Fellows’ Journal Club: Automatic Spinal Cord Gray Matter Quantification: A Novel Approach

Editor’s Choice

The authors assessed the reproducibility and accuracy of cervical spinal cord gray matter and white matter cross-sectional area measurements using magnetization inversion recovery acquisition images and a fully automatic postprocessing segmentation algorithm. The cervical spinal cord of 24 healthy subjects was scanned in a test-retest fashion on a 3T MR imaging system. Twelve axial averaged magnetization inversion recovery acquisition slices were acquired over a 48-mm cord segment. GM and WM were both manually segmented by 2 experienced readers and compared with an automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior. Reproducibility was high for both methods, while being better for the automatic approach. The accuracy of the automatic method compared with the manual reference standard was excellent. They conclude that the fully automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation.

BACKGROUND AND PURPOSE

Automatic Spinal Cord Gray Matter Quantification
Exemplary axial AMIRA slice of 1 representative volunteer at the C4 level. A–H, Eight images of different tissue contrast acquired by the AMIRA sequence, shown in chronologic order from lowest-to-highest TI. I, Average image from A to E in full view, which delivers a high contrast-to-noise-ratio for GM/WM. J, Average image from F to H, which delivers a high contrast-to-noise ratio for SC/CSF. K, Same average image as in I but histogram-equalized and zoomed.

Currently, accurate and reproducible spinal cord GM segmentation remains challenging and a noninvasive broadly accepted reference standard for spinal cord GM measurements is still a matter of ongoing discussion. Our aim was to assess the reproducibility and accuracy of cervical spinal cord GM and WM cross-sectional area measurements using averaged magnetization inversion recovery acquisitions images and a fully-automatic postprocessing segmentation algorithm.

MATERIALS AND METHODS

The cervical spinal cord of 24 healthy subjects (14 women; mean age, 40 ± 11 years) was scanned in a test-retest fashion on a 3T MR imaging system. Twelve axial averaged magnetization inversion recovery acquisitions slices were acquired over a 48-mm cord segment. GM and WM were both manually segmented by 2 experienced readers and compared with an automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior. Precision and accuracy of the automatic method were evaluated using coefficients of variation and Dice similarity coefficients.

RESULTS

The mean GM area was 17.20 ± 2.28 mm2 and the mean WM area was 72.71 ± 7.55 mm2 using the automatic method. Reproducibility was high for both methods, while being better for the automatic approach (all mean automatic coefficients of variation, ≤4.77%; all differences, P < .001). The accuracy of the automatic method compared with the manual reference standard was excellent (mean Dice similarity coefficients: 0.86 ± 0.04 for GM and 0.90 ± 0.03 for WM). The automatic approach demonstrated similar coefficients of variation between intra- and intersession reproducibility as well as among all acquired spinal cord slices.

CONCLUSION

Our novel approach including the averaged magnetization inversion recovery acquisitions sequence and a fully-automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation. This pipeline is promising for both the exploration of longitudinal structural GM changes and application in clinical settings in disorders affecting the spinal cord.

Read this article: http://bit.ly/320QL7h

Fellows’ Journal Club: Automatic Spinal Cord Gray Matter Quantification: A Novel Approach
Tags:
Jeffrey Ross
Fatal error: Uncaught Error: Call to undefined function get_cimyFieldValue() in /home2/ajnrblog/public_html/wp-content/themes/ample-child/author-bio.php:13 Stack trace: #0 /home2/ajnrblog/public_html/wp-content/themes/ample-child/content-single.php(35): include() #1 /home2/ajnrblog/public_html/wp-includes/template.php(812): require('/home2/ajnrblog...') #2 /home2/ajnrblog/public_html/wp-includes/template.php(745): load_template('/home2/ajnrblog...', false, Array) #3 /home2/ajnrblog/public_html/wp-includes/general-template.php(206): locate_template(Array, true, false, Array) #4 /home2/ajnrblog/public_html/wp-content/themes/ample/single.php(21): get_template_part('content', 'single') #5 /home2/ajnrblog/public_html/wp-includes/template-loader.php(106): include('/home2/ajnrblog...') #6 /home2/ajnrblog/public_html/wp-blog-header.php(19): require_once('/home2/ajnrblog...') #7 /home2/ajnrblog/public_html/index.php(17): require('/home2/ajnrblog...') #8 {main} thrown in /home2/ajnrblog/public_html/wp-content/themes/ample-child/author-bio.php on line 13