# Patent

## Title
Phase-to-amplitude/slope mapping

## Abstract

A method includes obtaining a signal that includes a plurality of cycles and generating a map that maps motion phases to the signal based on both an amplitude and a slope of the signal. A system includes a processor that identifies a set of motion signal timestamps, for a plurality of motion cycles in a motion signal indicative of cyclic motion of a moving object, based on a predetermined motion phase of interest and a phase-to-amplitude/slope mapping, wherein the set of motion signal timestamps correspond to a common signal amplitude. A method include identifying a peak of a plurality of peaks in a motion cycle of a noisy cyclic signal having irregular periodicity, wherein the peak corresponds to a point lying between two points with amplitudes below a predetermined threshold, comparing points before and after the peak with the peak, and identifying the peak as a local maximum when the peak is greater than the points.

## Background
CROSS REFERNCE TO RELATED APPLICATIONS
This application is a national filing of PCT application Ser. No. PCT/IB2011/051840, filed Apr. 27, 2011, published as WO 2011/151740 A2 on Dec. 8, 2011, which claims the benefit of U.S. provisional application Ser. No. 61/351,310 filed Jun. 4, 2010, which is incorporated herein by reference.


DESCRIPTION
The following generally relates to phase-to-amplitude/slope mapping and is described herein with particular application to a cyclic signal having irregular periodicity; other applications are also contemplated herein.
In imaging, respiratory phase-based gating of a respiratory signal, which is concurrently obtained while scanning a patient, has been used to identify projection data corresponding to a particular respiratory phase of interest (e.g., full inspiration, full expiration, or somewhere in between) for procedures where a patient breathes freely rather than holding his/her breath. With this approach, the respiratory signal has been divided into individual breathing cycles (based on signal maxima and minima), and each cycle is divided into phases ranging from the beginning of the cycle (0%) (e.g., full inspiration or expiration or) to the end of the cycle (99%).
FIG. 1 shows an example of the above. In FIG. 1, the y-axis represents signal amplitude (i.e. the mechanical state of the lungs, including expansion and contraction) and the x-axis represents time. For this example, a breathing cycle 102 is defined to begin with full inspiration or lung expansion, which corresponds to the signal maximum 104 of the cycle 102. In this example, the 0% phase is assigned to the maximum 104. The remaining portion of the cycle 102 is linearly divided into a plurality of phases such as 25%, 50%, 75%, and 99% of the cycle 102, relative to the maxima 104.
For reconstruction, a set of timestamps for a particular phase of interest is identified, based on percentage of the cycle, and conveyed to the reconstructor, which uses the timestamp to identify projection data to reconstruct. By way of example, in FIG. 1, where the 75% phase is the phase of interest, the timestamp 112 for the cycle 102 is identified and conveyed to the reconstructor. For the other cycles in FIG. 1, the timestamps 112, 114, 116, 118, and 120 are identified as corresponding to the 75% phase and conveyed to the reconstructor.
Unfortunately, there are several problems with this assumption: (a) the rate of expiration is generally less than the rate of inspiration so the phases do not linearly span across a cycle; (b) the breathing cycle shape may be different cycle to cycle (as can be seen in FIG. 1); and (c) the mechanical state (amplitude) of the lungs during inhalation is not guaranteed to be the same during exhalation. For example, in FIG. 1, the timestamps corresponding to point 112, 116, and 120 are nearer to or at full expiration where the lungs are contracted and the amplitude is at a minimum, whereas the timestamps 114 and 118 correspond to mechanical states during inspiration where the lungs are partially expanded and have greater amplitude. As a consequence, the anatomy in the reconstructed image data may not align, which may introduce motion artifact into the reconstructed image data.
Aspects of the present application address the above-referenced matters and others.
According to one aspect, a method includes obtaining a signal that includes a plurality of cycles and generating a map that maps motion phases to the signal based on both an amplitude and a slope of the signal.
According to another aspect, a system includes a processor that identifies a set of motion signal timestamps, for a plurality of motion cycles in a motion signal indicative of cyclic motion of a moving object, based on a predetermined motion phase of interest and a phase-to-amplitude/slope mapping, wherein the set of motion signal timestamps correspond to a common signal amplitude.
According to another aspect, a method includes identifying a peak from a plurality of peaks in a motion cycle of a noisy cyclic signal having irregular periodicity, wherein the peak corresponds to a point lying between two points with amplitudes below a predetermined threshold, comparing points before and after the peak with the peak, and identifying the peak as a local maximum when the peak is greater than the surrounding points.



