The evolution of T1-weighted lesion inpainting tools in patients with brain injury: A scoping review

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.