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MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR
Andrew D Linkugel1, Tongyao Wang2, Parna Eshraghi Boroojeni2
1From 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.
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.
Insights
Radiation-free pseudo-CT scans using AI and motion correction show promise for pediatric cranial imaging. These advanced MRI-derived images accurately detect skull fractures and suture issues, offering a safer alternative to CT scans.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Pediatric Radiology
Background:
- Computed tomography (CT) exposes pediatric patients to ionizing radiation.
- Magnetic resonance (MR) imaging is radiation-free but has limitations in producing diagnostic-quality bone images quickly.
- Developing radiation-free imaging alternatives for pediatric cranial assessments is crucial.
Purpose of the Study:
- To evaluate the clinical utility of pseudo-CT images generated from motion-corrected MR imaging.
- To assess if these pseudo-CT images are acceptable for clinical use in pediatric patients.
- To compare the diagnostic accuracy of pseudo-CT with standard CT for cranial evaluations.
Main Methods:
- Recruited pediatric patients undergoing head CT for trauma or suture patency evaluation.
- Acquired 5-minute volumetric MR images using a golden-angle radial technique.
- Applied automated motion correction and deep learning to generate pseudo-CT images from MR data.
- Compared diagnostic performance of pseudo-CT and CT for skull fractures and suture patency.
Main Results:
- Pseudo-CT demonstrated 100% sensitivity and specificity for identifying cranial suture closure.
- For skull fracture detection, pseudo-CT achieved 100% specificity and 90% sensitivity.
- The study included 72 pediatric patients (12 for suture patency, 60 for trauma).
Conclusions:
- Automated motion-corrected, deep learning-generated pseudo-CT images show potential for clinical application in pediatric skull imaging.
- These pseudo-CT images offer high diagnostic accuracy comparable to standard CT scans.
- This radiation-free approach provides a promising alternative for evaluating pediatric cranial conditions.