The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
FIG. 1 illustrates prior art.
FIG. 2 illustrates an example imaging system in connection with a motion phase correlator.
FIG. 3 illustrates example motion phase correlator.
FIG. 4 illustrates an example approach for mapping motion phase to the amplitude and slope of a cycle in a motion signal.
FIG. 5 illustrates an example image generated without use of the motion phase correlator.
FIG. 6 illustrates an example image generated with projection data selected by the motion phase correlator.
FIG. 7 illustrates example approach for identifying maxima in a motion signal.
FIG. 8 illustrates example approach for identifying a local maximum amongst multiple maxima in a cycle.
FIG. 9 illustrates example approach for rejecting maxima amongst multiple maxima candidates for a cycle.
FIG. 10 illustrates example approach for augmenting a motion signal.
FIG. 11 illustrates an example method for identifying projection data for a motion phase of interest for reconstruction.
FIG. 12 illustrates an example method for determining the set of local maxima in a signal.



FIG. 2 illustrates an imaging system 200 such as a computed tomography (CT) scanner. However, in other embodiments, the imaging system 200 may include one or more other imaging modalities such as x-ray, Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance Imaging (MRI), Ultrasound (US), and/or other imaging modalities.
The imaging system 200 includes a stationary gantry 202 and a rotating gantry 204, which is supported by the stationary gantry 202. The rotating gantry 204 rotates around an examination region 206 about a longitudinal or z-axis.
A radiation source 208, such as an x-ray tube, is supported by the rotating gantry 204 and rotates with the rotating gantry 204, and emits radiation. A radiation sensitive detector array 210, located opposite the source 208 and across the examination region 206, detects radiation that traverses the examination region 206 and generates projection data corresponding to the detected radiation.
A support 212, such as a couch, supports the object or subject in the examination region 206. A motion monitor 214 monitors a motion state of a moving object during scanning and generates a motion signal indicative thereof. Examples of moving objects include, but are not limited to, the lung(s) or the heart of a patient, or other object with recurring regular or irregular cyclic motion.
A motion phase correlator 216 correlates motion phases with imaging or scan acquisition timestamps. As described in greater detail below, in one instance, this is achieved by correlating motion phases with the motion signal based on both an amplitude and a slope of the cycles in the motion signal. The resulting correlation can be used to identify timestamps of projection data, for different cycles, that corresponds to the same motion phase and that have a common or same mechanical amplitude or state. This may facilitate mitigating motion artifact due to misalignment of structure across the different cycles.
A reconstructor 218 reconstructs projection data and generates volumetric image data indicative of the examination region 206. The reconstructor 218 is configured to selectively reconstruct project data based on timestamps identified via the motion phase correlator 216 for a phase of interest. The reconstructor 218 may employ various reconstruction algorithms, and the projection data from a single or multiple acquisitions may be reconstructed to generate a single image.
A general purpose computing system serves as an operator console 220, which includes human readable output devices such as a display and/or printer and input devices such as a keyboard and/or mouse. Software resident on the console 220 allows the operator to control the operation of the system 200, for example, by allowing the operator to select a scanning protocol, initiate scanning, select a reconstruction algorithm (e.g., a motion gated reconstruction), etc.
It is to be appreciated that the motion phase correlator 216 may be part of the system 100, for example, part of the console 220 or otherwise, or remote from the system 100, for example, part of workstations, distributed across a network, and/or otherwise. Furthermore, the motion phase correlator 216 may be implemented by one or more processors processing computer readable instructions embedded or encoded on computer readable medium such as memory.
FIG. 3 illustrates an example motion phase correlator 216.
A signal characteristic identifier 302 identifies one or more characteristics of a motion signal. This may include identifying one or more characteristics for one or more of the cycles of the motion signal. Examples of such characteristics include, but are not limited to, maxima and minima and/or other data points.
A signal characteristic processor 304 processes the identified signal characteristic(s). This may include determining one or more statistics (e.g., mean, median, maximum, minimum, etc.) and/or other information for the identified signal characteristic(s). In one instance, the statistic relates to the amplitude of the signal for one or more cycles.
A signal slope identifier 306 identifies descending and ascending regions in the cycles in the motion signal.
A phase-to-amplitude/slope mapper 308 maps motion phases to the motion signal based on the processed identified signal characteristic and the identified slopes.
A timestamp identifier 310 identifies timestamps in the motion signal that correspond to a motion phase of interest and have a similar signal amplitude based on the mapping.
