DATA GENERATION APPARATUS, DATA GENERATION METHOD, AND NONVOLATILE COMPUTER-READABLE STORAGE MEDIUM STORING DATA GENERATION PROGRAM

1 . A data generation apparatus comprising:

processing circuitry configured to:

receive a relative value of molecules in a first region as input data;

apply a function to the input data, the function having predetermined coefficients to be learned;

compute a relative value of the molecules in a second region to be output data from the function; and

output the output data.

2 . The data generation apparatus according to claim 1 , wherein the processing circuitry is further configured to:

receive a morphological image including the first region and the second region as input data, and

compute the relative value of the molecules in the second region by using the relative value of the molecules in the first region and the morphological image as input data.

3 . The data generation apparatus according to claim 1 , wherein

the input data includes data acquired by measuring a same subject.

4 . The data generation apparatus according to claim 1 , wherein

the relative value represents a relationship between the molecules in a chemical shift band.

5 . The data generation apparatus according to claim 4 , wherein

the relative value is obtained by dividing a density estimate of a first molecule by a density estimate of a second molecule among the molecules.

6 . The data generation apparatus according to claim 1 , wherein

the relative value of the molecules is an estimate obtained by using any of magnetic resonance spectroscopy, magnetic resonance spectroscopic imaging, and chemical exchange saturation transfer (CEST) imaging.

7 . The data generation apparatus according to claim 1 , wherein the processing circuitry is further configured to:

receive a morphological image including a two-dimensional or three-dimensional Ti weighted image, T 2 weighted image, fluid attenuated inversion recovery (FLAIR) image, T 2 * weighted image, diffusive weighted image, or proton density weighted image, and

compute the output data by applying the function and the relative value of the molecules in the first region to the morphological image.

8 . The data generation apparatus according to claim 1 , wherein

the second region includes a region different from the first region.

9 . The data generation apparatus according to claim 1 , wherein

the first region and the second region are set on different slices.

10 . The data generation apparatus according to claim 1 , wherein

the second region is the same as or larger than the first region in size.

11 . The data generation apparatus according to claim 1 , wherein

the coefficients are determined in such a manner that a loss function decreases, the loss function representing a difference between a result of applying the function to known training data and answer data relative to the known training data.

12 . The data generation apparatus according to claim 11 , wherein

the function is a data generative function trained by using a conditional generative adversarial network or a conditional variational autoencoder.

13 . The data generation apparatus according to claim 1 , wherein

the relative value represents a ratio between an integral signal value of a first molecule in a first chemical shift band and an integral signal value of a second molecule in a second chemical shift band among the molecules.

14 . The data generation apparatus according to claim 11 , wherein

the known training data and the answer data include a relative value of the molecules, the relative value being prepared prior to computation of the loss function.

15 . A data generation method comprising:

receiving a relative value of molecules in a first region as input data;

applying a function to the input data, the function having predetermined coefficients to be learned;

computing a relative value of the molecules in a second region to be output data from the function; and

outputting the output data.

16 . A nonvolatile computer-readable storage medium storing a data generation program which causes a computer to execute:

receiving a relative value of molecules in a first region as input data;

applying a function to the input data, the function having predetermined coefficients to be learned; computing a relative value of the molecules in a second region to be output data from the function; and

outputting the output data.