低场核磁共振弛豫谱反演算法研究

从核磁共振成像分析仪上直观采集到的信号无法直接反映样品属性,必须采用反演算法获取弛豫时间及这种弛豫分量所对应回波的初始幅度之间的关系。因此,正确的反演算法结果直接影响核磁共振信号的解释和应用。本文基于不适定反问题正则化理论,优化了正则化因子的选取方法,通过Matlab7.0数值模拟,设计了一套低场核磁共振弛豫谱反演正则化算法。构造谱反演和样品实测回波串反演表明本算法能够满足核磁共振成像分析仪测试和科研的需要。     

高分辨率细胞牵引力频域反演技术

目的 针对经典细胞牵引力反演算法的病态特征,发展基于二维傅立叶空间的高分辨率细胞牵引力反演新技术。方法 根据基底表面位移测量数据和积分核函数在二维傅立叶空间各自的分布特点和规律,探讨牵引力反演过程中出现的反卷积病态现象,构建一套自适应加窗滤波算法来约束反演计算过程中出现的高频噪声放大效应。结果 经典细胞牵引力频域反演算法（Fourier transform traction cytometry, FTTC）具有明显的反卷积病态特征,特别是在位移采样网格较密情况下,牵引力反演结果极不可靠。利用本文提出的频域滤波方法则能显著削弱位移场高频噪声对于力反演结果的影响。结论 这种细胞牵引力频域反演新方法可有效抑制反问题病态现象的发生,显著提高力场测量的稳定性和空间分辨率。该方法有望在细胞与胞外基质相互作用研究中得到广泛应用。     

基于红外技术的人体异常热源的探测研究

采用红外测温技术及改进蚁群算法求解导热反问题中的内热源强度及表面换热系数。通过建立含有内热源的二维稳态导热反问题实验模型,用红外热像仪测量试件的表面温度,并选用若干点温度作为求解反问题的补充条件,来反演热源强度及表面换热系数,并讨论了测点布置方式对反演结果的影响。计算结果表明：采用红外热像仪测温,结合改进蚁群算法可准确地对热源强度和表面换热系数进行反演识别。在整个边界上均匀布置测点的个数越多,则反演时计算步数越少,反演结果越精确;在测点个数相同时,在整个边界上均匀布置测点比在部分边界上均匀布置测点,更容易反演计算出准确的结果。     

2DMRS技术在脑内GABA浓度的定量检测的研究进展

目的：探讨定量检测脑内GABA浓度的方法和研究现状。方法：本文从二维磁共振谱的角度，讨论了二维化学位移相关谱（2DCOSY）、双量子滤波谱（DQF2DMRS）以及J分解谱（J—resolved 2D MRS）研究方法。这三种处理方法所得到的谱图均可反映不同代谢物偶合常数与波峰位置的关系。无论那种类型的谱图，都可通过截取某一特殊位置处的一维谱线得到GABA的浓度。结合研究现状，讨论了上述三种方法的差异。结果和结论：相对于传统IDMRS技术而言，2DMRS技术具有明显的优势，包含更为丰富的信息，这对于准确检测脑内GABA的浓度，甚至临床应用，都具有深远的意义。     

二维超声及彩色多普勒血流显像诊断下肢深静脉瓣功能不全

目的：探讨下肢深静脉瓣功能不全的二维声像图及彩色多普勒血流的特点。方法：对14例17条静脉瓣功能不全的下肢静脉进行了二维超声及彩色多普勒血流显像的检查。结果：17条下肢静脉瓣功能不全，根据二维超声表现分为原发性13条静脉，继发性4条静脉；根据血流反流时间的不同，可分为I组（9条静脉），Ⅱ级（5条静脉），Ⅲ级（3条静脉）。结论：二维超声及彩色多普勒血流显像能观察静脉管腔，管壁的结构及实时状况，并能明确瓣膜的反流部位及反流程度。以此标准诊断下肢深静脉瓣功能不全，对外科合理的选择手术部位及方式提供有效的依据。     

