Dataset Information

Dixon T2 imaging of vertebral bone edema: reliability and comparison with short tau inversion recovery.

ABSTRACT:

Background

It is uncertain whether T2-weighted Dixon water images (DixonT2w) and short tau inversion recovery (STIR) are interchangeable when evaluating vertebral bone edema, or if one method is superior or visualizes the edema differently.

Purpose

To compare image quality and Modic change (MC)-related edema between DixonT2w and STIR and estimate inter-observer reliability for MC edema on DixonT2w.

Material and methods

Consecutive patients (n = 120) considered for the Antibiotics in Modic changes (AIM) trial underwent lumbar 1.5-T magnetic resonance imaging with two-point DixonT2w and STIR. Two radiologists assessed MC-related high-signal lesions on DixonT2w and compared image quality and lesion extent with STIR. Cohen's kappa and mean of differences ± limits of agreement were calculated.

Results

Fat suppression and artefacts were similar on DixonT2w and STIR in 116 of 120 (97%) patients. Lesion conspicuity was similar in 88, better on STIR in 10, and better on DixonT2w in 9 of 107 patients with MC-related high-signal lesions. Contrast-to-noise ratio for STIR versus DixonT2w was 19.1 versus 17.1 (mean of differences 2.0 ± 8.2). Of 228 lesions L4-S1, 215 (94%) had similar extent on DixonT2w and STIR, 11 were smaller/undetected on STIR, and two were smaller/undetected on DixonT2w. Lesions missed on STIR (n = 9) or DixonT2w (n = 1) had a weak signal increase on the other sequence (≤17%; 0% = vertebral body, 100% = cerebrospinal fluid). Inter-observer reliability (mean kappa L4-S1) was very good for presence (0.87), moderate for height (0.44), and good for volume (0.63) of lesions on DixonT2w.

Conclusion

DixonT2w provided similar visualization of MC-related vertebral edema as STIR.

SUBMITTER: Dagestad MH

PROVIDER: S-EPMC10964391 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dixon T2 imaging of vertebral bone edema: reliability and comparison with short tau inversion recovery.

Dagestad Magnhild H MH Toppe Monika K MK Kristoffersen Per M PM Gjefsen Elisabeth E Andersen Erling E Assmus Jörg J Vetti Nils N Espeland Ansgar A

Acta radiologica (Stockholm, Sweden : 1987) 20221222 3

<h4>Background</h4>It is uncertain whether T2-weighted Dixon water images (DixonT2w) and short tau inversion recovery (STIR) are interchangeable when evaluating vertebral bone edema, or if one method is superior or visualizes the edema differently.<h4>Purpose</h4>To compare image quality and Modic change (MC)-related edema between DixonT2w and STIR and estimate inter-observer reliability for MC edema on DixonT2w.<h4>Material and methods</h4>Consecutive patients (n = 120) considered for the Antib ...[more]

PMID: 36560906

Similar Datasets

Project description:Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Dataset Information

Dixon T2 imaging of vertebral bone edema: reliability and comparison with short tau inversion recovery.

Background

Purpose

Material and methods

Results

Conclusion

Publications

Dixon T2 imaging of vertebral bone edema: reliability and comparison with short tau inversion recovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets