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Research ArticleArtificial Intelligence

MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images

Andrew D. Linkugel, Tongyao Wang, Parna Eshraghi Boroojeni, Cihat Eldeniz, Yasheng Chen, Gary B. Skolnick, Paul K. Commean, Corinne M. Merrill, Jennifer M. Strahle, Manu S. Goyal, Hongyu An and Kamlesh B. Patel
American Journal of Neuroradiology September 2024, 45 (9) 1284-1290; DOI: https://doi.org/10.3174/ajnr.A8335
Andrew D. Linkugel
aFrom the Division of Plastic and Reconstructive Surgery (A.D.L., G.B.S., C.M.M., K.B.P.), Washington University in St. Louis, St. Louis, Missouri
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Tongyao Wang
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Parna Eshraghi Boroojeni
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Cihat Eldeniz
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Yasheng Chen
cDepartment of Neurology (Y.C., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Gary B. Skolnick
aFrom the Division of Plastic and Reconstructive Surgery (A.D.L., G.B.S., C.M.M., K.B.P.), Washington University in St. Louis, St. Louis, Missouri
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Paul K. Commean
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Corinne M. Merrill
aFrom the Division of Plastic and Reconstructive Surgery (A.D.L., G.B.S., C.M.M., K.B.P.), Washington University in St. Louis, St. Louis, Missouri
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Jennifer M. Strahle
dDepartment of Neurosurgery (J.M.S.), Washington University in St. Louis, St. Louis, Missouri.
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Manu S. Goyal
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Hongyu An
bMallinckrodt Institute of Radiology (T.W., P.E.B., C.E., P.K.C., M.S.G., H.A.), Washington University in St. Louis, St. Louis, Missouri
cDepartment of Neurology (Y.C., H.A.), Washington University in St. Louis, St. Louis, Missouri
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Kamlesh B. Patel
aFrom the Division of Plastic and Reconstructive Surgery (A.D.L., G.B.S., C.M.M., K.B.P.), Washington University in St. Louis, St. Louis, Missouri
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Abstract

BACKGROUND AND PURPOSE: CT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce diagnostic-quality images of bone on a timeline suitable for clinical use. We developed automated motion correction and use deep learning to generate pseudo-CT images from MR images. We aim to evaluate whether motion-corrected pseudo-CT produces cranial images that have potential to be acceptable for clinical use.

MATERIALS AND METHODS: Patients younger than age 18 who underwent CT imaging of the head for either trauma or evaluation of cranial suture patency were recruited. Subjects underwent a 5-minute golden-angle stack-of-stars radial volumetric interpolated breath-hold MR image. Motion correction was applied to the MR imaging followed by a deep learning-based method to generate pseudo-CT images. CT and pseudo-CT images were evaluated and, based on indication for imaging, either presence of skull fracture or cranial suture patency was first recorded while viewing the MR imaging–based pseudo-CT and then recorded while viewing the clinical CT.

RESULTS: A total of 12 patients underwent CT and MR imaging to evaluate suture patency, and 60 patients underwent CT and MR imaging for evaluation of head trauma. For cranial suture patency, pseudo-CT had 100% specificity and 100% sensitivity for the identification of suture closure. For identification of skull fractures, pseudo-CT had 100% specificity and 90% sensitivity.

CONCLUSIONS: Our early results show that automated motion-corrected and deep learning–generated pseudo-CT images of the pediatric skull have potential for clinical use and offer a high level of diagnostic accuracy when compared with standard CT scans.

ABBREVIATIONS:

AO
AO Foundation
BB
black bone
GA-VIBE
golden-angle stack-of-stars radial volumetric interpolated breath-hold examination
GRE
gradient-echo
ResUNet
residual U-Net
UTE
ultrashort echo
ZTE
zero-echo time
  • © 2024 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 45 (9)
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Cite this article
Andrew D. Linkugel, Tongyao Wang, Parna Eshraghi Boroojeni, Cihat Eldeniz, Yasheng Chen, Gary B. Skolnick, Paul K. Commean, Corinne M. Merrill, Jennifer M. Strahle, Manu S. Goyal, Hongyu An, Kamlesh B. Patel
MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images
American Journal of Neuroradiology Sep 2024, 45 (9) 1284-1290; DOI: 10.3174/ajnr.A8335

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Motion-Corrected vs Deep Learning Pseudo-CT Images
Andrew D. Linkugel, Tongyao Wang, Parna Eshraghi Boroojeni, Cihat Eldeniz, Yasheng Chen, Gary B. Skolnick, Paul K. Commean, Corinne M. Merrill, Jennifer M. Strahle, Manu S. Goyal, Hongyu An, Kamlesh B. Patel
American Journal of Neuroradiology Sep 2024, 45 (9) 1284-1290; DOI: 10.3174/ajnr.A8335
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