Abstract
Systems and methods are provided for detecting motion in an imaging system. A time series of volumetric images of a region of interest are captured at the imaging system. Each volumetric image of the time series of volumetric images is captured as a series of twodimensional slices of the region of interest. A representative value is calculated for each voxel to create a representative volumetric dataset representing the region of interest. For each slice of the series of twodimensional slices, a simulated volumetric time series is generated, including time series data for the slice and the calculated representative value at all times for the other slices of the series of twodimensional slices. A volumetric registration is performed on each of the simulated volumetric time series to provide a set of estimated motion parameters for the slice associated with the simulated volumetric time series.
Claims

A method for detecting motion in an imaging system, the method comprising:
capturing a time series of volumetric images of a region of interest at the imaging system, each volumetric image of the time series of volumetric images being captured as a series of twodimensional slices of the region of interest;
calculating a representative value for each voxel of the series of twodimensional slices to create a representative volumetric dataset representing the region of interest;
generating, for each slice of the series of twodimensional slices, a simulated volumetric time series including time series data for the slice and the calculated representative value at all times for the other slices of the series of twodimensional slices; and
performing a volumetric registration on each of the simulated volumetric time series to provide a set of estimated motion parameters for the slice associated with the simulated volumetric time series.

The method of claim 1, further comprising:
determining, for each of a plurality of voxels in the region of interest, a time series of translations from the set of estimated motion parameters associated with the slice of the series of twodimensional slices to which the voxel belongs and the position of the voxel within the slice;
performing a regression analysis on each time series of translations, determined for the plurality of voxels in the region of interest, to provide a regression model representing the time series; and
subtracting a set of values generated from the regression model from the time series of volumetric images to provide a set of residual values representing a motion corrected time series of volumetric images.
 The method of claim 2, wherein performing the regression analysis on the time series of translations for a given voxel comprises performing the regression analysis on the time series of translations for the voxel and at least one time series associated with an adjacent voxel.
 The method of claim 3, wherein the at least one time series associated with an adjacent voxel is associated with an adjacent voxel in a slice adjacent to the slice to which the voxel belongs.
 The method of claim 4, wherein performing the regression analysis on the time series of translations for a given voxel comprises performing the regression analysis on the time series of translations using a plurality of regressors comprising a first translation along a first axis, a second translation along a second axis, perpendicular to the first axis, and a translation along a third axis, perpendicular to each of the first and second axes, squared values of each of the first, second, and third translations, and an adjacent voxel value representing a translation along the first axis for the adjacent voxel.
 The method of claim 2, wherein performing the regression analysis on the time series of translations for a given voxel comprises performing the regression analysis on the time series of translations using a plurality of regressors comprising a first translation along a first axis, a second translation along a second axis, perpendicular to the first axis, and a translation along a third axis, perpendicular to each of the first and second axes, squared values of each of the first, second, and third translations, a delayed transformation of the first translation that is delayed by one interval in the time series of translations for the voxel.

The method of claim 2, further comprising:
resampling simulated spin history and partial volume signals according to the set of estimated motion parameters to determine a simulated signal level change over time for each voxel due to motion artifact arising from spin history and partial volume effects;
wherein performing the regression analysis on the time series of translations for a given voxel comprises performing the regression analysis on the time series of translations using a plurality of regressors comprising the simulated signal level change.
 The method of claim 1, further comprising applying an inplane motion correction to the captured time series of volumetric images by coregistering each twodimensional slice at each time to a set of representative values for the slice to provide motion parameters comprising, for each of the series of twodimensional slices, a first time series representing translation along a first axis within a plane of the slice, a second time series representing translation along a second axis within a plane of the slice and perpendicular to the first axis, and a third time series representing a rotation around a third axis normal to the plane of the slice.

The method of claim 8, wherein the set of estimated motion parameters comprise a fourth time series representing rotation around the first axis, a fifth time series representing rotation around the second axis, and a sixth time series representing a translation along the third axis and the method further comprises:
determining, for each of a plurality of voxels in the region of interest, a time series of motion parameters derived from the first time series for the slice of the series of twodimensional slices to which the voxel belongs, the second time series for the slice of the series of twodimensional slices to which the voxel belongs, the third time series for the slice of the series of twodimensional slices to which the voxel belongs, the fourth time series for the slice of the series of twodimensional slices to which the voxel belongs, the fifth time series for the slice of the series of twodimensional slices to which the voxel belongs, and the sixth time series for the slice of the series of twodimensional slices to which the voxel belongs, and the position of the voxel within the series of twodimensional slices;
performing a regression analysis on each time series of motion parameters, determined for the plurality of voxels in the region of interest, to provide a regression model representing the time series; and
subtracting a set of values generated from the regression model from the time series of volumetric images to provide a set of residual values representing a set of motion correction parameters.

The method of claim 1, further comprising:
applying an initial, volumetric motion correction to the captured time series of volumetric images by coregistering each of the series of threedimensional volumes to a representative set of volumetric data, to provide for each of the series of threedimensional volumes, a set of motion correction data; and
applying the set of motion correction data to the captured time series of volumetric images before calculating a representative value for each voxel of the series of twodimensional slices.

The method of claim 1, wherein the set of estimated motion parameters comprise a first time series representing rotation around a first axis within a plane of the slice, a second time series representing rotation around second axis within a plane of the slice and perpendicular to the first axis, and a third time series representing a translation along a third axis normal to the plane of the slice, the method further comprising:
calculating a first measure of variation for the first time series;
calculating a second measure of variation for the second time series;
calculating a third measure of variation for the third time series;
dividing each value in the first time series by the first measure of deviation to provide a first normalized time series;
dividing each value in the second time series by the second measure of deviation to provide a second normalized time series; and
dividing each value in the third time series by the third measure of deviation to provide a third normalized time series.

The method of claim 11, further comprising:
performing a volumetric registration on the time series of volumetric images of the region of interest to produce volumetric motion parameters for the entire region of interest, the motion parameters including at least a rotation of the volume around the first axis, a rotation of the volume around the second axis, and a translation along a third axis;
fitting the volumetric motion parameters to the first, second, and third normalized time series to provide a scaling factor for each of the first, second, and third normalized time series; and
multiplying the values in each of the first, second, and third normalized time series by its associated scaling factor.
 The method of claim 12, further comprising applying a temporal filter to each of the first normalized time series, the second normalized time series, and the third normalized time series before the volumetric registration is performed.

A magnetic resonance imaging (MRI) system comprising:
a magnetic resonance imaging scanner configured to capture a time series of volumetric images of a region of interest at the imaging system, each volumetric image of the time series of volumetric images being captured as a series of twodimensional slices of the region of interest; and
a system control comprising a processor and a nontransitory computer readable medium storing instructions executable by the processor, the instructions comprising:
a volume simulator configured to calculate a representative value for each voxel over time to create a representative volumetric dataset representing the region of interest and generate, for each slice of the series of twodimensional slices, a simulated volumetric time series including time series data for the slice and the calculated representative value at all times for the other slices of the series of twodimensional slices;
a slicetovolume registration component configured to perform a volumetric registration on each of the simulated volumetric time series to provide a set of estimated motion parameters for the slice associated with the simulated volumetric time series; and
a correction component configured to determine, for each of a plurality of voxels in the region of interest, a time series of translations from the set of estimated motion parameters associated with the slice of the series of twodimensional slices to which the voxel belongs and the position of the voxel within the series of twodimensional slices, perform a regression analysis on each time series of translations, determined for the plurality of voxels in the region of interest, to provide a regression model representing the time series, and subtract a set of values generated from the regression model from the time series of volumetric images to provide a set of residual values representing a set of motion corrected parameters.
 The MRI system of claim 14, wherein the set of estimated motion parameters comprise a first time series representing rotation around a first axis within a plane of the slice, a second time series representing rotation around second axis within a plane of the slice and perpendicular to the first axis, and a third time series representing a translation along a third axis normal to the plane of the slice, and the instructions further comprising a normalization component configured to calculate a first measure of variation for the first time series, calculate a second measure of variation for the second time series, calculate a third measure of variation for the third time series, divide each value in the first time series by the first measure of deviation to provide a first normalized time series, divide each value in the second time series by the second measure of deviation to provide a second normalized time series, and divide each value in the third time series by the third measure of deviation to provide a third normalized time series.
 The MRI system of claim 15, the instructions further comprising an inplane correction component configured to apply an initial, inplane motion correction to the time series of images by coregistering each slice at each time to a set of representative values for the slice to provide, for each of the series of twodimensional slices, a first time series representing translation along a first axis within a plane of the slice, a second time series representing translation along a second axis within a plane of the slice and perpendicular to the first axis, and a third time series representing a rotation around a third axis normal to the plane of the slice.
 The MRI system of claim 15, the correction component being configured to perform the regression analysis on the time series of translations for the voxel and at least one time series associated with an adjacent voxel in a slice adjacent to the slice to which the voxel belongs.

