CT/MRI影像融合对肺癌脑转移放射治疗靶区勾画的影响

背景与目的脑转移瘤靶区勾画的准确性一直是放射治疗的关键,CT/MRI融合技术提供了可行的方法 ,本研究旨在探讨CT/MRI图像融合技术在肺癌脑转移靶区勾画中的作用。方法将31例肺癌脑转移患者的增强CT和MRI图像传送至图像处理工作站,分别在CT和CT/MRI融合图像上勾画GTV,比较勾画后的GTV体积,分析最大平均误差及瘤周水肿对靶区勾画的影响。结果 CT/MRI融合图像上勾画的GTV明显小于CT图像上勾画的GTV;瘤周水肿对靶区勾画存在明显影响。结论 CT/MRI图像融合技术可以提高肺癌脑转移靶区勾画的准确性。     

新时期药品行业“产、学、研”合作发展的新思路

目的为新时期药品行业＂产、学、研＂合作发展提供参考。方法分析新的《药品注册管理办法》实施近两年来对制药企业新药研发产生的影响。结果与结论提出药品行业在新形势下＂产、学、研＂结合的发展机遇及相应对策。     

应用Delphi＋Matlab／VC开发足疗软件

本文介绍了一种足疗软件的开发,重点介绍了用于反射区定位的图像配准算法。软件开发过程中应用了混合编程技术,使得软件开发速度加快,难度降低。该软件解决了当前足疗推广中存在的反射区定位不准确、专业按摩治疗费用高的问题。     

High-Dimensional Image Registration Using Symmetric Priors

This paper is about warping a brain image from one subject (the object image) so that it matches another (the template image). A high-dimensional model is used, whereby a finite element approach is employed to estimate translations at the location of each voxel in the template image. Bayesian statistics are used to obtain amaximum a posteriori(MAP) estimate of the deformation field. The validity of any registration method is largely based upon the constraints or, in this instance, priors incorporated into the model describing the transformations. In this approach we assume that the priors should have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i.e., warping brain A to brain B should not be different probabilistically from warping B to A). The fundamental assumption is that the probability of stretching a voxel by a factor ofnis considered to be the same as the probability of shrinkingnvoxels by a factor ofn⁻¹. In the Bayesian framework adopted here, the priors are assumed to have a Gibbs form, where the Gibbs potential is a penalty function that embodies this symmetry. The penalty function of choice is based upon the singular values of the Jacobian having a lognormal distribution. This enforces a continuous one-to-one mapping. A gradient descent algorithm is presented that incorporates the above priors in order to obtain a MAP estimate of the deformations. We demonstrate this approach for the two-dimensional case, but the principles can be extended to three dimensions. A number of examples are given to demonstrate how the method works.     

Redistribution of the neurosensory retina in inferior limited macular translocation: an evaluation using image registration

Background The present work aims to demonstrate the role of redistribution of the neurosensory retina (RNR)—that is, retinal tissue stretching and compression—in inferior limited macular translocation (LMT), to perform displacement mapping all over the area centralis, and to try and improve displacement evaluation precision. Methods Using piecewise linear image registration (through Matlab) on indocyanine green angiographic frames, we have evaluated the deformation (on both the retinal and choroido-scleral layer) of a pre-operative grid superimposed on the area centralis, plotted displacement vectors all over the area centralis, and calculated a scale ratio between pre- and post-operative frames. The scale ratio tests whether or not the same physical distance is represented by the same distance on these frames. Results The choroido-scleral layer is not deformed following surgery. The retinal layer shows areas of stretching and compression, demonstrating the RNR. Displacement vectors feature a rotating pattern around the optic disk (except if a macular fold exists) and a positive naso-temporal gradient. The median scale ratio is 3.8% (2.5% to 5.8%). Conclusions LMT involves RNR accounting for inferior retinal displacement, despite the supposedly limited internal scleral shortening. Our study confirms that the optic disk temporal edge is the displacement axis (except if a macular fold exists). Our method, unaffected by a non-zero scale ratio, provides more precision than previously published.     

CT与MR图像融合技术在颅脑肿瘤诊断的应用

目的探讨CT与MR图像融合技术在颅脑肿瘤诊断中的临床应用价值.资料与方法 26例颅脑肿瘤患者均在1～2周内分别进行CT和MRI扫描.全部CT和MR图像传送至Siemens 3D Virtuoso图像后处理工作站,并应用其中的Registration软件包进行融合.由2位有经验的放射科医师对CT、MR和融合图像进行评价分析.结果经Kappa检验,2位医师的CT、MR和融合图像诊断一致性均较好(Kappa值＞0.75).经χ2检验,融合图像对颅脑肿瘤的显示均明显优于单独CT或MR图像(P＜0.01).结论图像融合技术可以为临床医师提供诊断信息量更丰富、可视化程度更高的图像,从而有利于患者的进一步诊治.     

颅脑手术可视化中手术场空间与图像空间之间的四点配准算法

手术场空间与图像空间之间的配准是影响颅脑手术可视化系统精度的一个主要因素。给定两个三维空间之间的对应两组点（ｐｉ）和（ｐ＾１ｉ），ｉ＝１，２，…，Ｎ。两个空间中点之间的关系可由，ｐ′ｉ＝Ｒｐｉ＋Ｔ表示，其中Ｒ是旋转矩阵，Ｔ是平移矢量。本文提供了一种基于矩阵单值分解（ＳＶＤ）求解Ｒ和Ｔ的最小二乘解的四点算法，并对算法进行了推导，该算法与迭代相相比，具有速度快，用点少的优点，尤其适用于实际应用的颅脑手     

Registration of resected lung cancer in Niigata Prefecture

Objectives: Our registration of surgically treated lung cancer patients in Niigata Prefecture began in 2001. The purpose of this study was to identify the characteristics of patients and surgical treatment of lung cancer. Methods: All patients who underwent resection for lung cancer in Niigata Prefecture from January 2001 to December 2002 were eligible for registration. A total of 31 medical data for each patient were registered. Results: During the 2-year period, 1,211 patients were registered. A total of 605 cases (50%) were detected by mass screening, and 874 cases (72%) were diagnosed preoperatively. There were 718 (59%) c-stage IA cases and 317 (26%) c-stage IB cases. The most common operative procedure was lobectomy; 850 patients underwent single lobectomy. Limited resection was performed in 301 patients (25%), and video-assisted thoracoscopic surgery in 193 (16%). The most common histological type was adenocarcinoma in 860 cases (71%), followed by squamous cell carcinoma in 273 (23%). Pathologic staging yielded stage IA in 635 cases (52%) and stage IB in 262 (22%). Conclusions: The results of our registration demonstrate a very high ratio of surgically treated stage IA cases in Niigata Prefecture and that limited resection was performed in many patients. Accumulation of these data will reveal the characteristics of lung cancer surgically treated in Niigata Prefecture and will provide a basis for determining the future course of surgical treatment for lung cancer. Registration is continuing, and it will provide new and useful information about lung cancer, eventually including 5-year survival data.     

Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.

Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption.