Arman Avesta, Anastasia Yendiki1, Vincent Perlbarg2, Lionel Velly3, Omid Khalilzadeh, Louis Puybasset2, Damien Galanaud, Rajiv Gupta4. 1. A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA. 2. Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Université Paris 06 UMCR2, Inserm U1146, CNRS UMR7371, CHU Pitié-Salpêtrière. 3. Department of Anesthesiology and Intensive Care, Groupe Hospitalier Pitié-Salpêtrière, Université Paris 6-Pierre-et-Marie-Curie, Paris, France. 4. Advanced X-ray Imaging Science Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Abstract
OBJECTIVE: This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. METHODS: We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. RESULTS: The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). CONCLUSIONS: Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.
OBJECTIVE: This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. METHODS: We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. RESULTS: The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). CONCLUSIONS: Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.
Authors: Anastasia Yendiki; Patricia Panneck; Priti Srinivasan; Allison Stevens; Lilla Zöllei; Jean Augustinack; Ruopeng Wang; David Salat; Stefan Ehrlich; Tim Behrens; Saad Jbabdi; Randy Gollub; Bruce Fischl Journal: Front Neuroinform Date: 2011-10-14 Impact factor: 4.081