The motion phase correlator 216 is now described in connection with processing a cyclic signal with irregular periodicity such as a respiratory signal obtained concurrently with imaging a subject. Other suitable signals include other cyclic signal including, but not limited to noisy cyclic signals with irregular periodicity.
Turning to FIG. 4, a signal 400 includes a plurality of breathing (inspiration and expiration) cycles 4021-4026 having varying amplitudes and time widths. In FIG. 4, the y-axis represents signal amplitude (mechanical amplitude) and the x-axis represents time.
The signal characteristic identifier 302 identifies maxima 4041-4046 and minima 4061-4066 of the respiratory cycles 4021-4026 of the respiratory signal 400. In this example, for a particular cycle, the maximum (or maximum amplitude) corresponds to full inspiration in which the lungs are expanded or distended, and the minimum (or minimum amplitude) corresponds to full expiration in which the lungs are contracted or collapsed.
The signal characteristic processor 304 determines a mean (average) maximum 408 and a mean (average) minimum 410 respectively for the identified maxima 4041-4046 and minima 4061-4066.
The slope identifier 306 identifies descending (expiration or inspiration) and ascending (inspiration or expiration) regions or slopes and for each cycle based on the maxima 4041-4046 and the minima 4061-4066. For sake of clarity, only slopes 412 and 414 for the cycle 4022 are identified in FIG. 4 through reference numeral. Note that in FIG. 4, the rate of expiration (slope 412) is slower than the rate of inspiration (slope 414).
The phase-to-amplitude/slope mapper 308 maps motion phases respectively to the expiration and inspiration slopes 412 and 414, between the mean maximum 408 and the mean minimum 410. For example, the cycle 4022 spans from the maximum 4042 to the next maximum 4043, and the 0% phase is mapped to the mean maximum 408 and the 49% phase is mapped to the mean minimum 410.
Expiration, which occurs between the 0% and 49% phases of the cycle 4022, is mapped to the expiration slope 412, and inspiration, which occurs between 49% and 99% phases of the cycle 4022, is mapped to the inspiration slope 414. As such, the phases for expiration and inspiration are separately determined, which takes into consideration that the average rate of expiration is generally less than the average rate of inspiration. As such, each cycle is guaranteed to have data points corresponding to phases between 0% and 99%.
Some cycles may have more than one point that corresponds to a particular phase. In these cases, the first or other candidate point is selected. Where a cycle (e.g., the cycle 4021) includes a maximum that is less than the mean maximum (e.g., the maximum 4041), the cycle maximum is deemed 0%. Thus, in FIG. 4, the phase mapping would be suitably scaled to span from the maximum 4041 and the minimum 4061.
The timestamp identifier 310 identifies imaging or scan acquisition timestamps for motion phases. For example, in FIG. 4, the timestamp identifier 310 is used to identify timestamps 4161-4166 corresponding to the 25% phase of the motion cycles 4021-4026. Of course timestamps corresponding to phases other than 25% can also be identified. The timestamp identifier 310 generates a signal indicative of the timestamps and conveys the signal to the reconstructor 218 (FIG. 2), which may select projection data to reconstruct based thereon.
As these timestamps correlate to substantially similar mechanical amplitude of the moving object, the structure of the moving object in the reconstructed image data will likely be well-aligned. This can be seen in FIGS. 5 and 6.
FIG. 5 shows an image 500 reconstructed where such timestamps are not used to select the projection data to reconstruct, and the anatomical structure in the projection data is not aligned or misaligned, introducing artifact 502 into the image 500, as indicated by the arrow in the image. The artifact in FIG. 5 is provided for explanatory purposes, and other or different artifact may be present in the image.
FIG. 6 shows an image 600 reconstructed based on projection data corresponding to timestamps identified by the motion phase correlator 216 for a particular motion phase. Herein the anatomical structure in the projection data is aligned, and the artifact in image 500 is absent, as indicated by the arrow in the image.
As discussed above, the signal characteristic identifier 302 identifies one or more characteristics of a motion signal such as maxima and minima for cycles in a motion signal. It is to be appreciated that the motion signal may include noise, which may provide false maxima and/or minima in the signal and/or obscure true maxima and/or minima. FIGS. 7-9 illustrate an approach for determining the maxima and minima in the presence of noise. Examples of such signals include respiratory, cardiac, and/or other signals.
Initially referring to FIG. 7, a sub-portion of a noisy signal 702 is illustrated. Peaks or maxima are identified in the signal 702 for a predetermined evaluation range. In the illustrated example, a peak 704 is identified as the single highest point between two points 706 and 708, which are smaller by a predefined detection threshold 710. In this example, the threshold 710 is defined to be one tenth ( 1/10) of the signal's global dynamic range, or the difference between the global maximum and global minimum. In other embodiments, the threshold 710 can be otherwise defined.
Each data point of the signal 702 in the evaluation range is then evaluated. The data points of the signal 702 in the evaluation range before the data point 704 are searched until a data point drops below a second threshold 712, which corresponds to the amplitude from the minimum to the maximum less the threshold 710. Likewise, the data points of the signal 702 in the evaluation range after the data point 704 are searched until a data point drops below the second threshold 712. All of the data points in the evaluation range are then compared to the data point 704.
If the peak 704, over the evaluation range, is the maximum, it is identified as a local maximum. Otherwise, the search continues with the next point. This is further illustrated in FIGS. 8 and 9. In FIG. 8, a peak 802, identified in an evaluation range 804 based on a threshold 806, is identified as a local maximum as it is the highest point in the range 804. In FIG. 9, a peak 902 identified in an evaluation range 904, based on a threshold 906, is not identified as a local maximum as it is not the highest point in the range 904.
FIG. 10 illustrates a non-limiting technique for augmenting the signal.
With this technique, where a time distance 1002 between two peaks 1004 and 1006 of a motion signal 1000 exceeds an add peak threshold, one or more peak 1008 and 1010 are added to the signal 1000. Where the time distance 1012 between a peak 1014 and a preceding peak 1016 and a time distance 1018 between the maximum 1014 and a subsequent peak 1020 are both less than an extract peak threshold, the peak 1014 is removed from the signal 1000. The thresholds may be based on an average peak spacing or otherwise.
FIG. 11 illustrates a method for identifying projection data for a motion phase of interest for reconstruction.
At 1102, a motion signal is currently obtained whiled scanning a moving object have recurring cyclic motion. The motion may be related to breathing, the heart beating, or other cyclic motion.
At 1104, maxima and minima amplitudes are identified in the motion signal.
At 1106, a global mean maximum amplitude and a global mean minimum amplitude are determined based on the identified maxima and minima.
At 1108, the global mean maximum amplitude is used to define a begin or 0% point for a first region of each cycle extending from the cycle maximum to the cycle minimum. Where the global maximum is greater than the cycle maximum, the begin or 0% point is defined at the cycle maximum. Where the global maximum is less than the cycle maximum, the begin or 0% point is defined at the point following the cycle maximum whose amplitude best matches the global maximum.
At 1110, the global mean minimum amplitude is used to define the 50% point for a second region of each cycle extending from the cycle minimum to the maximum of the next cycle. Where the global mean minimum is less than the cycle minimum, the 50% point is defined at the cycle minimum. Where the global minimum is greater than the cycle minimum, the 50% point is defined at the point following the cycle minimum whose amplitude best matches the global minimum.
At 1112, motion phases from 0% to 49% are mapped to the first region.
At 1114, motion phases from 50% to 99% are mapped to the second region.
At 1116, a phase of interest is identified.
At 1118, the mapping between the identified phase and the amplitudes/slopes is used to determine a scan acquisition timestamp per cycle that corresponds to the mapped amplitude.
At 1120, projection data acquired at the identified timestamps is reconstructed into an image volume.
FIG. 12 illustrates a method for determining signal maximum.
At 1202, a peak is determined as the first point whose amplitude is greater than that of any previous point up to the previous cycle maximum by a predetermined threshold.
At 1204, it is determined whether a point following the peak exceeds the peak.
If so, then act 1202 is repeated with the next point.
If not, then at 1206, it is determined whether a point following the peak is less than the peak by a predetermined threshold.
If so, then at 1208 the peak is identified as a cycle maximum.
If not, then act 1204 is repeated with the next point.
At 1210, it is determined whether the maxima are closer together than a predetermined threshold.
If so, then at 1212, one or more of these maxima are de-identified (removed) as maxima. With one non-limiting embodiment, the mean and standard deviation of the spacing between maxima is calculated, and one or more of the maxima that are closer together than mean−x*standard deviation (where x is an input parameter) are de-identified as maxima.
If so, or if not, then 1214, it is determined whether the maxima are farther apart than a predetermined threshold.
If so, then at 1216, one or more of these maxima are added. With one non-limiting embodiment, the mean and standard deviation of the spacing between maxima is calculated, and if one or more of the maxima are farther apart than mean+x*standard deviation (where x in an input parameter) then one or more maxima are added between the bounding peaks, using acts 1202 through 1208, where the classification threshold is iteratively diminished until suitable maxima are identified.
The above described acts may be implemented by way of computer readable instructions, which, when executed by a computer processor(s), causes the processor(s) to carry out the acts described herein. In such a case, the instructions are stored in a computer readable storage medium such as memory associated with and/or otherwise accessible to the relevant computer.
Although the above is discussed in the context of CT and respiratory signals, it is to be appreciated that the approach disclosed herein can be employed to identify substantially equivalent data points in another noisy cyclic signal with irregular periodicity.
The invention has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be constructed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

