Translator Disclaimer
11 March 2008 An image reconstruction method based on machine learning for dual-energy subtraction radiography
Author Affiliations +
Abstract
We propose a novel image reconstruction method for dual-energy subtraction radiography. When one of the dual-energy images is obtained at a low dose, a bone image generated with a dual-energy subtraction technique is degraded due to noise, especially high frequency noise. Our method restores the degraded bone image using a regression filter trained by support vector regression. The regression filter is trained based on the input of degraded bone images against an output of corresponding noiseless bone images. Due to strong correlation between the high frequency and low frequency signals of bone, the high frequency signal can be accurately generated based on the observed low frequency signal. However, learning such correlation directly is generally difficult. Therefore our technique first generates a "2-class bone model" that explicitly expresses a bone structure that should be restored. Then while utilizing this model, regression filtering is applied. The accuracy of regression learning is largely improved with this approach. Verification tests show that our method works well: a soft-tissue image obtained by subtracting a restored bone image from a standard radiograph reveals that the rib structure has been thoroughly removed and that the sharpness of the soft-tissue signal is improved in general and among the fine vessels. In conclusion, the proposed method can provide superior dose reduction as well as a better reflection of the anatomical structures in an image. With these advantages, the proposed method can offer high clinical value for the detection of lung lesions.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yoshiro Kitamura, Masahiko Yamada, and Wataru Ito "An image reconstruction method based on machine learning for dual-energy subtraction radiography", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691420 (11 March 2008); https://doi.org/10.1117/12.768320
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT


Back to Top