Validation of diffusion tensor imaging for diagnosis of traumatic brain injury
Background and Purpose: With an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528624000062 |
| _version_ | 1827246292341882880 |
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| author | Micah Daniel Vinet Alexander Samir Ayoub Russell Chow Joseph C. Wu |
| author_facet | Micah Daniel Vinet Alexander Samir Ayoub Russell Chow Joseph C. Wu |
| author_sort | Micah Daniel Vinet |
| collection | DOAJ |
| description | Background and Purpose: With an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the use of DTI with Statistical Parametric Mapping (SPM) for Traumatic Brain Injury (TBI) supporting its use as a diagnostic tool. Materials and Methods: This study was retrospective and compared controls to patients clinically diagnosed with TBI. Forty-two controls (mean age = 34.1; range, 19 - 58; 28 Males and 13 Females) were screened (n = 41) for cognitive impairment and neurological abnormality. Two cohorts, each of eighteen patients (first cohort: mean age, 41.8; range, 23 - 70; 9 Males and 9 Females; second cohort: mean age, 45.7; range, 23 - 68; 9 Males and 9 Females) clinically diagnosed with TBI (n = 36) were pooled. DTI image acquisition was obtained using a 3 Tesla MRI scanner. DTI images were analyzed through voxel-based t-tests using SPM comparing each individual to the normative control group to generate z-maps for each individual control and each individual patient with a TBI. Test statistics were ranged for p-values (0.001-0.05) and cluster extent values (0, 30, 60, 65, 70, 75). Area Underneath A Receiver Operating Characteristic Curve (AUCROC) was used to validate diagnostic capability. AUCROC analysis was conducted on all sets of p-value and extent threshold values. Significance of results was determined by examining the AUCROC values. Results and Conclusions: A maximal AUCROC of 1.000 was obtained across the p-value range and cluster extent thresholding values specified across the two cohorts. The high AUCROC supports validation of the methodology presented and the use of diffusion tensor imaging and SPM as an adjunct diagnostic tool for TBI. |
| first_indexed | 2024-04-24T11:56:40Z |
| format | Article |
| id | doaj.art-8c20e447923d4c95b0f1e131dba85a54 |
| institution | Directory Open Access Journal |
| issn | 2772-5286 |
| language | English |
| last_indexed | 2025-03-21T22:57:38Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj.art-8c20e447923d4c95b0f1e131dba85a542024-05-23T04:57:13ZengElsevierNeuroscience Informatics2772-52862024-06-0142100161Validation of diffusion tensor imaging for diagnosis of traumatic brain injuryMicah Daniel Vinet0Alexander Samir Ayoub1Russell Chow2Joseph C. Wu3Henry Samueli School of Engineering, University of California, Irvine, CA 92697-4550, United States; Henry Samueli School of Bioengineering University of California, Los Angeles, CA, 90095, United States; Corresponding author at: Henry Samueli School of Engineering, University of California, Irvine, CA 92697-4550, United States.Henry Samueli School of Engineering, University of California, Irvine, CA 92697-4550, United States; Creighton University, School of Medicine, Phoenix Health Sciences Campus, Phoenix, AZ 85012, United StatesJohns Hopkins School of Engineering, United StatesDepartment of Psychiatry and Human Behavior, University of California, Irvine, CA 92697-4550, United StatesBackground and Purpose: With an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the use of DTI with Statistical Parametric Mapping (SPM) for Traumatic Brain Injury (TBI) supporting its use as a diagnostic tool. Materials and Methods: This study was retrospective and compared controls to patients clinically diagnosed with TBI. Forty-two controls (mean age = 34.1; range, 19 - 58; 28 Males and 13 Females) were screened (n = 41) for cognitive impairment and neurological abnormality. Two cohorts, each of eighteen patients (first cohort: mean age, 41.8; range, 23 - 70; 9 Males and 9 Females; second cohort: mean age, 45.7; range, 23 - 68; 9 Males and 9 Females) clinically diagnosed with TBI (n = 36) were pooled. DTI image acquisition was obtained using a 3 Tesla MRI scanner. DTI images were analyzed through voxel-based t-tests using SPM comparing each individual to the normative control group to generate z-maps for each individual control and each individual patient with a TBI. Test statistics were ranged for p-values (0.001-0.05) and cluster extent values (0, 30, 60, 65, 70, 75). Area Underneath A Receiver Operating Characteristic Curve (AUCROC) was used to validate diagnostic capability. AUCROC analysis was conducted on all sets of p-value and extent threshold values. Significance of results was determined by examining the AUCROC values. Results and Conclusions: A maximal AUCROC of 1.000 was obtained across the p-value range and cluster extent thresholding values specified across the two cohorts. The high AUCROC supports validation of the methodology presented and the use of diffusion tensor imaging and SPM as an adjunct diagnostic tool for TBI.http://www.sciencedirect.com/science/article/pii/S2772528624000062Traumatic brain injuryDiffusion tensor imagingArea under the operating characteristic curveStatistical parametric mappingImage segmentationNeuroradiology |
| spellingShingle | Micah Daniel Vinet Alexander Samir Ayoub Russell Chow Joseph C. Wu Validation of diffusion tensor imaging for diagnosis of traumatic brain injury Neuroscience Informatics Traumatic brain injury Diffusion tensor imaging Area under the operating characteristic curve Statistical parametric mapping Image segmentation Neuroradiology |
| title | Validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| title_full | Validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| title_fullStr | Validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| title_full_unstemmed | Validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| title_short | Validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| title_sort | validation of diffusion tensor imaging for diagnosis of traumatic brain injury |
| topic | Traumatic brain injury Diffusion tensor imaging Area under the operating characteristic curve Statistical parametric mapping Image segmentation Neuroradiology |
| url | http://www.sciencedirect.com/science/article/pii/S2772528624000062 |
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