抄録
We propose a high accuracy algorithm for compressed sensing magnetic resonance imaging (CS-MRI) using a convex optimization technique. Lustig et al. proposed CS-MRI technique based on the minimization of a cost function defined by the sum of the data fidelity term, the 11-norm of sparsifying transform coefficients, and a total variation (TV). This function is not differentiable because of both l1-norm and TV. Hence, they used approximations of the non-differentiable terms and a nonlinear conjugate gradient algorithm was applied to minimize the approximated cost function. The obtained solution was also an approximated one, thus of low-quality. In this paper, we propose an algorithm that obtains the exact solution based on the simultaneous direction method of multipliers (SDMM), which is one of the convex optimization techniques. A simple application of SDMM to CS-MRI cannot be implemented because the transformation matrix size is proportional to the square of the image size. We solve this problem using eigenvalue decompositions. Simulations using real MR images show that the proposed algorithm outperforms the conventional one regardless of compression ratio and random sensing patterns.
本文言語 | English |
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ホスト出版物のタイトル | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ISBN(電子版) | 9789881476821 |
DOI | |
出版ステータス | Published - 2017 1月 17 |
外部発表 | はい |
イベント | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of 継続期間: 2016 12月 13 → 2016 12月 16 |
Other
Other | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 |
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国/地域 | Korea, Republic of |
City | Jeju |
Period | 16/12/13 → 16/12/16 |
ASJC Scopus subject areas
- 人工知能
- コンピュータ サイエンスの応用
- 情報システム
- 信号処理