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Osteoporosis Evaluation through Full Developed Speckle Imaging

Amaral MM1Del-Valle M2,3Raele MP2de Pretto LR2Ana PA3.

J Biophotonics. 2020 Apr 10:e202000025. doi: 10.1002/jbio.202000025. [Epub ahead of print]



Osteoporosis is a disease characterized by Bone Mineral Density (BMD) reduction, weakening the bone structure. Its diagnosis is performed using ionizing radiation, increasing health risk. Optical techniques are safer, due to non-ionizing radiation use, but limited to the analyses of bone tissue. This limitation may be circumvented in the oral cavity. In this work we explored the use of Laser Speckle Imaging (LSI) to differentiate the sound and osteoporotic maxilla and mandible bones in an in vitro model. Osteoporosis lesions were simulated with acid attack. The samples were evaluated by optical profilometry and LSI, using a custom software. Two image parameters were evaluated, speckle contrast ration and patches ratio. With the speckle contrast ratio, it was possible to differentiate sound from osteoporotic tissue. From speckle patches ratio it was observed a negative correlation with the roughness parameter. LSI is a promissory technique for assessment of osteoporosis lesions on alveolar bone. 


In this work we proposed the use of laser speckle imaging (LSI) for the

characterization of osteoporosis, based on an in vitro model. A custom system was

implemented to obtain LSI images of the osteoporosis induced mandible and maxilla samples.

A custom software was implemented to obtain the speckle patches ration and the speckle contrast ratio. These values were compared to the roughness parameters measured with optical profilometry using a validation sample set. We showed a positive correlation between

the roughness and speckle parameter in our trial. The speckle contrast ratio was able to

differentiate sound from osteoporotic tissue, while the speckle patches ratio presented a

negative correlation with the roughness parameter. This result is limited to the in vitro model used but we believe that this technique could be applied for clinical studies to validate it for future human application. To validate it for in vivo application, one should evaluate factors such as polarization, camera exposure time and optical setup, among others. It is worth mentioning that the depth distribution in a surface is a statistical process and therefore other statistical methods can be used for a complete description of the surface, not invalidating the findings of this study [23]. Other limitation is related to the computational techniques applied.

We have not conducted an extensive study on different methods of image analysis. Studies in this direction could significantly improve the performance of the technique for optical diagnosis of osteoporosis.