An image reconstruction method based on machine learning for dual-energy subtraction radiography

Yoshiro Kitamura; Masahiko Yamada; Wataru Ito

doi:10.1117/12.768320

11 March 2008 An image reconstruction method based on machine learning for dual-energy subtraction radiography

Yoshiro Kitamura, Masahiko Yamada, Wataru Ito

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 691420 (2008) https://doi.org/10.1117/12.768320
Event: Medical Imaging, 2008, San Diego, California, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Bone

Image restoration

Image filtering

Radiography

Lung

Image processing

Lithium

Show All Keywords

Keywords/Phrases

Search In:

Publication Years