{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T05:16:44Z","timestamp":1780723004648,"version":"3.54.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":95,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000005","name":"United States Department of Defense","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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 \u2265 7 (positive) and poor recovery (negative) as 3 \u2264 GOSE \u2264 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 \u00b1 0.10) is statistically significant (P &amp;lt; 0.05) compared to using CT alone (AUC = 0.68 \u00b1 0.13). The significant improvement from using the combination of real CT and synthetic T1-MRI in sensitivity (SEN = 0.95 \u00b1 0.07) and overall performance metrics, such as F1-score (F1 = 0.84 \u00b1 0.03), suggests that the proposed approach provides a robust and effective prognostic approach compared to using CT alone (SEN = 0.83 \u00b1 0.18 and F1 = 0.76 \u00b1 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.<\/jats:p>","DOI":"10.1093\/braincomms\/fcag123","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:43:15Z","timestamp":1775216595000},"source":"Crossref","is-referenced-by-count":1,"title":["Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study"],"prefix":"10.1093","volume":"8","author":[{"given":"Yiming","family":"Che","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University , Tempe, AZ 85281 ,","place":["USA"]},{"name":"ASU-Mayo Center for Innovative Imaging , Tempe, AZ 85281 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amogh Manoj","family":"Joshi","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University , Tempe, AZ 85281 ,","place":["USA"]},{"name":"ASU-Mayo Center for Innovative Imaging , Tempe, AZ 85281 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-2395","authenticated-orcid":false,"given":"Jay","family":"Shah","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University , Tempe, AZ 85281 ,","place":["USA"]},{"name":"ASU-Mayo Center for Innovative Imaging , Tempe, AZ 85281 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-215X","authenticated-orcid":false,"given":"Md Mahfuzur","family":"Rahman Siddiquee","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University , Tempe, AZ 85281 ,","place":["USA"]},{"name":"ASU-Mayo Center for Innovative Imaging , Tempe, AZ 85281 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine D","family":"Chong","sequence":"additional","affiliation":[{"name":"ASU-Mayo Center for Innovative Imaging , Tempe, AZ 85281 ,","place":["USA"]},{"name":"Department of Neurology, Mayo Clinic , Phoenix, AZ 85054 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona","family":"Nikolova","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Phoenix, AZ 85054 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gina","family":"Dumkrieger","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Phoenix, AZ 85054 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University , Tempe, AZ 85281 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