波谱学杂志
波譜學雜誌
파보학잡지
Chinese Journal of Magnetic Resonance
2015年
4期
584-595
,共12页
肖洒%吕植成%孙献平%叶朝辉%周欣
肖灑%呂植成%孫獻平%葉朝輝%週訢
초쇄%려식성%손헌평%협조휘%주흔
磁共振成像(MRI)%压缩感知%随机欠采样矩阵%点扩散函数
磁共振成像(MRI)%壓縮感知%隨機欠採樣矩陣%點擴散函數
자공진성상(MRI)%압축감지%수궤흠채양구진%점확산함수
MRI%compressed sensing%random undersampling matrix%point spread function
在压缩感知-磁共振成像(CS-MRI)中,随机欠采样矩阵与重建图像质量密切相关。而选取随机欠采样矩阵一般是通过计算点扩散函数(PSF),以可能产生的伪影的最大值为评价参数,评估欠采样对图像重建的影响,然而最大值只反应了伪影的最坏情况。该文引入了两种新的统计学评价参数平均值(MV)和标准差(SD),其中平均值评估了伪影的平均大小,标准差可以反映伪影的波动情况。该文分别使用这3种参数对小鼠和人体脑部MRI数据以不同的采样比率进行CS图像重建,实验结果表明,当采样比率不低于4倍稀疏度时,使用平均值获得了质量更优的重建图像。因此,通过稀疏度先验知识指导合理选取采样比率,并以平均值为评价参数选取随机欠采样矩阵,能够获得更优的CS-MRI重建图像。
在壓縮感知-磁共振成像(CS-MRI)中,隨機欠採樣矩陣與重建圖像質量密切相關。而選取隨機欠採樣矩陣一般是通過計算點擴散函數(PSF),以可能產生的偽影的最大值為評價參數,評估欠採樣對圖像重建的影響,然而最大值隻反應瞭偽影的最壞情況。該文引入瞭兩種新的統計學評價參數平均值(MV)和標準差(SD),其中平均值評估瞭偽影的平均大小,標準差可以反映偽影的波動情況。該文分彆使用這3種參數對小鼠和人體腦部MRI數據以不同的採樣比率進行CS圖像重建,實驗結果錶明,噹採樣比率不低于4倍稀疏度時,使用平均值穫得瞭質量更優的重建圖像。因此,通過稀疏度先驗知識指導閤理選取採樣比率,併以平均值為評價參數選取隨機欠採樣矩陣,能夠穫得更優的CS-MRI重建圖像。
재압축감지-자공진성상(CS-MRI)중,수궤흠채양구진여중건도상질량밀절상관。이선취수궤흠채양구진일반시통과계산점확산함수(PSF),이가능산생적위영적최대치위평개삼수,평고흠채양대도상중건적영향,연이최대치지반응료위영적최배정황。해문인입료량충신적통계학평개삼수평균치(MV)화표준차(SD),기중평균치평고료위영적평균대소,표준차가이반영위영적파동정황。해문분별사용저3충삼수대소서화인체뇌부MRI수거이불동적채양비솔진행CS도상중건,실험결과표명,당채양비솔불저우4배희소도시,사용평균치획득료질량경우적중건도상。인차,통과희소도선험지식지도합리선취채양비솔,병이평균치위평개삼수선취수궤흠채양구진,능구획득경우적CS-MRI중건도상。
In compressed sensing magnetic resonance imaging (CS-MRI), the quality of reconstructed image is largely determined by the random undersampling matrix. It is a common practice to select the random undersampling matrix though computation of the point spread function (PSF) and the maximal artifacts possible. In this paper, we proposed to use two novel statistical parameters, mean value (MV) and standard deviation (SD), to guide the selection of random undersampling matrix. The two parameters evaluate the average amplitude and fluctuation of the possible artifacts, respectively. Experiments on mice brain and human brain were used to compare image quality of CS reconstructions of MRI data acquired with random undersampling matrices determined by different criteria. It was shown that reconstruction withMV had better performance when the sampling ratio is above four times of sparsity. It is concluded that better CS-MRI reconstruction quality can be achieved with reasonable selection of sampling ratio guided by prior knowledge of sparsity andMV as random undersampling matrix evaluation parameter.