Enhancing neuroradiology education with systematic image and video sequences

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Authors: Wieslaw Lucjan Nowinski
Cited by: 1
References: 28

Abstract

Neuroanatomy remains one of the most challenging areas in medical education, including neuroradiology, due to its complexity and limitations of traditional teaching methods. This study proposes a novel approach based on systematic image and video sequences to enhance the presentation, exploration, communication, learning, and teaching of neuroanatomy. Systematic image sequences are ordered sets of spatially and contextually correlated images characterized by five features: anatomical content, parcellation, annotation, location, and dimensionality. Five elementary sequence types are introduced: appearance, contextual, multi-dimensional, dissection, and special, which can be combined into composite (homogeneous/heterogeneous, uni-view/multi-view) sequences. These sequences are presented in interactive, single multi-image, and automated modes, and extended to video formats. The sequences were created using a high-resolution, fully parcellated 3D atlas of the human brain, head, and neck. 12 image sequences (51 images) and two video sequences are presented and discussed, along with references to three major resources of ready-to-use sequences: NOW in BRAIN repository (over 8600 3D neuroimages), ebook Neuroanatomy Made Easy (over 350 sequences), and the latest edition of Gray’s Anatomy /Chapter_28. The proposed sequences enhance visualization and understanding of anatomical structures and relationships. Appearance sequences improve recognition through parcellation and labeling, while contextual sequences reveal spatial relationships. Dissection and cortical opening sequences expose otherwise hidden structures, and multi-dimensional sequences bridge 2D radiology with 3D anatomy. Hence, the method elucidates complex anatomical organization, including cortico-vascular relationships, ventricular-venous alignment, and cranial nerve pathways. Overall, systematic image-video sequences provide a conceptually straightforward, easy-to-use, simply integrable, and cost-effective framework enhancing spatial understanding and interpretation, particularly in neuroradiology.

Fields of Study & Subjects

Anatomy and Medical Technology Surgical Simulation and Training AI in cancer detection

Publication details

Journal / source
The Neuroradiology Journal
Publisher
SAGE Publications
Pages
19714009261460775–19714009261460775
Publication date
14 June 2026
Language
EN