A magnetic resonance imaging (MRI) system comprising:
a magnetic resonance imaging scanner configured to capture a time series of volumetric images of a region of interest at the imaging system, each volumetric image of the time series of volumetric images being captured as a series of twodimensional slices of the region of interest; and
a system control comprising a processor and a nontransitory computer readable medium storing instructions executable by the processor, the instructions comprising:
a volume simulator configured to calculate a representative value for each voxel over time to create a representative volumetric dataset representing the region of interest and generate, for each slice of the series of twodimensional slices, a simulated volumetric time series including time series data for the slice and the calculated representative value at all times for the other slices of the series of twodimensional slices;
a slicetovolume registration component configured to perform a volumetric registration on each of the simulated volumetric time series to provide a set of estimated motion parameters for the slice associated with the simulated volumetric time series, the set of estimated motion parameters comprising a first time series representing rotation around a first axis within a plane of the slice, a second time series representing rotation around second axis within a plane of the slice and perpendicular to the first axis, and a third time series representing a translation along a third axis normal to the plane of the slice; and
a slicewise normalization component configured to calculate a first measure of variation for the first time series, calculate a second measure of variation for the second time series, calculate a third measure of variation for the third time series, divide each value in the first time series by the first measure of deviation to provide a first normalized time series, divide each value in the second time series by the second measure of deviation to provide a second normalized time series, and divide each value in the third time series by the third measure of deviation to provide a third normalized time series.

The MRI system of claim 18, the instructions further comprising performing a volumetric registration component configured to apply a volumetric reconstruction on the time series of volumetric images of the region of interest to produce volumetric motion parameters for the entire region of interest, the motion parameters including at least a rotation of the volume around the first axis, a rotation of the volume around the second axis, and a translation along a third axis; and
a volumetric normalization component being configured to fit the volumetric motion parameters to the first, second, and third normalized time series to provide a scaling factor for each of the first, second, and third normalized time series and multiplying the values in each of the first, second, and third normalized time series by its associated scaling factor.
 The MRI system of claim 18, the slicewise normalization component being configured to apply a temporal filter to each of the first normalized time series, the second normalized time series, and the third normalized time series before the volumetric reconstruction is performed.
Owners (US)

The Cleveland Clinic Foundation
(May 18 2016)
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Applicants

Cleveland Clinic Found
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Inventors

Beall Erik B
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Lowe Mark J
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CPC Classifications

G06N7/005
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G06T2207/10088
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G06T2207/20182
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G06T2207/20201
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G06T2207/30016
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G06T5/001
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G06T7/0012
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G06T7/20
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G06T7/215
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G06T7/38
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Document Preview
 Publication: Mar 27, 2018

Application:
May 9, 2016
US 201615149824 A

Priority:
May 9, 2016
US 201615149824 A

Priority:
May 7, 2015
US 201562158017 P