本文選題：圖像重建算法 + 運(yùn)動(dòng)偽影��； 參考：《中南民族大學(xué)》2012年碩士論文

【摘要】：磁共振成像技術(shù)是生物學(xué)和醫(yī)學(xué)領(lǐng)域研究的重要工具之一。目前磁共振成像已經(jīng)廣泛應(yīng)用于臨床，對(duì)人體疾病的診斷發(fā)揮著巨大作用。磁共振成像具有高分辨率、可任意方向斷層成像以及無(wú)輻射等優(yōu)點(diǎn)。隨著成像技術(shù)的不斷發(fā)展對(duì)成像質(zhì)量和圖像重建速度的要求越來(lái)越高。由于硬件水平、人體承受極限及梯度的切換速度使成像時(shí)間受到限制，致使病人常常會(huì)發(fā)生自主或非自主的運(yùn)動(dòng)導(dǎo)致成像中出現(xiàn)偽影，影響醫(yī)生的診斷。如何消除運(yùn)動(dòng)偽影成為當(dāng)今磁共振成像研究的技術(shù)熱點(diǎn)。 PROPELLER成像技術(shù)可以減輕病人的運(yùn)動(dòng)偽影，縮短掃描時(shí)間，，該技術(shù)已經(jīng)成功的應(yīng)用在頭顱磁共振成像中，對(duì)剛性運(yùn)動(dòng)偽影的消除效果顯著。 本文首先對(duì)磁共振成像技術(shù)的成像理論和圖像重建方法進(jìn)行了研究。深入分析了周期性旋轉(zhuǎn)重疊平行線采集和增強(qiáng)后處理重建算法(Propeller)的原理，并對(duì)運(yùn)動(dòng)估計(jì)、非笛卡爾數(shù)據(jù)的重建算法等進(jìn)行了深入的分析。 其次對(duì)低場(chǎng)強(qiáng)下的Propeller圖像重建算法進(jìn)行實(shí)現(xiàn)，并對(duì)實(shí)現(xiàn)中的幾個(gè)關(guān)鍵問(wèn)題進(jìn)行了深入的剖析。針對(duì)MRI采集的數(shù)據(jù)是非笛卡爾數(shù)據(jù)的特殊性，對(duì)算法中用到的幾個(gè)關(guān)鍵運(yùn)算的實(shí)現(xiàn)進(jìn)行了研究，主要包括：原始數(shù)據(jù)文件的讀取、顯示K空間采樣軌跡、運(yùn)動(dòng)估計(jì)、非笛卡爾數(shù)據(jù)的重建、密度補(bǔ)償函數(shù)的選擇等。圖像重建采用的是Jackson網(wǎng)格化算法，隨著采樣數(shù)據(jù)的增多，對(duì)應(yīng)的網(wǎng)格化的時(shí)間也就越長(zhǎng)，密度補(bǔ)償函數(shù)的選取也是難點(diǎn)。 最后采用經(jīng)典的Shepp-logan模型對(duì)propeller成像算法進(jìn)行測(cè)試，模擬Propeller成像算法的K空間數(shù)據(jù)，運(yùn)用propeller算法成功得到了重建圖像。 我們利用MATLAB來(lái)實(shí)現(xiàn)Propeller圖像重建算法，該軟件實(shí)現(xiàn)*.MRD文件的讀取、數(shù)據(jù)處理、運(yùn)動(dòng)估計(jì)、密度補(bǔ)償函數(shù)、網(wǎng)格化重建等進(jìn)行研究分析，為深入研究消除運(yùn)動(dòng)偽影的方法及非笛卡爾數(shù)據(jù)的重建打下了基礎(chǔ)。
[Abstract]:Magnetic resonance imaging (MRI) is one of the most important tools in biology and medicine. At present, magnetic resonance imaging has been widely used in clinical, and plays a great role in the diagnosis of human diseases. Magnetic resonance imaging has the advantages of high resolution, arbitrary direction tomography and no radiation. With the development of imaging technology, the quality of imaging and the speed of image reconstruction are becoming more and more important. Due to the level of hardware, the limit of human tolerance and the speed of gradient switching, the imaging time is limited, and the patient will often have spontaneous or involuntary motion, which will lead to artifacts in imaging, which will affect the diagnosis of doctors. How to eliminate motion artifacts has become the focus of magnetic resonance imaging. PROPELLER imaging technique can reduce the motion artifacts and shorten the scanning time. It has been successfully used in cranial magnetic resonance imaging and has a remarkable effect on the elimination of rigid motion artifacts. Firstly, the imaging theory and image reconstruction method of magnetic resonance imaging technology are studied in this paper. The principle of periodic rotation overlapping parallel line acquisition and enhancement post-processing reconstruction algorithm Propeller is deeply analyzed and the motion estimation and non-Cartesian data reconstruction algorithm are analyzed. Secondly, the Propeller image reconstruction algorithm under low field intensity is implemented, and several key problems in the implementation are deeply analyzed. In view of the particularity of non-Cartesian data collected by MRI, several key operations used in the algorithm are studied, including: reading of original data file, displaying K-space sampling trajectory, motion estimation, and so on. Reconstruction of non-Cartesian data, selection of density compensation function, etc. The Jackson mesh algorithm is used in image reconstruction. With the increase of sampling data, the corresponding time of gridding is longer, and the selection of density compensation function is also difficult. Finally, the classical Shepp-logan model is used to test the propeller imaging algorithm, and the K-space data of the Propeller imaging algorithm is simulated, and the reconstructed image is successfully obtained by using the propeller algorithm. We use MATLAB to realize the algorithm of Propeller image reconstruction. The software realizes the reading of Propeller files, data processing, motion estimation, density compensation function, mesh reconstruction and so on. It lays a foundation for further research on the method of eliminating motion artifacts and the reconstruction of non-Cartesian data.
【學(xué)位授予單位】：中南民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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本文編號(hào)：1811676