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.
- Annular fissure
- Correlative image enhancement CIE
- Lumbar spine