本文選題：動態(tài)MRI + 壓縮感知重建��； 參考：《浙江大學》2016年碩士論文

【摘要】：磁共振成像(MRI)因其具有無輻射,多平面成像,掃描參數(shù)多,提供病理信息豐富,軟組織對比分辨率高等優(yōu)點,在臨床醫(yī)學和科研中得到了廣泛的應用。而動態(tài)MR2可用于捕捉成像目標的動態(tài)變化過程,如心臟的收縮舒張、腹部對比劑灌注成像等,所以普遍應用于人體運動器官和功能成像等領(lǐng)域。但是受限于掃描時間,現(xiàn)有的動態(tài)MRI技術(shù)很難獲得同時滿足高時空分辨率和高信噪比的重建圖像序列。而且掃描期間受檢者的呼吸會引起內(nèi)臟器官的非剛性運動,破壞K空間掃描數(shù)據(jù)的一致性,引入運動偽影。多次屏氣是動態(tài)MRI掃描中提高圖像質(zhì)量的一種常用呼吸控制協(xié)議,但是在長時間的屏氣之后,受檢者通常會快速地大幅度換氣,導致圖像質(zhì)量嚴重下降。本文針對大呼吸運動,研究了基于高度降采樣數(shù)據(jù)的動態(tài)MRI重建技術(shù),主要成果包括：(1)針對降采樣動態(tài)MR成像問題,提出了一種基于時空塊的雙稀疏字典學習算法,通過結(jié)合雙稀疏模型和時空域的高相關(guān)性來訓練3D字典。相比于傳統(tǒng)的單層字典方案和k-tFOCUSS算法,在重建圖像的時空域局部細節(jié)特征和去除混疊偽影上獲得了更好的效果。(2)針對人體腹部大幅度呼吸運動問題,提出了一種基于馬爾可夫隨機場的離散優(yōu)化配準算法,解決了傳統(tǒng)的基于B樣條的自由形變配準算法存在的問題。實驗表明此算法不但能夠很好地建模slice方向大幅度和不連續(xù)運動場,而且性能指標要優(yōu)于同類deedsMST和Elastix配準算法。(3)結(jié)合上述字典學習和呼吸運動配準算法,實現(xiàn)了大呼吸運動下降采樣腹部灌注動態(tài)MR重建系統(tǒng)。利用多線圈螺旋采樣方式采集人體腹部數(shù)據(jù)進行測試,對實驗結(jié)果進行多方面分析,并與TRACER和PROUD算法進行比較,本文算法能夠在高時空分辨率下有效改善MR體三維圖像的重建質(zhì)量。本文實現(xiàn)的腹部灌注動態(tài)MR重建系統(tǒng),能夠在呼吸運動和對比劑雙重動態(tài)因素影響下,從約50倍降采樣的K空間數(shù)據(jù)中重建出高幀率高信噪比的MR體三維圖像。本系統(tǒng)在數(shù)據(jù)采集中無需呼吸門控等其他技術(shù)的配合,采樣方式較簡便。后期重建過程中對關(guān)鍵算法模塊進行多方面改進,經(jīng)過充分的實驗驗證,并與現(xiàn)有的先進算法進行比較,重建效果基本滿足應用需求。
[Abstract]:Magnetic resonance imaging (MRI) has been widely used in clinical medicine and scientific research because of its advantages of non-radiation, multi-plane imaging, multiple scanning parameters, abundant pathological information and high contrast resolution of soft tissue. Dynamic MR2 can be used to capture the dynamic changes of imaging targets, such as systolic and diastolic heart, abdominal contrast media perfusion imaging, so it is widely used in the field of human motor organs and functional imaging. However, limited by the scanning time, the existing dynamic MRI technology is difficult to obtain the reconstruction image sequence which can satisfy both high spatio-temporal resolution and high signal-to-noise ratio (SNR). Moreover, the respiration of the subject during scanning will cause non-rigid movement of visceral organs, destroy the consistency of K space scanning data, and introduce motion artifacts. Multiple breath-holding is a common respiratory control protocol for improving image quality in dynamic MRI scanning. However, after a long period of breath-holding, the subjects usually breathe quickly and greatly, which results in a serious deterioration of image quality. In this paper, a dynamic MRI reconstruction technique based on height downsampling data is studied for large respiratory motion. The main achievements include: 1) to solve the problem of dynamic Mr imaging, a novel algorithm based on spatio-temporal block is proposed for learning double sparse dictionaries. A 3D dictionary is trained by combining the double sparse model with the high correlation in time and space domain. Compared with the traditional monolayer dictionary scheme and k-tFOCUSS algorithm, a better result is obtained on local detail features and aliasing artifacts of reconstructed images in space-time domain. A discrete optimal registration algorithm based on Markov random field is proposed, which solves the problem of traditional free deformation registration algorithm based on B-spline. Experiments show that the proposed algorithm not only can model large and discontinuous motion fields in slice direction, but also has better performance than deedsMST and Elastix registration algorithms. A large respiratory motion descending sampling abdominal perfusion dynamic Mr reconstruction system is realized. The multi-coil spiral sampling method is used to collect human abdominal data for testing, and the experimental results are analyzed in many aspects, and compared with TRACER and PROUD algorithms. This algorithm can effectively improve the reconstruction quality of Mr volume 3D images at high spatial and temporal resolution. The abdominal perfusion dynamic Mr reconstruction system can reconstruct 3D Mr volume images with high frame rate and high signal-to-noise ratio from K-space data of about 50 times lower sampling under the influence of both respiratory movement and contrast agent. This system does not need the cooperation of other technologies such as respiratory gating in data acquisition, and the sampling method is relatively simple. In the later stage of reconstruction, the key algorithm modules are improved in many aspects. After full experimental verification, and compared with the existing advanced algorithms, the reconstruction results basically meet the needs of application.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R445.2

【參考文獻】

相關(guān)期刊論文 前1條

1 王倩;楊春蘭;吳水才;;醫(yī)學圖像非剛體配準技術(shù)研究進展[J];北京生物醫(yī)學工程;2014年03期

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本文編號：1851417