Method, system and/or computer readable medium for characterizing motion of tissue based on acquired pet data

1 . A computer-implemented method, comprising:

obtaining positron emission tomography (PET) data of moving tissue of interest acquired over a plurality of motion cycles of the tissue of interest;

generating a set of short PET frames from the PET data based on a predetermined time duration, which is less than a period of the plurality of motion cycles;

identifying the tissue of interest in each short PET frame in the set of short PET frames;

identifying a position value of the identified tissue of interest in each short PET frame in the set of short PET frames;

identifying each of the plurality of motion cycles based on the position of the identified tissue of interest in each short PET frame in the set of short PET frames;

segmenting each identified cycle of the plurality of motion cycles into a predetermined number of segments, where a length of each segment corresponds to a predetermined displacement value;

re-sampling the position values of the plurality of motion cycles into bins corresponding to the segments;

determining statistics for each bin and therefore of the position values for each of the segments; and

characterizing the motion of the tissue of interest over the plurality of motion cycles based on the statistics.

2 . The computer-implemented method of claim 1 , further comprising:

planning a procedure for the tissue of interest based on the characterization of the motion of the tissue of interest.

3 . The computer-implemented method of claim 1 , further comprising:

identifying local minimums in the position values, local maximums in the position values, or both local minimums and local maximums in the position values; and

identifying each of the motion cycles based on the identified local minimums, local maximums, or local minimums and local maximums.

4 . The computer-implemented method of claim 1 , wherein the statistic incudes mean values and the standard deviations, and further comprising:

graphically presenting the mean values and the standard deviations.

5 . The computer-implemented method of claim 1 , further comprising:

determining a set of motion amplitude ranges based on the estimated motion;

identifying PET data from each motion cycle that corresponds to a motion amplitude range of interest in the set of motion amplitude ranges; and

combining the identified PET data to generate a frame of PET data.

6 . A computing system, comprising:

a computer readable medium memory that includes instructions for characterizing motion of moving tissue of interest in PET data; and

a processor configured to execute the instructions, wherein the instructions cause the processor to:

obtain PET data of the moving tissue of interest acquired over a plurality of motion cycles of the tissue of interest;

generate a set of short PET frames from the PET data based on a predetermined time duration, which is less than a period of the plurality of motion cycles;

identify the tissue of interest in each short PET frames in the set of short PET frames;

identify a position value of the identified tissue of interest in each short PET frame in the set of short PET frames;

identify each of the plurality of motion cycles based on the position of the identified tissue of interest in each short PET frame in the set of short PET frames;

segment each identified cycle of the plurality of motion cycles into a predetermined number of segments, where a length of each segment corresponds to a predetermined displacement value;

re-sample the position values of the plurality of motion cycles into bins corresponding to the segments;

determine statistics for each bin and therefore of the position values for each of the segments; and

characterize the motion of the tissue of interest over the plurality of motion cycles based on the statistics.

7 . The computing system of claim 6 , wherein the instructions cause the processor to:

identify local minimums in the position values, local maximums in the position values, or both local minimums and local maximums in the position values; and

identify each of the motion cycles based on the identified local minimums, local maximums, or local minimums and local maximums.

8 . The computing system of claim 6 , wherein the instructions cause the processor to:

determine a mean value and a standard deviation for each bin; and

characterize the motion of the tissue of interest based on mean values and standard deviations for the bins.

9 . The computing system of claim 8 , wherein the instructions cause the processor to:

graphically present the mean values and the standard deviations.

10 . The computing system of claim 6 , wherein the instructions cause the processor to:

perform amplitude-based gating without an external device based on the estimated motion of the tissue of interest over the plurality of motion cycles.

11 . A non-statutory computer readable storage medium encoded with computer executable instructions, which when executed by a processor, causes the processor to:

obtain PET data of moving tissue of interest acquired over a plurality of motion cycles of the tissue of interest;

generate a set of short PET frames from the PET data based on a predetermined time duration, which is less than a period of the plurality of motion cycles;

identify the tissue of interest in each short PET frame in the set of short PET frames;

identify a position value of the identified tissue of interest in each short PET frame in the set of short PET frames;

identify each of the plurality of motion cycles based on the position of the identified tissue of interest in each short PET frame in the set of short PET frames;

segment each identified cycle of the plurality of motion cycles into a predetermined number of segments, where a length of each segment corresponds to a predetermined displacement value;

re-sample the position values of the plurality of motion cycles into bins corresponding to the segments;

determine statistics for each bin and therefore of the position values for each of the segments; and

characterize the motion of the statistics.

12 . The non-statutory computer readable storage medium of claim 11 , where the instructions further cause the processor to:

identify local minimums in the position values, local maximums in the position values, or both local minimums and local maximums in the position values; and

identify each of the motion cycles based on the identified local minimums, local maximums, or local minimums and local maximums.

13 . The non-statutory computer readable storage medium of claim 11 , where the instructions further cause the processor to:

identify PET data from each motion cycle that corresponds to a motion amplitude range of interest that was determined from the estimated motion; and

combine the identified PET data to generate a frame of PET data.