Prediction and Reduction of Motion Artifacts in Free-Breathing Dynamic Contrast Enhanced CT Perfusion Imaging of Primary and Metastatic Intrahepatic Tumors

Rationale and Objective

To develop and evaluate a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values.

Materials and Methods

Thirty patients (23 males, 7 females, median age of 74 years) with primary or metastatic intrahepatic tumors underwent dynamic contrast enhanced CT scans with axial shuttling. A semiautomatic respiratory motion correction algorithm was applied to align the acquired images along the z-axis. Perfusion maps were generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves were calculated.

Results

Precorrection RMSD correlated positively with magnitude of change in functional values resulting from motion. Blood flow, arterial blood flow, and permeability surface product were significantly increased in tumor compared to normal tissue ( P < .05), blood volume was significantly reduced in tumor compared to normal tissue ( P < .05). In a subgroup of patients with high-amplitude motion significant difference was observed between uncorrected and motion correction blood flow maps.

Conclusions

Patients can breathe freely during hepatic perfusion imaging if retrospective motion correction is applied to reduce motion artifacts. RMSD provides a regional assessment of motion induced artifacts in liver perfusion maps.

Key points:
1. Allowing patients to breathe freely during liver DCE-CT acquisition is possible if images are corrected for motion before functional maps are generated.
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Materials and methods

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Results

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Table 1

Perfusion Parameters in Tumor Versus Normal Tissue

Functional Parameter Normal Tissue Tumor_P_ Value ∗ BF (mlmin⋅100g) (

min

⋅

100

) 140 ± 45 182 ± 71 .006 BV (ml100g) (

100

) 37 ± 7 28 ± 9 <.001 HAF (%) 33 ± 9 46 ± 11 <.001 PS (mlmin⋅100g) (

min

⋅

100

) 13 ± 5 19 ± 9 .002 HABF (mlmin⋅100g) (

min

⋅

100

) 47 ± 25 85 ± 44 <.001 PVBF (mlmin⋅100g) (

min

⋅

100

) 93 ± 31 97 ± 40 .6

BF, hepatic blood flow; BV, hepatic blood volume; HABF, hepatic arterial blood flow; HAF, hepatic arterial fraction (fraction of total blood flow that is arterial); PS, permeability surface product; PVBF, portal venous blood flow.

Mean perfusion values for entire patient group after motion correction. Values are mean ± standard deviation.

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Table 2

Normal Tissue Versus Tumor in Patients with High- and Low-Amplitude Motion

Functional Parameter Motion Normal Tissue Tumor_P_ Value † BF (mlmin⋅100g) (

min

⋅

100

) High Uncorrected 150 ± 82 191 ± 73 .10 ∗ Corrected 160 ± 56 222 ± 70 .03 ∗ Low Uncorrected 113 ± 26 166 ± 72 .002 Corrected 130 ± 36 163 ± 64 .03 BV (ml100g) (

100

) High Uncorrected 37 ± 6 29 ± 9 .006 Corrected 39 ± 5 31 ± 8 .003 Low Uncorrected 35 ± 6 27 ± 10 <.001 Corrected 37 ± 8 27 ± 10 <.001 HAF (%) High Uncorrected 35 ± 11 50 ± 20 .007 Corrected 29 ± 12 46 ± 12 <.001 Low Uncorrected 34 ± 10 44 ± 9 <.001 Corrected 35 ± 7 47 ± 11 <.001 PS (mlmin⋅100g) (

min

⋅

100

) High Uncorrected 19 ± 10 22 ± 14 .21 Corrected 16 ± 6 22 ± 12 .08 Low Uncorrected 12 ± 6 16 ± 8 .01 Corrected 12 ± 3 18 ± 8 .002

BF, hepatic blood flow; BV, hepatic blood volume; HAF, hepatic arterial fraction; PS, permeability surface product.

Values are mean ± standard deviation. Paired t -test was used to determine significance. (fraction of total blood flow that is arterial).

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$Figure 5, Absolute difference in blood flow (a) , blood volume (b) , hepatic arterial blood flow (c) , portal veinous blood flow (d) , hepatic arterial fraction (e) and permeability surface product (f) before and after motion correction versus pre-correction root mean square deviation (RMSD) averaged over 30 patients for the whole liver including tumor. The area of each circle is proportional to the number of voxels sampled. Differences from motion increase with RMSD for all four functional values demonstrating the ability of RMSD to predict local motion induced artifacts.$

$Figure 6, Comparison between maps calculated from one patient with severe breathing motion (first and second column) and one with shallow breathing motion (third column). Uncorrected (a) averaged computed tomography (CT), (b) root mean square deviation (RMSD), (c) blood flow, and (d) hepatic arterial fraction maps are from patient 24, and (e–h) are the corresponding respiratory motion correction functional maps. Note the more uniform blood flow and hepatic arterial fraction values throughout the liver in the respiratory motion correction maps versus those in the motion uncorrected maps and the reduction in root mean square deviation values at organ edge. (i–l) Motion uncorrected functional maps of patient 12. The similarity of the root mean square deviation maps (f) and (j) suggests that CT perfusion scan of patient 12 had less respiratory liver motion than patient 24 and that the developed image registration algorithm was able to reduce the respiratory motion in patient 24 to the same level as that in patient 12. The high blood flow and hepatic arterial fraction in the anterior part of the liver of patient 12 is the tumor, which appears as being marginally hypodense in the averaged CT image (i) .$

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Discussion

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Acknowledgments

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Appendix 1

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r=∑nmaxn=1∑mmaxm=1(Refm,n−Ref¯¯¯¯¯)(Testm,n−Test¯¯¯¯¯¯¯)∑nmaxn=1∑mmaxm=1(Refm,n−Ref¯¯¯¯¯)2∑nmaxn=1∑mmaxm=1(Testm,n−Test¯¯¯¯¯¯¯)2√ r

∑

max

∑

max

(

Ref

−

Ref

)

(

Test

−

Test

)

∑

max

∑

max

(

Ref

−

Ref

)

∑

max

∑

max

(

Test

−

Test

)

Calculation of two-dimensional discrete cross-correlation coefficient, r = Ref is the selected reference image, and test is the image being tested for correlation.

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Step 1

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Step 2

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Step 3

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Step 4

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Step 5

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Prediction and Reduction of Motion Artifacts in Free-Breathing Dynamic Contrast Enhanced CT Perfusion Imaging of Primary and Metastatic Intrahepatic Tumors

Rationale and Objective

Materials and Methods

Results

Conclusions

Key points:

Materials and methods

Results

Discussion

Acknowledgments

Appendix 1

Step 1

Step 2

Step 3

Step 4

Step 5

References

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