The evolution of T1-weighted lesion inpainting tools in patients with brain injury: A scoping review.
Authors
Affiliations (5)
Affiliations (5)
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia. Electronic address: [email protected].
- Brain and Mental Health, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; The School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia.
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA; Centre for Healthy Brain Aging, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, England, UK.
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT.
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia.
Abstract
Focal brain lesions from Acquired Brain Injuries (ABIs) present as regions of abnormal signal intensity on T1-weighted Magnetic Resonance Imaging (MRI) scans. These can disrupt automated neuroimaging processing algorithms traditionally developed on and for healthy brains. Lesion filling (or inpainting) can replace lesioned image voxels with signal intensities approximating healthy tissue. This creates a 'lesion free' brain to use as input to the image processing algorithms thus aiming to reduce the presence of lesion induced errors. This scoping review provides a detailed overview of the available inpainting tools for use in neuroimaging analysis of patients with ABI. First, we define lesion inpainting and highlight its importance for pre-processing of MRI scans. Next, we classify the papers resulting from our search (24 in total) into: (a) Traditional Methods (Local Diffusion, Global Diffusion, Search Patch-Based, a priori Patch-Based, or Low Rank Sparse Decomposition) and (b) Deep Learning methods (Convolutional Neural Networks, Generative Adversarial Networks, or Denoising Diffusion Models). We then discuss the strengths and limitations of each different inpainting method. Finally, we provide recommendations for both the use, and development of inpainting tools, to increase the adoption of lesion inpainting across ABI studies.