基于SBF算法的HRV信号信息熵分析

目的：HRV信号是发生在非均匀间隔时间点上的RR序列,其传统的频域分析方法为功率谱分析,但对于非均匀采样的HRV信号,快速傅里叶变换（FFT）并不适用,同时HRV信号是具有混沌性和非平稳性的信号,功率谱也不善于表现HRV信号的非平稳性质。SBF（Similar Basis Function）算法是相对于FFT的另一种傅里叶积分估计方法,适用于均匀与非均匀采样信号,片段谱是基于SBF算法定义的表示信号能量分布的参数,相对于功率谱其主要的优点是能表现谱随时间的变化,对于处理非平稳信号也有一定的优势。因此本文探究用片段谱信息熵作为HRV信号的参数指标,分析HRV信号在不同频段能量分布复杂度随年龄的变化。方法：本文以20名年轻（21～34）岁与20名年老（68～81）岁二组健康人的HRV信号为实验数据,用SBF算法计算出二组人的片段谱,再算出多个不同频率段上的信息熵,同时用全频段分割法计算两组人的信息熵。结果：在0．003Hz～0．04Hz和0．04Hz～0．15Hz频段内,年轻组的片段谱熵明显大于老年组（p〈0．001,p〈0．01）;由全频段分割法也得到类似的结果。结论：因而基于SBF算法的片段谱熵是分析HRV信号的有效指标。     

结合二维灰度直方图与PCNN的医学图像多类分割

针对医学图像中对组织器官多类分割的要求,提出一种结合二维灰度直方图的脉冲耦合神经网络（pulse coupled neural networks,PCNN）图像多类分割算法.首先根据PCNN模型的局部连接作用和阈值衰减特性对图像进行多类分割,然后利用基于类内最小离散度的二维直方图算法计算出PCNN网络迭代时的最佳门限值,从而实现医学图像的多类分割.通过对仿真的正常颅脑和非正常的颅脑核磁共振图像进行测试,结果显示本PCNN图像多类分割算法能够有效地分割出核磁共振颅脑图像中不同脑组织.因此,本文算法具有应用于医学图像的多类分割的可行性,并提高计算机辅助分割医学图像的准确性.     

基于PCA和LDA方法的肿瘤基因表达谱数据分类

目的基因芯片技术对医学临床诊断、治疗、药物开发和筛选等技术的发展具有革命性的影响。针对高维医学数据降维困难及基因表达谱样本数据少、维度高、噪声大的特点,维数约减十分必要。基于主成分分析（principalcomponentanalysis,PCA）和线性判别分析（1ineardiscriminantanalysis,LDA）方法,有效解决了基因表达谱数据分类问题,并提高了识别率。方法分别引人PCA和LDA方法对基因表达谱数据进行降维,然后用K近邻（K—nearestneighbor,KNN）作为分类器对数据进行分类,并分别在乳腺癌和卵巢癌质谱数据上。结果在两类癌症质谱数据上应用PCA和LDA方法能够有效提取分类特征信息,并在保持较高分类正确率的前提下大幅度降低医学数据的维数。结论利用维数约减的方法对癌症基因表达谱数据进行分类,可辅助临床医生发现新的疾病特征,提高疾病诊断的正确率。     

扩张型心肌病与缺血性心肌病二维彩色多普勒的对比研究

利用二维彩色多普勒在心脏形态学、血液动力学及心室功能方面对扩张型心肌病 (DCM )及缺血性心肌病 (ICM )进行诊断及鉴别诊断。利用二维彩色多普勒技术分别测定 5 2例DCM组、ICM组及正常对照组的数据 ,利用定量和半定量的方法对数据进行测定 ,用统计学对数据进行处理。结果表明正常组分别与DCM组及ICM组比较有显著的差异 (P <0 0 5或0 0 1) ,ICM组与DCM组有比较显著的差异 (P <0 0 1)。DCM组主要是心肌弥漫性改变 ,心腔普遍扩大 ,心脏收缩功能明显减退 ,舒张功能为“正常”。多瓣膜返流发生率高且严重。ICM组以心肌局部病变为主 ,主要是左心腔扩大 ,左室收缩功能正常或轻度减低 ,舒张功能明显减低。瓣膜多为单瓣膜返流且较轻。结论 :二维彩色多普勒是临床鉴别诊断DCM和ICM最可靠、最简单及无创伤的首选方法。     

高频二维超声及彩色多普勒对乳腺肿瘤的诊断价值

对284例乳腺肿瘤进行了二维及彩色多普勒检查。总结各自的二维声像图及彩色多普勒血流特征,分析了二维超声及彩色多普勒在乳腺肿瘤疾病的诊断价值。结果显示：二维超声彩色多普勒是诊断及鉴别诊断乳腺肿瘤的首选方法。     

