Reliability of T2-weighted signal intensity-based quantitative measurements and visual grading of lumbar disc degeneration on MRI

Author:

Murto Niko1ORCID,Luoma Katariina1,Lund Teija2,Kerttula Liisa1

Affiliation:

1. Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

2. Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

Abstract

Background Decrease in signal intensity (SI) of the nucleus pulposus (NP) on T2-weighted (T2W) images, a sign of disc degeneration (DD), is usually graded based on an observer's image interpretation. No gold standard for quantitative assessment of NP SI exists to date. Purpose To compare different quantitative methods and visual gradings of lumbar DD and to evaluate the ability of the quantitative methods to differentiate DD grades. Material and Methods The mean SI of 95 lumbar discs was measured from sagittal T2W images using three regions of interest (ROI): the whole disc, ellipsoid ROI on the NP, and targeted ROI on the most homogenous, brightest area of the NP. SI values were adjusted with cerebrospinal fluid (CSF) SI and compared with vertebral bone SI-adjusted values. DD was evaluated with Pfirrmann grading and visual grading of NP SI. Intra- and inter-observer agreements and relationships between measurements and visual gradings were assessed. Results Repeatability of all measurements was excellent. All measurements had a strong correlation with Pfirrmann grading and visual NP SI grading, and the CSF SI-adjusted values had a stronger correlation than the vertebral bone SI-adjusted values. The SI values obtained with the targeted ROI had the most significant differences between visual DD grades. Conclusion Quantitative measurement of the NP SI provides a reliable method for evaluating lumbar DD. Targeted selection of the NP structures included in the measurement offers the best differentiation of DD grades. A reliable quantitative method for DD evaluation is needed for the development of machine-learning-based DD classification.

Funder

Helsingin ja Uudenmaan Sairaanhoitopiiri

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

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