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Evaluating the effect of a post-processing algorithm in detection of annular fissure on MR imaging

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Abstract

Background and purpose

Visualization of annular fissures on MRI is becoming increasingly important but remains challenging. Our purpose was to test whether an image processing algorithm could improve detection of annular fissures.

Materials and methods

In this retrospective study, two neuroradiologists identified 56 IVDs with annular fissures and 97 IVDs with normal annulus fibrosus in lumbar spine MRIs of 101 patients (58 M, 43 F; age ± SD 15.1 ± 3.0 years). Signal intensities of diseased and normal annulus fibrosus, and contrast-to-noise ratio between them on sagittal T2-weighted images were calculated before and after processing with a proprietary software. Effect of processing on detection of annular fissures by two masked neuroradiologists was also studied for IVDs with Pfirrmann grades of ≤ 2 and > 2.

Results

Mean (SD) signal baseline intensities of diseased and normal annulus fibrosus were 57.6 (23.3) and 24.4 (7.8), respectively (p < 0.001). Processing increased (p < 0.001) the mean (SD) intensity of diseased annulus to 110.6 (47.9), without affecting the signal intensity of normal annulus (p = 0.14). Mean (SD) CNR between the diseased and normal annulus increased (p < 0.001) from 11.8 (14.1) to 29.6 (29.1). Both masked readers detected more annular fissures after processing in IVDs with Pfirrmann grade of ≤ 2 and > 2, with an apparent increased sensitivity and decreased specificity using predefined image-based human categorization as a reference standard.

Conclusions

Image processing improved CNR of annular fissures and detection rate of annular fissures. However, further studies with a more stringent reference standard are needed to assess its effect on sensitivity and specificity.

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Abbreviations

IVDs:

Intervertebral discs

CIE:

Correlative Image Enhancement

STIR:

Short Tau Inversion Recovery

TSE:

Turbo Spine Echo

ROI:

Region of Interest

CNR:

Contrast-to-noise ratio

SD:

Standard Deviation

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Authors and Affiliations

  1. Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 South Kingshighway Boulevard, St. Louis, MO, 63110, USA

    Rami W. Eldaya, Matthew S. Parsons, Hilary L. P. Orlowski, Martin N. Reis & Aseem Sharma

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  1. Rami W. Eldaya
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  2. Matthew S. Parsons
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  3. Hilary L. P. Orlowski
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  4. Martin N. Reis
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  5. Aseem Sharma
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Correspondence to Rami W. Eldaya.

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Conflict of interest

Aseem Sharma holds the intellectual property rights to the image processing technology used in this study and have co-founded a company (Correlative Enhancement LLC) with the aim of it future commercialization. All other authors have no conflict of interest to declare.

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Eldaya, R.W., Parsons, M.S., Orlowski, H.L.P. et al. Evaluating the effect of a post-processing algorithm in detection of annular fissure on MR imaging. Eur Spine J 30, 2150–2156 (2021). https://doi.org/10.1007/s00586-021-06793-5

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  • DOI: https://doi.org/10.1007/s00586-021-06793-5

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