Anatomically Accurate, High-Resolution Modeling of the Human Index Finger Using In Vivo Magnetic Resonance Imaging

Tomography. 2022 Sep 21;8(5):2347-2359. doi: 10.3390/tomography8050196.

Abstract

Anatomically accurate models of a human finger can be useful in simulating various disorders. In order to have potential clinical value, such models need to include a large number of tissue types, identified by an experienced professional, and should be versatile enough to be readily tailored to specific pathologies. Magnetic resonance images were acquired at ultrahigh magnetic field (7 T) with a radio-frequency coil specially designed for finger imaging. Segmentation was carried out under the supervision of an experienced radiologist to accurately capture various tissue types (TTs). The final segmented model of the human index finger had a spatial resolution of 0.2 mm and included 6,809,600 voxels. In total, 15 TTs were identified: subcutis, Pacinian corpuscle, nerve, vein, artery, tendon, collateral ligament, volar plate, pulley A4, bone, cartilage, synovial cavity, joint capsule, epidermis and dermis. The model was applied to the conditions of arthritic joint, ruptured tendon and variations in the geometry of a finger. High-resolution magnetic resonance images along with careful segmentation proved useful in the construction of an anatomically accurate model of the human index finger. An example illustrating the utility of the model in biomedical applications is shown. As the model includes a number of tissue types, it may present a solid foundation for future simulations of various musculoskeletal disease processes in human joints.

Keywords: high-resolution MRI; segmentation; simulation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Fingers
  • Humans
  • Magnetic Resonance Imaging* / methods
  • Radio Waves
  • Tendon Injuries* / pathology
  • Tendons / diagnostic imaging
  • Tendons / pathology

Grants and funding

The authors would like to thank the Slovenian Research Agency for financial support (Research core funding No. P1-0389).