Brain tractography, derived from diffusion magnetic resonance imaging (dMRI), has become a key tool for studying the structural connectivity of the human brain. This technique infers the pathways of white matter fibers, generating detailed maps that have significantly impacted both neuroscience research and clinical applications. However, technical limitations still hinder its accuracy and widespread clinical adoption, particularly in surgical planning and the diagnosis of neurological disorders. Major challenges include sensitivity to motion artifacts, lack of standardized validation methods, and difficulties in resolving complex fiber configurations such as crossings and bifurcations. This paper presents a critical review of the current state of tractography, along with a novel methodological proposal based on a microstructural convex optimization model. This approach aims to enhance resolution in anatomically complex regions and reduce false positives in fiber reconstruction. Its potential to improve the reliability of tractography and facilitate its effective integration into clinical practice is also discussed.