WarpDrive: Improving spatial normalization using manual refinements

• Published in: Medical image analysis 2024-01, Vol.91, p.103041-103041, Article 103041
• Main Authors: Oxenford, Simón, Ríos, Ana Sofía, Hollunder, Barbara, Neudorfer, Clemens, Boutet, Alexandre, Elias, Gavin J B, Germann, Jurgen, Loh, Aaron, Deeb, Wissam, Salvato, Bryan, Almeida, Leonardo, Foote, Kelly D, Amaral, Robert, Rosenberg, Paul B, Tang-Wai, David F, Wolk, David A, Burke, Anna D, Sabbagh, Marwan N, Salloway, Stephen, Chakravarty, M Mallar, Smith, Gwenn S, Lyketsos, Constantine G, Okun, Michael S, Anderson, William S, Mari, Zoltan, Ponce, Francisco A, Lozano, Andres, Neumann, Wolf-Julian, Al-Fatly, Bassam, Horn, Andreas
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - diagnostic imaging; Brain - diagnostic imaging; Brain Mapping - methods; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Magnetic Resonance Imaging - methods
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2023.103041

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.