本文關(guān)鍵詞：基于低秩約束的磁共振圖像重構(gòu)方法研究　出處：《浙江理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 低秩約束 多尺度 信差比 結(jié)構(gòu)相似性

【摘要】：動(dòng)態(tài)磁共振成像數(shù)據(jù)是一組變化的圖像序列,由于運(yùn)動(dòng)會(huì)導(dǎo)致磁共振圖像出現(xiàn)偽影,所以動(dòng)態(tài)磁共振成像通常要求減少采樣K空間的數(shù)據(jù),以提高磁共振掃描速度,便于尋求更高的磁共振圖像重構(gòu)質(zhì)量�；诘椭燃s束的磁共振(MR)圖像重構(gòu),需處理矩陣的秩越低,重構(gòu)圖像的精度會(huì)越高。因此,如何更好的運(yùn)用磁共振成像的低秩性,對(duì)提高重構(gòu)圖像的質(zhì)量具有重要的研究意義。論文的主要工作與成果如下:(1)低秩+稀疏(L+S,Low-Rank plus Sparse)矩陣分解模型是一種基于低秩組件和稀疏組件的模型,該方法可以提高動(dòng)態(tài)MRI數(shù)據(jù)的壓縮性,用于處理欠采樣磁共振成像的背景與動(dòng)態(tài)組件的分離,是比較成功的圖像重建模型之一。另外,基于分塊低秩(patch based low rank)方法是一種基于搜索相似塊進(jìn)而形成低秩組件的模型,分析了低秩與塊之間的關(guān)系以及如何處理低秩最小化問(wèn)題。通過(guò)與L+S方法,direct IFFT方法進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果表明基于分塊低秩的方法能夠更加準(zhǔn)確地重建心臟電影圖像重要的組織結(jié)構(gòu)和腹部數(shù)據(jù)的局部細(xì)節(jié)結(jié)構(gòu),改善了圖像質(zhì)量并使得噪聲和混疊偽影更少。(2)基于多尺度低秩模型(MSL,Multi-Scale Low Rank)的磁共振成像方法將矩陣分解成多尺度的塊低秩矩陣之和,并將多尺度塊低秩矩陣之和的最小化作為約束條件用于磁共振成像。采用交替方向乘子方法(ADMM,Alternating Direction Method of Multiplier)實(shí)現(xiàn)基于多尺度低秩模型的磁共振圖像重構(gòu)凸優(yōu)化問(wèn)題的求解。利用不同的采樣方式及加速因子,對(duì)不同類型的磁共振圖像數(shù)據(jù)進(jìn)行重構(gòu)。實(shí)驗(yàn)結(jié)果表明,相比于k-t SLR(k-t Sparsity Low-Rank)和L+S(Low-Rank plus Sparse)方法,我們所提出的MSL方法具有更好的重建效果(圖像結(jié)果紋理清晰、邊緣光滑),減少了重構(gòu)誤差,獲得更高的重構(gòu)信差比(SER,Signal to Error Ratio),具有更好的結(jié)構(gòu)相似性。
[Abstract]:Dynamic magnetic resonance imaging data is a sequence of image changes, because the movement will lead to artifacts in magnetic resonance images, so the dynamic magnetic resonance imaging is usually required to reduce the sampling K spatial data, in order to improve the scanning speed of magnetic resonance, magnetic resonance to seek higher image reconstruction quality. Based on low rank constraint (MR) image reconstruction, the lower the rank of the matrix, the higher the accuracy of the reconstructed image. Therefore, how to better use the low rank of MRI is of great significance to improve the quality of the reconstructed image. The main work and achievements of the thesis are as follows: (1) low rank + (L+S, Low-Rank plus Sparse sparse matrix decomposition) model is a kind of low rank component and a sparse component based model, this method can improve the compression of dynamic MRI data processing, for separation of background and dynamic component sampling under magnetic resonance imaging. Is one of the more successful model of image reconstruction. In addition, the block based low rank (patch based low rank) method is a model based on searching similar blocks to form low rank components. It analyzes the relationship between low rank and block and how to deal with low rank minimization. By comparing with the L+S method, direct IFFT method, the experimental results show that the method of block based on low rank can more accurately reconstruct local detail structure of heart structure and film image of abdominal data, improve the quality of image and makes the noise and aliasing artifacts less. (2) based on the multi-scale low rank model (MSL, Multi-Scale Low Rank), the magnetic resonance imaging method decomposes the matrix into the sum of multi-scale block low rank matrix, and the minimum of the sum of multi-scale block low rank matrices is used as a constraint condition for MRI. The alternating direction multiplier method (ADMM, Alternating Direction Method of Multiplier) is applied to solve the convex optimization problem of MRI reconstruction based on multi-scale and low rank model. Different types of magnetic resonance image data are reconstructed by different sampling methods and acceleration factors. The experimental results show that, compared with K-T SLR (K-T Sparsity Low-Rank) and L+S (Low-Rank plus Sparse) MSL method, our method has better reconstruction effect (the image texture clear, smooth edges), reduce the reconstruction error, get higher reconstruction contrast (SER, Signal to channel Error Ratio). The structural similarity is better.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R445.2;TP391.41

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本文編號(hào)：1338052