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Toward optimal inline respiratory motion correction for in vivo cardiac diffusion tensor MRI using symmetric and inverse‐consistent deformable image registration.

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Title: Toward optimal inline respiratory motion correction for in vivo cardiac diffusion tensor MRI using symmetric and inverse‐consistent deformable image registration.
Authors: Liu, Yuchi1 (AUTHOR), Kara, Danielle2,3 (AUTHOR), Garrett, Thomas2 (AUTHOR), Chen, Shi2 (AUTHOR), Wee, Daniel2 (AUTHOR), Jin, Ning4 (AUTHOR), Speier, Peter5 (AUTHOR), Nakagawa, Hiroshi6 (AUTHOR), Santangeli, Pasquale6 (AUTHOR), Bolen, Michael A.3,6 (AUTHOR), Wazni, Oussama6 (AUTHOR), Hanna, Mazen6 (AUTHOR), Tang, W. H. Wilson2,6 (AUTHOR), Tandon, Animesh2,3,7,8 (AUTHOR), Kwon, Deborah2,3,6 (AUTHOR), Bi, Xiaoming9 (AUTHOR), Nguyen, Christopher2,3,6,7 (AUTHOR) nguyenc6@ccf.org
Source: Magnetic Resonance in Medicine. Aug2025, Vol. 94 Issue 2, p724-734. 11p.
Subjects: Diffusion tensor imaging, Cardiac magnetic resonance imaging, Diffusion magnetic resonance imaging, Diffusion gradients, Image registration
Abstract: Purpose: This study aims to develop a free‐breathing cardiac DTI method with fast and robust motion correction. Methods: Two proposed image registration‐based motion correction (MOCO) strategies, MOCONaive and MOCOAvg, were applied to diffusion‐weighted images acquired with M2 diffusion gradients under free‐breathing. The effectiveness of MOCO was assessed by tracking epicardium pixel positions across image frames. Resulting mean diffusivity (MD), fractional anisotropy (FA), and helix angle (HA) maps were compared against a previous low rank tensor based MOCO method (MOCOLRT) in 20 healthy volunteers and two patients scanned at 3 T. Results: Compared with the MOCOLRT method, both proposed MOCONaive and MOCOAvg methods generated slightly lower MD and helix angle transmurality (HAT) magnitude values, and significantly lower FA values. Moreover, both proposed MOCO methods achieved significantly smaller SDs of MD and FA values, and more smoothly varying helical structure in HA maps in healthy volunteers, indicating more effective MOCO. Elevated MD, decreased FA, and lower HAT magnitude were observed in two patients compared with healthy volunteers. Furthermore, the computing speed of image registration‐based MOCO is twice as fast as the LRT method on the same dataset and same workstation. Conclusion: This study demonstrates a fast and robust motion correction approach using image registration for in vivo free‐breathing cardiac DTI. It improves the quality of quantitative diffusion maps and will facilitate clinical translation of cardiac DTI. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: Toward optimal inline respiratory motion correction for in vivo cardiac diffusion tensor MRI using symmetric and inverse‐consistent deformable image registration.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yuchi%22">Liu, Yuchi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kara%2C+Danielle%22">Kara, Danielle</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Garrett%2C+Thomas%22">Garrett, Thomas</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Shi%22">Chen, Shi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wee%2C+Daniel%22">Wee, Daniel</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Ning%22">Jin, Ning</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Speier%2C+Peter%22">Speier, Peter</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nakagawa%2C+Hiroshi%22">Nakagawa, Hiroshi</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Santangeli%2C+Pasquale%22">Santangeli, Pasquale</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bolen%2C+Michael+A%2E%22">Bolen, Michael A.</searchLink><relatesTo>3,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wazni%2C+Oussama%22">Wazni, Oussama</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hanna%2C+Mazen%22">Hanna, Mazen</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+W%2E+H%2E+Wilson%22">Tang, W. H. Wilson</searchLink><relatesTo>2,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tandon%2C+Animesh%22">Tandon, Animesh</searchLink><relatesTo>2,3,7,8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kwon%2C+Deborah%22">Kwon, Deborah</searchLink><relatesTo>2,3,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bi%2C+Xiaoming%22">Bi, Xiaoming</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Christopher%22">Nguyen, Christopher</searchLink><relatesTo>2,3,6,7</relatesTo> (AUTHOR)<i> nguyenc6@ccf.org</i>
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Aug2025, Vol. 94 Issue 2, p724-734. 11p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Diffusion+tensor+imaging%22">Diffusion tensor imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiac+magnetic+resonance+imaging%22">Cardiac magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+magnetic+resonance+imaging%22">Diffusion magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+gradients%22">Diffusion gradients</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This study aims to develop a free‐breathing cardiac DTI method with fast and robust motion correction. Methods: Two proposed image registration‐based motion correction (MOCO) strategies, MOCONaive and MOCOAvg, were applied to diffusion‐weighted images acquired with M2 diffusion gradients under free‐breathing. The effectiveness of MOCO was assessed by tracking epicardium pixel positions across image frames. Resulting mean diffusivity (MD), fractional anisotropy (FA), and helix angle (HA) maps were compared against a previous low rank tensor based MOCO method (MOCOLRT) in 20 healthy volunteers and two patients scanned at 3 T. Results: Compared with the MOCOLRT method, both proposed MOCONaive and MOCOAvg methods generated slightly lower MD and helix angle transmurality (HAT) magnitude values, and significantly lower FA values. Moreover, both proposed MOCO methods achieved significantly smaller SDs of MD and FA values, and more smoothly varying helical structure in HA maps in healthy volunteers, indicating more effective MOCO. Elevated MD, decreased FA, and lower HAT magnitude were observed in two patients compared with healthy volunteers. Furthermore, the computing speed of image registration‐based MOCO is twice as fast as the LRT method on the same dataset and same workstation. Conclusion: This study demonstrates a fast and robust motion correction approach using image registration for in vivo free‐breathing cardiac DTI. It improves the quality of quantitative diffusion maps and will facilitate clinical translation of cardiac DTI. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mrm.30485
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        Text: English
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