Respiratory interval-based correlation and processing of dynamic imaging data

1. A method of dynamic imaging, the method comprising:

acquiring volume data for a volume of a patient over a plurality of respiratory cycles;

acquiring respiratory data representative of respiration over the plurality of respiratory cycles;

dividing each respiratory cycle of the plurality of respiratory cycles into a set of respiratory intervals based on the respiratory data;

correlating the volume data produced over the plurality of respiratory cycles with a respective respiratory interval of the set of respiratory intervals;

determining average volume data for the volume per respiratory interval based on the volume data from the plurality of respiratory cycles correlated with the respective respiratory interval;

selecting, for each respiratory interval, a subset of the volume data correlated with the respective respiratory interval, using the average volume data for the respective respiratory interval as a template; and

displaying a respective composite three-dimensional image of the volume for each respiratory interval based on the selected subset of the volume data for each respiratory interval.

2. The method of claim 1 , wherein displaying the respective composite three-dimensional image comprises determining a standard deviation of the volume data for each respiratory interval.

3. The method of claim 2 , further comprising normalizing the standard deviation based on the average volume data for each respiratory interval.

4. The method of claim 1 , wherein displaying the respective composite three-dimensional image comprises determining a maximum or minimum intensity projection of the volume data for each respiratory interval.

5. The method of claim 1 , further comprising:

computing a similarity metric for the volume data in each respiratory interval from each respiratory cycle of the plurality of respiratory cycles relative to the average volume data for the respiratory interval;

wherein selecting the subset comprises determining, for each respiratory interval, a subset of images of the volume data that meet a threshold of the similarity metric; and

wherein displaying the respective composite three-dimensional image comprises displaying a further respective three-dimensional image for each respiratory interval based on an average of the volume data in each subset.

6. The method of claim 5 , wherein computing the similarity metric comprises cropping the volume data to one or more regions of interest.

7. The method of claim 5 , wherein the similarity metric is normalized cross correlation.

8. The method of claim 1 , wherein the volume data comprises two-dimensional magnetic resonance imaging (MRI) images for a plurality of slice locations that collectively form the volume.

9. The method of claim 1 , wherein acquiring the respiratory data comprises acquiring and sampling an external respiratory surrogate signal.

10. The method of claim 9 , wherein dividing each respiratory cycle comprises:

defining each respiratory cycle based on peak inspiratory maxima of a moving average representation of the sampled external respiratory signal; and

defining the set of respiratory intervals for each respiratory cycle by dividing time between each peak inspiratory maximum into equal-time intervals.

11. The method of claim 1 , wherein acquiring the volume data and acquiring the respiratory data are implemented simultaneously.

12. A system for dynamic imaging, the system comprising:

a respiratory monitor to acquire surrogate respiratory data over a plurality of respiratory cycles;

a scanner to acquire frame data over the plurality of respiratory cycles, the frame data comprising a plurality of frames; and

one or more processors in communication with the respiratory monitor and the scanner, the one or more processors being configured to:

divide each respiratory cycle of the plurality of respiratory cycles into a set of respiratory intervals based on the surrogate respiratory data;

correlate the frame data produced over the plurality of respiratory cycles with a respective respiratory interval of the set of respiratory intervals;

determine average frame data per respiratory interval of the set of respiratory intervals based on the frame data from the plurality of respiratory cycles correlated with the respective respiratory interval;

select, for each respiratory interval, a subset of the frame data correlated with the respective respiratory interval, using the average frame data for the respective respiratory interval as a template; and

construct a respective composite three-dimensional image for each respiratory interval based on the selected subset of the frame data for each respiratory interval.

13. The system of claim 12 , wherein the one or more processors are configured to determine a standard deviation of the frame data for each respiratory interval.

14. The system of claim 13 , wherein the one or more processors are configured to normalize the standard deviation based on the average frame data for each respiratory interval.

15. The system of claim 12 , wherein the one or more processors are configured to determine a maximum or minimum intensity projection of the frame data for each respiratory interval.

16. The system of claim 12 , wherein the one or more processors are configured to:

implement an image registration to compute a similarity metric for the frame data in each respiratory interval from each respiratory cycle of the plurality of respiratory cycles relative to the average frame data for the respiratory interval;

determine, for each respiratory interval, a subset of frames of the frame data that meet a threshold of the similarity metric to select the subset of the frame data for each respiratory interval; and

construct a further respective three-dimensional image for each respiratory interval based on an average of the frame data in each subset to construct the respective composite three-dimensional image for each respiratory interval.

17. The system of claim 12 , wherein the one or more processors are configured to:

define each respiratory cycle based on peak inspiratory maxima of a moving average representation of the surrogate respiratory data; and

defining the set of respiratory intervals for each respiratory cycle by dividing time between each peak inspiratory maximum into equal-time intervals.

18. A magnetic resonance imaging (MRI) apparatus comprising:

an MRI scanner configured to acquire data representing a plurality of two-dimensional slices over a plurality of respiratory cycles; and

a control system in communication with the MRI scanner and configured to direct the MRI scanner to implement a series of pulse sequences configured to scan the plurality of two-dimensional image slices over the plurality of respiratory cycles to produce respective scan data representing each two-dimensional image slice, the control system comprising one or more processors configured to:

divide each respiratory cycle of the plurality of respiratory cycles into a set of respiratory intervals;

correlate the scan data produced over the plurality of respiratory cycles with a respective respiratory interval of the set of respiratory intervals;

determine average scan data per respiratory interval of the set of respiratory intervals based on the scan data from the plurality of respiratory cycles correlated with the respective respiratory interval;

select, for each respiratory interval, a subset of the scan data correlated with the respective respiratory interval, using the average frame data for the respective respiratory interval as a template; and

construct a respective composite three-dimensional image for each respiratory interval based on the selected subset of the scan data for each respiratory interval.

19. The magnetic resonance imaging (MRI) apparatus of claim 18 , wherein the one or more processors are further configured to:

implement an image registration to compute a similarity metric for the data representing the two-dimensional slices in each respiratory interval from each respiratory cycle of the plurality of respiratory cycles relative to the average scan data for the respiratory interval;

determine, for each respiratory interval, a subset of the two-dimensional slices that meet a threshold of the similarity metric to select the subset of the scan data for each respiratory interval; and

construct a further respective three-dimensional image for each respiratory interval based on an average of the slices in each subset to construct the respective composite three-dimensional image for each respiratory interval.