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. 2023 Apr 7;12(8):2762.
doi: 10.3390/jcm12082762.

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Rafal Obuchowicz  1 , Karolina Nurzynska  2 , Monika Pierzchala  3 , Adam Piorkowski  4 , Michal Strzelecki  5
Affiliations

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Rafal Obuchowicz et al. J Clin Med. .

Abstract

Currently, bone age is assessed by X-rays. It enables the evaluation of the child's development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age.

Background: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive.

Methods: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age.

Results: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE.

Conclusions: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

Keywords: bone MRI; bone age; image texture; pediatric radiology; regression analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of the age of the patient within the dataset.
Figure 2
Figure 2
Exemplary magnetic resonance imaging. The red rectangle shows the growth region in the image on the right and bone region on the left scan.
Figure 3
Figure 3
The result of regression algorithms for all cases examined for data describing bone images (metric). The real age is presented by blue circles and estimated age with a regression algorithm is depicted by orange dots. The samples are ordered on the y axis with increasing age. (a) DICOM T1-weighted, (b) DICOM T2-weighted, (c) PNG T1-weighted, and (d) PNG T2-weighted.
Figure 4
Figure 4
The result of regression algorithms for all examined cases for data describing growth region images (metric). The real age is given as a blue circle and age estimated with a regression algorithm is depicted as orange dot. The samples are ordered on the y axis with increasing age. (a) DICOM T1-weighted, (b) DICOM T2-weighted, (c) PNG T1-weighted, and (d) PNG T2-weighted.
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