Optimal detection of scapholunate ligament tears with MRI. Academic Article uri icon

Overview

abstract

  • Background Scapholunate interosseous ligament (SLIL) injuries can often be difficult to detect using magnetic resonance imaging (MRI), especially with older 1.0 and 1.5 Tesla magnets. Wrist arthroscopy is the gold standard for diagnosis of SLIL injuries, but is an invasive procedure with associated risks. Purpose To assess whether SLIL injuries can be more accurately detected using axial MRI sequences instead of coronal sequences. Material and Methods An institutional review board approved retrospective analysis of arthroscopic wrist surgeries performed at our institution. Patients that had a preoperative MRI performed at our university center using a 1.5 Tesla scanner with a dedicated wrist coil were included in the study. Three fellowship-trained musculoskeletal radiologists reviewed the axial sequences and coronal sequences independently. The accuracy of the coronal and axial sequences was compared with the arthroscopic/surgical findings. Result Twenty-six patients met the inclusion criteria. The sensitivity for SLIL tears was 79% and 65% for the axial and coronal sequences, respectively. The specificity was 82% for the axial and 69% for the coronal sequences, respectively. The positive and negative predictive values for the axial sequences were 76% and 84% respectively, compared to 68% and 71% for the coronal sequences, a statistically significant difference. Conclusion SLIL tears are more readily detectable on axial MRI sequences than coronal. Clinically, patients with radial-sided wrist pain and suspicion for SLIL tears should have the axial sequences scrutinized carefully. An otherwise normal study with the axial sequence being degraded by motion or other MRI artifacts might need repeat imaging.

publication date

  • July 20, 2016

Research

keywords

  • Ligaments, Articular
  • Magnetic Resonance Imaging
  • Wrist Injuries

Identity

Scopus Document Identifier

  • 84995546075

Digital Object Identifier (DOI)

  • 10.1177/0284185115626468

PubMed ID

  • 26861205

Additional Document Info

volume

  • 57

issue

  • 12