Rapid Musculoskeletal MRI in 2026: Clinical Integration of Deep Learning Reconstruction.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Musculoskeletal Radiology

Background:

• Musculoskeletal MRI acquisition times have decreased due to hardware and acceleration strategies.
• Conventional acceleration methods face limitations like noise and artifacts at higher factors.
• Deep learning (DL) based reconstruction offers a solution to these limitations.

Purpose of the Study:

• To evaluate the impact of deep learning reconstruction on musculoskeletal MRI.
• To assess the feasibility of achieving ultra-fast MRI protocols (<10 minutes).
• To determine if DL reconstruction maintains or improves diagnostic performance.

Main Methods:

• Implementation of DL reconstruction and superresolution techniques.
• Application across various musculoskeletal MRI protocols, field strengths, and vendors.
• Comparison of DL-accelerated MRI with conventional protocols.

Main Results:

• DL reconstruction enables higher acceleration factors with improved signal-to-noise ratios and reduced artifacts.
• Comprehensive musculoskeletal MRI protocols can be completed in under 10 minutes.
• Early validation studies show preserved or improved diagnostic performance.

Conclusions:

• Deep learning reconstruction is a key technology for accelerating musculoskeletal MRI.
• This technology allows for efficient MRI protocols without compromising diagnostic quality.
• Further development and validation will expand the role of DL in clinical MRI.