a first MRI scanner that has scanned a scan target, wherein, for outputting the transform information, the MRI data transform device provides the MRI data and information about device characteristics of the MRI scanner to the transform network unit, wherein the device characteristics of the first MRI scanner is formed of parameter values causing a difference between the first MRI data and second MRI data, wherein the second MRI data is outputted by a second MRI scanner which has scanned the scan target, wherein the second MRI scanner is different from the first MRI scanner. ### Claim 15 (depends on 14) The MRI data transform device of claim 14, wherein the parameter values comprise one or more among a user setting parameter for determining an operation state of the first MRI data, a structure parameter representing a specification of a first hardware constituting the first MRI scanner, and a data characteristic value acquired from the first MRI data. ### Claim 16 (depends on 2) The MRI data transform device of claim 2, wherein the information about the device characteristics comprises a user setting parameter of the MRI scanner. ### Claim 17 (depends on 2) The MRI data transform device of claim 2, wherein the information about the device characteristics comprises a structure parameter representing a specification of a hardware or a software constituting the MRI scanner.