Systems and methods for predicting image quality scores of images by medical imaging scanners
Systems and methods are disclosed for identifying image acquisition parameters. One method includes receiving a patient data set including one or more reconstructions, one or more preliminary scans or patient information, and one or more acquisition parameters; computing one or more patient characteristics based on one or both of one or more preliminary scans and the patient information; computing one or more image characteristics associated with the one or more reconstructions; grouping the patient data set with one or more other patient data sets using the one or more patient characteristics; and identifying one or more image acquisition parameters suitable for the patient data set using the one or more image characteristics, the grouping of the patient data set with one or more other patient data sets, or a combination thereof.
1. A computer-implemented method of predicting image quality scores of images for use in operating a medical imaging scanner, the method comprising:
identifying one or more data acquisition device types;
receiving one or more data sets associated with each of the one or more data acquisition device types, wherein each data set of the one or more data sets includes one or more patient characteristics;
receiving a selected data acquisition device type for an imaging procedure; and
determining, based on the patient characteristics of the received one or more data sets and the imaging procedure, a predicted image quality score for an image produced by the selected data acquisition device type.
2. The method of claim 1 , wherein further comprising:
determining a patient characteristic of a selected patient; and
determining the predicted image quality score further based on the patient characteristic of the selected patient.
3. The method of claim 1 , further comprising:
determining one or more recommended image acquisition parameters associated with the predicted image quality score.
4. The method of claim 3 , further comprising:
determining the one or more recommended image acquisition parameters using machine learning.
5. The method of claim 1 , further comprising:
determining the predicted image quality score based on one or more imaging operator characteristics, one or more image characteristics, or one or more effects of imaging, wherein the one or more effects of imaging includes radiation exposure.
6. The method of claim 1 , wherein the predicted image quality score is determined using machine learning.
7. The method of claim 1 , further comprising:
grouping the one or more data sets based on similarities between the patient characteristics respective to each of the one or more data sets; and
determining the predicted image quality score based on the grouping.
8. The method of claim 1 , further comprising:
initiating or instructing production of an image based on the predicted image quality score.
9. A system for predicting image quality scores of images for use in operating a medical imaging scanner, the system comprising:
a data storage device storing instructions for determining predicted image quality scores of one or more images for use in operating a medical imaging scanner; and
a processor configured to execute the instructions to perform a method including:
identifying one or more data acquisition device types;
receiving one or more data sets associated with each of the one or more data acquisition device types, wherein each data set of the one or more data sets includes one or more patient characteristics;
receiving a selected data acquisition device type for an imaging procedure; and
determining, based on the patient characteristics of the received one or more data sets and the imaging procedure, a predicted image quality score for an image produced by the selected data acquisition device type.
10. The system of claim 9 , wherein the processor is further configured to perform the method comprising:
determining a patient characteristic of a selected patient; and
determining the predicted image quality score further based on the patient characteristic of the selected patient.
11. The system of claim 9 , wherein the system is further configured for:
determining one or more recommended image acquisition parameters associated with the predicted image quality score.
12. The system of claim 9 , wherein the system is further configured for:
determining the one or more recommended image acquisition parameters using machine learning.
13. The system of claim 9 , wherein the system is further configured for:
determining the predicted image quality score based on one or more imaging operator characteristics, one or more image characteristics, or one or more effects of imaging, wherein the one or more effects of imaging includes radiation exposure.
14. The system of claim 9 , wherein the predicted image quality score is determined using machine learning.
15. The system of claim 9 , wherein the system is further configured for:
grouping the one or more data sets based on similarities between the patient characteristics respective to each of the one or more data sets; and
determining the predicted image quality score based on the grouping.
16. The system of claim 9 , wherein the system is further configured for:
initiating or instructing production of an image based on the predicted image quality score.
17. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for predicting image quality scores of images for use in operating a medical imaging scanner; the method comprising:
identifying one or more data acquisition device types;
receiving one or more data sets associated with each of the one or more data acquisition device types, wherein each data set of the one or more data sets includes one or more patient characteristics;
receiving a selected data acquisition device type for an imaging procedure; and
determining, based on the patient characteristics of the received one or more data sets and the imaging procedure, a predicted image quality score for an image produced by the selected data acquisition device type.
18. The non-transitory computer readable medium of claim 17 , the method further comprising:
determining a patient characteristic of a selected patient; and
determining the predicted image quality score further based on the patient characteristic of the selected patient.
19. The non-transitory computer readable medium of claim 17 , the method further comprising:
determining one or more recommended image acquisition parameters associated with the predicted image quality score.
20. The non-transitory computer readable medium of claim 17 , the method further comprising:
determining the one or more recommended image acquisition parameters using machine learning.