Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone

Abstract

Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 μm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a ‘ratio metric’ determined from a few k -space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 01, 2018
Source ID
10.1098/rsos.180563

Entities

People

  • Chantal Nguyen
  • Jean M. Carlson
  • Kimberly J Schlesinger
  • Koichi Masuda
  • Kristin M. James
  • Robert L. Sah
  • Timothy W. James

Organizations

  • Army Research Office
  • Division of Industrial Innovation & Partnerships
  • University of California, San Diego
  • University of California, Santa Barbara

Tags

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • Space