System and methods for radiographic image quality assessment and protocol optimization
The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing. based on the evaluating, scan acquisition parameters and image reconstruction parameters, and generating, optimal imaging protocol parameters, wherein the optimal imaging protocol parameters maximize image quality while minimizing radiation exposure.
1. A method for generating a patient-specific imaging protocol using a neural network having been trained to generate at least one probabilistic quality representation corresponding to at least one region of a generated simulated image, comprising:
receiving, by processing circuitry, scout scan data, the received scout scan data including scout scan information and scout scan parameters;
generating, by the processing circuitry, the generated simulated image based on the received scout scan data; scan acquisition parameters, and image reconstruction parameters;
deriving, by the processing circuitry, a simulated dose map from the received scout scan data and the scan acquisition parameters;
evaluating, by the processing circuitry, a determined image quality of the generated simulated image relative to a predetermined image quality threshold and the derived simulated dose map relative to a predetermined dosage threshold; and
generating, by the processing circuitry and based on the evaluating, imaging protocol parameters based on the scan acquisition parameters and the image reconstruction parameters.
2. The method according to claim 1 , further comprising
generating; by the processing circuitry and based on the evaluating, a subsequent generated simulated image based on the received scout scan data, subsequent scan acquisition parameters, and subsequent image reconstruction parameters,
deriving, by the processing circuitry, a subsequent simulated dose map from the received scout scan data and the subsequent scan acquisition parameters,
evaluating, by the processing circuitry, a determined image quality of the subsequent generated simulated image relative to the predetermined image quality threshold and the subsequent derived simulated dose map relative to the predetermined dosage threshold, and
generating, by the processing circuitry and based on the evaluating, subsequent imaging protocol parameters based on the subsequent scan acquisition parameters and the subsequent image reconstruction parameters, the subsequent imaging protocol parameters increasing image quality while reducing radiation exposure.
3. The method according to claim 1 , wherein the determined image quality of the generated simulated image is determined by applying the trained neural network to the generated simulated image, the trained neural network having been trained on scored reference images.
4. The method according to claim 1 , wherein the at least one probabilistic quality representation is based on one or more image quality properties including resolution, contrast, artifacts, and noise.
5. The method according to claim 1 , wherein the trained neural network is at least one of a disease-specific neural network and a body region-specific neural network.
6. The method according to claim 1 , wherein the scan acquisition parameters include x-ray beam energy and tube current.
7. The method according to claim 1 , wherein the image reconstruction parameters include reconstruction method and reconstruction kernel.
8. An apparatus for generating a patient-specific imaging protocol using a neural network having been trained to generate at least one probabilistic quality representation corresponding to at least one region of a generated simulated image, comprising:
processing circuitry configured to
receive scout scan data, the received scout scan data including scout scan information and scout scan parameters,
generate the generated simulated image based on the received scout scan data, scan acquisition parameters, and image reconstruction parameters,
derive a simulated dose map from the received scout scan data and the scan acquisition parameters,
evaluate a determined image quality of the generated simulated image relative to a predetermined image quality threshold and the derived simulated dose map relative to a predetermined dosage threshold, and
generate, based on the evaluating, imaging protocol parameters based on the scan acquisition parameters and the image reconstruction parameters.
9. The apparatus according to claim 8 , wherein the processing circuitry is further configured to
generate, based on the evaluating, a subsequent generated simulated image based on the received scout scan data, subsequent scan acquisition parameters, and subsequent image reconstruction parameters,
derive a subsequent simulated dose map from the received scout scan data and the subsequent scan acquisition parameters,
evaluate a determined image quality of the subsequent generated simulated image relative to the predetermined image quality threshold and the subsequent derived simulated dose map relative to the predetermined dosage threshold, and
generate, based on the evaluating, subsequent imaging protocol parameters based on the subsequent scan acquisition parameters and the subsequent image reconstruction parameters, the subsequent imaging protocol parameters increasing image quality while reducing radiation exposure.
10. The apparatus according to claim 8 , wherein the processing circuitry is further configured to
apply the trained neural network to the generated simulated image in order to determine the determined image quality of the generated simulated image, the trained neural network having been trained on scored reference images.
11. The apparatus according to claim 8 , wherein the processing circuitry is further configured to
generate, as the determined image quality of the generated simulated image, at least one image quality metric based on the at least one probabilistic quality representation.
12. The apparatus according to claim 8 , wherein the trained neural network is at least one of a disease-specific neural network and a body region-specific neural network.
13. The apparatus according to claim 8 , wherein the scan acquisition parameters include x-ray beam energy and tube current.
14. The apparatus according to claim 8 , wherein the image reconstruction parameters include reconstruction method and reconstruction kernel.
15. A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method of generating a patient-specific imaging protocol using a neural network having been trained to generate at least one probabilistic quality representation corresponding to at least one region of a generated simulated image, comprising:
receiving scout scan data, the received scout scan data including scout scan information and scout scan parameters;
generating the generated simulated image based on the received scout scan data, scan acquisition parameters; and image reconstruction parameters;
deriving a simulated dose map from the received scout scan data and the scan acquisition parameters;
evaluating a determined image quality of the generated simulated image relative to a predetermined image quality threshold and the derived simulated dose map relative to a predetermined dosage threshold; and
generating, based on the evaluating, imaging protocol parameters based on the scan acquisition parameters and the image reconstruction parameters.
16. The non-transitory computer-readable storage medium according to claim 15 , the method further comprising
generating, based on the evaluating, a subsequent generated simulated image based on the received scout scan data, subsequent scan acquisition parameters, and subsequent image reconstruction parameters,
deriving a subsequent simulated dose map from the received scout scan data and the subsequent scan acquisition parameters,
evaluating a determined image quality of the subsequent generated simulated image relative to a predetermined image quality threshold and the subsequent derived simulated dose map relative to a predetermined dosage threshold, and
generating, based on the evaluating, subsequent imaging protocol parameters based on the subsequent scan acquisition parameters and the subsequent image reconstruction parameters, the subsequent imaging protocol parameters increasing image quality while reducing radiation exposure.
17. The non-transitory computer-readable storage medium according to claim 15 , wherein the determined image quality of the generated simulated image is determined by
applying the trained neural network to the generated simulated image, the trained neural network having been trained on scored reference images.
18. The non-transitory computer-readable storage medium according to claim 15 , the method further comprising
generating, as the determined image quality, at least one image quality metric based on the at least one probabilistic quality representation.
19. The non-transitory computer-readable storage medium according to claim 15 , wherein the neural network is at least one of a disease-specific neural network and a body region-specific neural network.
20. The non-transitory computer-readable storage medium according to claim 15 , wherein the scan acquisition parameters include x-ray beam energy and tube current.