Fast multidimensional NMR spectroscopy for sparse spectra

Multidimensional NMR spectroscopy is widely used for studies of molecular and biomolecular structure. A major disadvantage of multidimensional NMR is the long acquisition time which, regardless of sensitivity considerations, may be needed to obtain the final multidimensional frequency domain coefficients. In this article, a method for under‐sampling multidimensional NMR acquisition of sparse spectra is presented. The approach is presented in the case of two‐dimensional NMR acquisitions. It relies on prior knowledge about the support in the two‐dimensional frequency domain to recover an over‐determined system from the under‐determined system induced in the linear acquisition model when under‐sampled acquisitions are performed. This over‐determined system can then be solved with linear least squares. The prior knowledge is obtained efficiently at a low cost from the one‐dimensional NMR acquisition, which is generally acquired as a first step in multidimensional NMR. If this one‐dimensional acquisition is intrinsically sparse, it is possible to reconstruct the corresponding two‐dimensional acquisition from far fewer observations than those imposed by the Nyquist criterion, and subsequently to reduce the acquisition time. Further improvements are obtained by optimizing the sampling procedure for the least‐squares reconstruction using the sequential backward selection algorithm. Theoretical and experimental results are given in the case of a traditional acquisition scheme, which demonstrate reliable and fast reconstructions with acceleration factors in the range 3–6. The proposed method outperforms the CS methods (OMP, L1) in terms of the reconstruction performance, implementation and computation time. The approach can be easily extended to higher dimensions and spectroscopic imaging. Copyright © 2014 John Wiley & Sons, Ltd.     

基于肺部CT序列图像的肺实质三维分割

目的:肺实质分割是基于CT图像的肺结节计算机辅助检测技术必不可少的步骤。结合阈值技术、连通区域标记以及形态学技术,提出了一种简单有效的从CT图像中分割三维肺实质的方法,以期能为后续肺结节计算机辅助检测技术的研究奠定基础。方法:首先,将原图像二值化,并应用三维连通域标记去除背景及细小空洞;然后,经三维区域生长法去除气管;最后,经形态学滤波平滑肺边界得到肺部精确的三维模板,并采用该模板从CT序列图像中分割出肺实质。结果:根据对20组层厚2.0mm、每组约250个切片的肺部CT临床数据实验验证,其肺实质分割的平均正确度为91.55%,处理单组数据平均耗时167.4563s。结论:实验结果表明,本文方法能自动快速地从CT序列图像中分割出肺实质。     

液体核磁共振中多核体系的量子模拟

目的:基于量子力学原理,对液体核磁共振中的多核体系进行自旋动力学模拟。方法:首先把液体核磁共振实验过程中出现的化学位移、J耦合相互作用写成狄拉克算符,然后转化成矩阵形式,将它们代入密度矩阵的演化方程,在采样阶段使用观测算符记录每个采样点的信号,最后使用傅里叶变换得到谱图。结果:通过量子模拟得到了多核体系演化的核磁共振谱图,与对应的理论预测及预期的实验结果一致。结论:在核磁共振中使用量子力学建模和模拟是完全可靠的,可用于辅助分析真实的核磁共振实验。     

VarianClinaciX15MeV光子束能谱重建

目的：精确重建VarianClinaciX15MeV光子束能谱。方法：利用先验模型和遗传算法,以光子束中轴百分深度剂量（PDD）为基础数据实现医用直线加速器光子能谱重建。1．EGS模拟仿真VarianClinacix治疗头和标准水模体,获得15MeV光子束的模拟能谱以及单能光子中轴PDD数据;2．根据测量得到的中轴PDD数据以及模拟得到的单能光子中轴PDD数据,运用遗传算法优化求解先验模型的参数：3．将优化后的先验模型所计算的结果作为初始化种群．再用遗传算法二次优化重建光子能谱。结果：重建能谱与蒙特卡洛模拟得到的能谱具有良好的一致性,相关系数为0．9970;重建能谱的平均能量与由相空间文件分析所得平均能量的相对误差为1．16％;根据重建能谱计算得到的中轴PDD数据与实际测量的中轴PDD数据之间的相关系数为O．9999。结论：利用先验模型和遗传算法进行光子束能谱重建可靠有效．具有实用价值。     

Chemical speciation by selective heteronuclear single-quantum coherence spectroscopy: determination of double-bond quantity in unsaturated fatty acid compounds

A method for identifying fatty acid species based on the number of double bonds contained in a lipid molecule is presented. Common to all polyunsaturated fatty acids are two signature resonances at approximately 5.3 and 2.8 ppm in the proton chemical-shift spectrum of NMR. These resonances are from the vinyl and bis-allyl protons, respectively, and, although they can be readily observed by NMR from lipid extracts of biological samples, direct speciation has never been demonstrated by NMR. By modifying a conventional HSQC pulse sequence with a J-pulse on the spin system of the vinyl group (generalized as an IS spin system) at the beginning of the initial polarization transfer period and selectively inverting the 13C (I) spins with a narrowband sech/tanh inversion pulse, the collection of data in both dimensions can be restricted to a narrow slice of the chemical-shift range. The resolution is subsequently determined by digitizer efficiency, and spectra can be collected optimally from within a very narrow 1 x 6 ppm window of the respective proton and carbon chemical-shift ranges. With this modification it is possible to distinguish at least one resonance each from the multiple shifts expected from the indirectly detected nuclei of the fatty acid species, oleic acid, linoleic acid, linolenic acid and arachidonic acid, which contain one, two, three and four double bonds, respectively. This and similar methods of applied selectivity are of potential interest in characterizing speciation in biological samples where mixtures are often encountered and chemical shifts of the same structural group of similar molecules give rise to complicated overlapping resonances but are important for diagnosis of disease processes such as cancer.     

