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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Bone Magnetic Resonance Imaging: From Conventional Methods to AI-Driven Solutions.

Yi He1, Rahman Ud Din1, Lieve Morbée2

  • 1Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.

Journal of Magnetic Resonance Imaging : JMRI
|April 12, 2026
PubMed
Summary

Magnetic resonance imaging (MRI) offers a radiation-free method for bone assessment, overcoming limitations of X-rays. This review details advanced MRI techniques and artificial intelligence applications for improved diagnosis and management of bone diseases.

Keywords:
artificial intelligencebonefracture riskmagnetic resonance imagingosteoporosissequences

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Area of Science:

  • Biomedical Imaging
  • Radiology
  • Musculoskeletal Research

Background:

  • Aging populations and increased life expectancy lead to a rise in bone diseases, creating a significant socioeconomic burden.
  • X-ray imaging is the standard for bone assessment but involves ionizing radiation and poor soft-tissue contrast.
  • Magnetic resonance imaging (MRI) is a promising radiation-free alternative for detailed bone evaluation.

Purpose of the Study:

  • To synthesize recent advancements in bone MRI techniques.
  • To discuss the strengths and limitations of various MRI methods for bone assessment.
  • To highlight the role of MRI in diagnosing and managing bone disorders.

Main Methods:

  • Review of conventional MRI sequences (T1-weighted, T2-weighted).
  • Exploration of advanced MRI sequences (ultrashort echo time, zero echo time).
  • Analysis of metabolic imaging (magnetic resonance spectroscopy, dynamic contrast-enhanced MRI) and hardware innovations (ultra-high-field MRI).
  • Investigation of artificial intelligence (deep learning) applications in bone MRI.

Main Results:

  • Various MRI techniques show potential for detailed bone microstructure and functional status evaluation.
  • Advanced sequences and hardware innovations address inherent challenges in bone MRI.
  • Artificial intelligence enhances MRI by reducing scan time, improving image quality, and increasing diagnostic accuracy.
  • MRI plays a role in early diagnosis, therapeutic monitoring, and management of osteoporosis, osteoarthritis, osteosarcoma, and osteonecrosis.

Conclusions:

  • Bone MRI techniques are rapidly advancing, offering significant improvements over conventional methods.
  • AI integration promises further enhancements in MRI capabilities for bone assessment.
  • This review provides guidance for preclinical research and clinical translation of bone MRI, aiming to improve musculoskeletal disease management.