개선된 뇌하수체 선종 진단을 위한 자기공명영상 노이즈 제거 기법
A Noise Reduction Technique for Enhancing Pituitary Adenoma Diagnostic on Magnetic Resonance Image
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초록
Magnetic resonance imaging is a technique specialized in soft tissue imaging with high contrast resolution without in vivo ionization and has been widely used in various clinical settings. In particular, the recent increase in social stress factors has been used in the diagnosis of pituitary adenoma, the incidence increases rapidly. Recently, due to the development of magnetic resonance imaging, it is possible to diagnose micro pituitary adenoma, but despite the use of contrast medium, there has been a difficulty in diagnosing the pituitary adenoma due to its small size and noise. In order to solve this problem, a proposed method of separating signal components image and noise components image from a measured image is applied, and the improvement of diagnostic efficiency is attempted by removing noise. As a result, it was confirmed that the image quality was improved as a whole by applying SNR for 30 subjects data. It is expected that this study will be useful as a pre-processing method for improving the image quality and developing diagnostic indicators of pituitary adenoma.
Abstract
Magnetic resonance imaging is a technique specialized in soft tissue imaging with high contrast resolution without in vivo ionization and has been widely used in various clinical settings. In particular, the recent increase in social stress factors has been used in the diagnosis of pituitary adenoma, the incidence increases rapidly. Recently, due to the development of magnetic resonance imaging, it is possible to diagnose micro pituitary adenoma, but despite the use of contrast medium, there has been a difficulty in diagnosing the pituitary adenoma due to its small size and noise. In order to solve this problem, a proposed method of separating signal components image and noise components image from a measured image is applied, and the improvement of diagnostic efficiency is attempted by removing noise. As a result, it was confirmed that the image quality was improved as a whole by applying SNR for 30 subjects data. It is expected that this study will be useful as a pre-processing method for improving the image quality and developing diagnostic indicators of pituitary adenoma.
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