Derivative-free generation and interpolation of convex Pareto optimal IMRT plans

In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning.     

Feasibility of a fast inverse dose optimization algorithm for IMRT via matrix inversion without negative beamlet intensities

A fast optimization algorithm is very important for inverse planning of intensity modulated radiation therapy (IMRT), and for adaptive radiotherapy of the future. Conventional numerical search algorithms such as the conjugate gradient search, with positive beam weight constraints, generally require numerous iterations and may produce suboptimal dose results due to trapping in local minima. A direct solution of the inverse problem using conventional quadratic objective functions without positive beam constraints is more efficient but will result in unrealistic negative beam weights. We present here a direct solution of the inverse problem that does not yield unphysical negative beam weights. The objective function for the optimization of a large number of beamlets is reformulated such that the optimization problem is reduced to a linear set of equations. The optimal set of intensities is found through a matrix inversion, and negative beamlet intensities are avoided without the need for externally imposed ad-hoc constraints. The method has been demonstrated with a test phantom and a few clinical radiotherapy cases, using primary dose calculations. We achieve highly conformal primary dose distributions with very rapid optimization times. Typical optimization times for a single anatomical slice (two dimensional) (head and neck) using a LAPACK matrix inversion routine in a single processor desktop computer, are: 0.03 s for 500 beamlets; 0.28 s for 1000 beamlets; 3.1 s for 2000 beamlets; and 12 s for 3000 beamlets. Clinical implementation will require the additional time of a one-time precomputation of scattered radiation for all beamlets, but will not impact the optimization speed. In conclusion, the new method provides a fast and robust technique to find a global minimum that yields excellent results for the inverse planning of IMRT.     

一种在PC上实现三维医学图像实时旋转的方法

实时旋转是三维医学图示中的一个重要环节，但巨大的显示数据量使旋转在普通微机上成为一个难以解决的问题，本文提出的旋转与显示分离技术，采用先旋转，而后再显示的方法，巧妙解决了该问题，使得大数据量的三维学图像在普通微机上实时旋转成为可能。     

基于RoughSet的特征集融合PET/CT肺部肿瘤CAD模型

针对肺部肿瘤PET/CT感兴趣区域(ROI)在高维特征表示下存在着特征相关和维数灾难问题,提出了一种基于粗糙集特征集融合的PET/CT肺部肿瘤CAD模型。首先提取肺部肿瘤ROI的8维形状特征、7维灰度特征、3维Tamura纹理特征、56维GLCM特征和24维频域特征,得到98维特征矢量;然后基于遗传算法的知识约简方法和基于属性重要度的启发式算法对提取的特征集合分别进行特征级融合得到特征子集G1、G2、G3,A1、A2、A3,降低特征矢量的维数;再次利用网格寻优算法优化核函数的SVM作为分类器分别进行融合前和融合后的分类识别比较,基于遗传算法的特征集融合和基于属性重要度的特征集融合的分类识别比较2组实验;最后以2 000幅肺部肿瘤的PET/CT图像为原始数据,采用基于粗糙集特征集融合的肺部肿瘤PET/CT计算机辅助诊断模型对肺部肿瘤进行辅助诊断。实验结果表明,经过粗糙集特征集融合的肺部肿瘤诊断识别方法能有效提高肺部肿瘤的诊断正确率,一定程度上降低了特征之间的相关性。     

Wiener noise power spectra of radiological television systems using a digital oscilloscope

Measurement of spatial noise power spectra from television based radiographic and fluoroscopic systems is essential to the understanding of their operation and optimization. However, conventional methods require acquisition and processing of large numbers of complete images, thus confining such measurements to special applications where accessible frame buffers already exist or elaborately equipped laboratories. We have developed a method which only requires storage of single TV lines or point scans. A digital oscilloscope captures these point scans and a laboratory microcomputer facilitates manipulation of the data to separate out different components of the noise power spectra. The x-ray dependent component of the noise power spectrum so produced is not the ordinary Wiener spectrum. However, it is shown that reconstruction of the full Wiener spectrum from this is possible subject only to the requirement that the x-ray noise spectrum at the output of the imaging system is circularly symmetric.