## Claims

1. A method, comprising:
obtaining a signal that includes a plurality of cycles;
generating a map that maps motion phases to the signal based on both an amplitude and a slope of the signal, wherein the map divides each cycle into a descending region and an ascending region, and the descending region includes a portion of the signal with a negative slope between a lesser of a cycle maximum and a mean of signal maxima of the plurality of cycles and a greater of a cycle minimum and a mean of signal minima of the plurality of cycles, and the ascending region includes a second portion of the signal with a positive slope between the lesser of the cycle maximum and the mean of the signal maxima and the greater of the cycle minimum and the mean of the signal minima; and
generating volumetric image data based on data acquired by a scanner and the mapped motions phases.

2. The method of claim 1, further comprising:
identifying a set of timestamps for the plurality of cycles for a predetermined phase of interest based on the map, wherein identified set of timestamps corresponds to a common signal amplitude; and
generating a signal indicative thereof.

3. The method of claim 1, wherein the signal is detected while imaging a moving object having recurring cyclic motion and a plurality of cycles corresponding to the recurring cyclic motion having irregular periodicity.

4. The method of claim 1, wherein the object is a subject and the signal is indicative of multiple breathing or cardiac cycles of the subject.

5. The method of claim 1, wherein the scanner includes at least one of a CT scanner, an X-ray scanner, a Positron Emission Tomography (PET) scanner, a Single Photon Emission Computed Tomography (SPECT) scanner, a Magnetic Resonance Imaging (MRI) scanner, or an Ultrasound (US) scanner; and
wherein generating volumetric image data includes:
selecting at least one mapped phase;
selecting data acquired by the scanner according to the at least one selected phase; and
reconstructing selected data in he volumetric image data.

6. The method of claim 1, wherein the descending amplitude region corresponds to one of contraction or expansion of the object and the ascending amplitude region corresponds to the other of expansion or contraction of the object.

7. The method of claim 1, wherein the phases span from 0% to 99% over the cycle, and one set of phases ranges from 0% to 49% and correspond to at least one of the ascending region or the descending region and another set of phases ranges from 50% to 99% and correspond to the other of the least one of the ascending region or the descending region.

8. The method of claim 1, further comprising:
identifying the signal maxima and minima for the plurality of cycles;
determining the mean signal maximum and the mean signal minimum respectively based on the identified signal maxima and minima; and
mapping the phases to the amplitude of each cycle in a range spanning from the lesser of the mean signal maximum and the cycle maximum to the greater of the mean signal minimum and the cycle minimum.

9. The method of claim 1, further comprising:
identifying a maximum for an evaluation range within a cycle, wherein the maximum corresponds to a point lying between two points with amplitudes below a predetermined threshold;
comparing points in the evaluation range before and after the maximum with the maximum;
identifying the maximum as a local maximum for the evaluation range when the maximum is greater than the points; and
determining that the maximum is not the local maximum when the maximum is less than at least one of the points in the evaluation range.

10. The method of claim 9, further comprising:
de-identifying a maximum identified as a local maximum when the maxima are closer together than a predetermined remove maximum threshold; and
adding a maximum to the local maxima when the maxima are farther apart than a predetermined add maximum threshold.

11. A method, comprising:
mapping a first set of motion phases to a descending amplitude region of at least one motion cycle of a motion signal including a plurality of motion cycles, and the descending amplitude region includes the signal between the lesser of the cycle maximum and a mean signal maxima and a greater of a cycle minimum and a mean signal minima;
mapping a second set of motion phases to an ascending region of the at least one motion cycle, and the ascending region of the at least one motion cycle includes the signal between the greater of the cycle minimum and the mean signal minima and the lesser of the cycle maximum and the mean signal maxima; and
reconstructing data acquired from a scanner according to at least one of the first set of motion phases or the second set of motion phases.