Image diagnosis support device, image diagnosis support program, and medical image acquisition apparatus

1. An image diagnosis support device comprising,

a processor;

a display coupled to the processor;

a memory coupled to the processor, the memory storing instructions that when executed by the processor configure the processor to:

receive various types of measured values at a plurality of positions within a diagnosis target of a living body,

divide the measured values into a plurality of groups depending on the position or the type of the measured value,

calculate a plurality of values of an intermediate index, where each value of the intermediate index is a diagnosis index of each of the plurality of the groups calculated from the measured values included in the group on a per-group basis, and

calculate a comprehensive index, as a diagnosis index of the diagnosis target, by merging from values of the intermediate index calculated on a per-group basis,

display a part or all of values of the calculated intermediate index in the form of numbers or in the form of images.

2. The image diagnosis support device according to claim 1 ,

wherein the measured value is any of the following: an original image, quantitative values calculated from the original image, a quantitative image having the quantitative values as pixel values, and statistics calculated from the original image or the quantitative image.

3. The image diagnosis support device according to claim 1 ,

wherein the intermediate index and the comprehensive index include any of the following: presence or absence of a disease or the probability of the disease, a degree of disease progression, and presence or absence of abnormality or the probability of the abnormality.

4. The image diagnosis support device according to claim 3 ,

wherein the intermediate index and the comprehensive index indicate an identical index.

5. The image diagnosis support device according to claim 3 ,

wherein the intermediate index and the comprehensive index indicate different indexes.

6. The image diagnosis support device according to claim 1 ,

wherein the processor is configured to calculate the intermediate index on the basis of learning data measured from a second living body different from the living body as to which the measured-value receiving unit receives the measured values.

7. The image diagnosis support device according to claim 6 ,

wherein the processor is configured to calculate the intermediate index by using a neural network comprising a function having a coefficient and a constant defined according to machine learning, or comprising a combination of more than one function having the coefficient and the constant defined according to the machine learning.

8. The image diagnosis support device according to claim 1 ,

wherein the processor is configured to: divide the measured values into a plurality of groups, and further divide the plurality of groups into a second plurality of groups,

calculate a first intermediate index on a per-group basis of the plurality of groups generated by the first group generator, by using the measured values included in the groups, and calculate a second intermediate index from the second plurality of groups generated by the second group generator, by using values of the first intermediate index, and

calculate a comprehensive index by using values of the second intermediate index.

9. The image diagnosis support device according to claim 8 ,

wherein the processor is configured to generate the plurality of groups, so that the measured values of the same type at the same position are put into one group, and the generate the second plurality of groups, by grouping the plurality of groups generated by the first group generator into one group, in such a manner that the groups having the measured values at the same position are put into one group, or the groups having the measured values of the same type are put into one group.

10. The image diagnosis support device according to claim 8 ,

wherein the processor is configured to generate groups by partitioning the measured values into a plurality of small areas, on the basis of the position, and

generate groups by merging and then partitioning the small areas into large areas.

11. A medical image acquisition apparatus, comprising

a gradient coil;

RF coil;

an RF magnetic field generator configured to drive the RF coil;

a receiver configured to detect echo signals;

a sequencer configured to send commands to the gradient power supply and to the RF magnetic field generator;

a processor;

a memory coupled to the processor, the memory storing instructions that when executed by the processor configure the processor to:

reconstruct the image of a subject by using the echo signals,

calculate various types of images at a plurality of positions within a diagnosis target of the subject, or statistics of the images, and

present an index related to a disease of the subject by using measured values,

divide the measured values into a plurality of groups depending on the position or the type of the measured value,

calculate a plurality of values of an intermediate index, where each value of the intermediate index is a diagnosis index of each of the plurality of the groups calculated from the measured values included in the group on a per-group basis,

calculate a comprehensive index, as a diagnosis index of the diagnosis target, by merging from values of the intermediate index calculated on a per-group basis, and

displaying, on a display, a part or all of values of the calculated intermediate index in the form of numbers or in the form of images.

12. The medical image acquisition apparatus, according to claim 11 ,

wherein

the measured values include a weighted image or a quantitative image, being obtained by performing computations on the nuclear magnetic resonance signals, or statistics of the weighted image or the quantitative image, being calculated from pixel values within a predetermined area.

13. The medical image acquisition apparatus, according to claim 12 ,

wherein the measured values include any of proton density, longitudinal relaxation time, longitudinal reflexivity, transverse relaxation time, transverse reflexivity, diffusion coefficient, a flow rate, blood flow volume, magnetic susceptibility, modulus of elasticity, concentration of contrast agent, a ratio of gray matter, a ratio of white matter, and a ratio of cerebrospinal fluid, or statistics calculated from the measured values.