Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study.

Overview

1. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
2. ASU-Mayo Center for Innovative Imaging, Tempe, AZ 85281, USA
3. Department of Neurology, Mayo Clinic, Phoenix, AZ 85054, USA

Abstract

To enhance the prediction of traumatic brain injury (mTBI) outcomes, we propose a deep learning approach that integrates brain computed tomography (CT) scans with corresponding synthetic T1-weighted magnetic resonance imaging (T1-MRI). Our method significantly outperforms the prediction using CT scans alone. TRACK-TBI Pilot dataset, which includes imaging and clinical outcome data from patients with TBI, is studied. The hypothesis is brain CT and T1-MRI complement each other and together will improve TBI prognosis compared to using either CT or T1-MRI alone. Since CT and T1-MRI may not be available for the same individual, we employed a specialized version of a generative adversarial network (GAN), known as fixed-point GAN (FP-GAN). FP-GAN was trained using unpaired CT and T1-MRI scans to generate synthetic T1-MRIs from real CT scans. This process produced pseudo-paired CT-MRI data, which was then used to train a deep learning classifier for outcome prediction. The classifier consists of dual parallel 3D ResNet-18 models, each independently processing T1-MRI and CT scans. We used Glasgow Outcome Scale-Extended (GOSE) scores at 3 months post-TBI as the measure of patient outcomes. To avoid data leakage, the subjects used in FP-GAN and ResNet-18 model have no overlap. We further divided the paired data, allocating 69 samples for 5-fold cross-validation and 17 samples for testing. Prognostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score (the harmonic mean of precision and recall), sensitivity (true positive rate) and specificity (true negative rate). For binary classification, we defined good recovery as GOSE ≥ 7 (positive) and poor recovery (negative) as 3 ≤ GOSE ≤ 6. Accordingly, our training set consists of 24 subjects with poor recovery and 45 subjects with good recovery, while the testing set includes 5 subjects with poor recovery and 12 subjects with good recovery. A DeLong test on AUC confirms that the improvement from incorporating synthetic T1-MRI (AUC = 0.76 ± 0.10) is statistically significant (P < 0.05) compared to using CT alone (AUC = 0.68 ± 0.13). The significant improvement from using the combination of real CT and synthetic T1-MRI in sensitivity (SEN = 0.95 ± 0.07) and overall performance metrics, such as F1-score (F1 = 0.84 ± 0.03), suggests that the proposed approach provides a robust and effective prognostic approach compared to using CT alone (SEN = 0.83 ± 0.18 and F1 = 0.76 ± 0.07). This pilot research demonstrates the potential of a deep learning-based harmonization model to bridge the gap between CT and T1-MRI in TBI assessment. By integrating synthetic T1-MRI with CT, prediction performance is substantially enhanced.

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Data

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Data availability

All data used in this study were obtained from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System. Access to the FITBIR datasets requires proper authorization and adherence to their data use agreements. Researchers interested in accessing the data can apply through the FITBIR Data Access Request process. Data sharing is not applicable to this article as no new data were created or analysed in this study. Model code, training scripts and pre/postprocessing pipelines are available at: https://github.com/SoloChe/TBI-Recovery-Prediction-Harmonization.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 1 funder, 42 references.

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This paper

Che, Y., Joshi, A. M., Shah, J., Rahman Siddiquee, M. M., Chong, C. D., Nikolova, S., Dumkrieger, G., Li, B., Wu, T., & Schwedt, T. J. (2026). Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study. Brain communications, 8(2), fcag123. https://doi.org/10.1093/braincomms/fcag123

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