Fore-Iterative重建方法中迭代次数和子集数对SPM分析结果的影响

目的:探讨功能图像常用的三维重建方法Fore-Iterative中不同迭代次数和不同子集数目对统计参数图软件(SPM)统计比较结果的影响.方法:利用Hoffman标准脑模型进行PET成像与该模型制作的缺损模型进行PET成像后,采用临床常用参数进行三维重建,比较统计参数图(Statistical parametric mapping,SPM)的团体T检验结果.结果:(1)随着迭代次数的增加SPM得到的激活区增大,敏感度增加,差异显著性提高.(2)子集数目不同得到的激活区大小和假阳性不同,其中子集为32时得到的激活区最小,特异性最高.结论:迭代5次和子集数为32时使用SPM得到的结果最逼近真实值,产生较少的假阳性结果.     

女性尿道阴道瘘数字化三维重建在临床治疗中的初步应用

目的:基于CT扫描图象建立精确的女性尿道阴道瘘数字化模型,探讨其在临床诊断及治疗中的应用。方法:选择1例女性尿道阴道瘘病例,进行尿道CT连续断层扫描,扫描结果导入Mimics软件中进行三维重建,利用三维重建模型指导临床。结果:建立女性尿道阴道瘘及周围结构的三维立体模型,可以方便地从任意角度和方向观察瘘管情况,测量有关的数据;还可以在数字化模型上进行手术设计。结论:女性尿道阴道瘘的数字化三维模型能够更直观、准确地反映病变部位的三维立体结构。对女性尿道阴道瘘的诊断、手术规划等有较大帮助。     

男性尿道直肠瘘数字化三维重建的初步应用

目的：基于CT扫描图象建立精确的男性尿道直肠瘘数字化模型,探讨其在临床诊断及治疗中的应用。方法：选择1例男性尿道直肠瘘病例,进行尿道CT连续断层扫描,扫描结果导入Mimics软件中进行三维重建,利用三维重建模型指导临床。结果：建立男性尿道直肠瘘及周围结构的三维立体模型,可以方便地从任意角度和方向观察瘘管情况,测量有关的数据;还可以在数字化模型上进行手术设计。结论：男性尿道直肠瘘的数字化三维模型能够更直观、准确地反映病变部位的三维立体结构。对男性尿道直肠瘘的诊断、手术规划等有较大帮助。     

C T、常规MRI 序列和SWI 诊断弥漫性轴索脑损伤比较

目的:探讨弥漫性轴索损伤(DAI)的CT、常规MRI序列和磁敏感加权成像(SWI)表现与诊断价值。方法:回顾分析42例DAI患者的影像资料,分析、比较CT、MRI和SWI的信号特征及脑内病灶显示率。结果:SWI显示病灶最多、最敏感;脑CT扫描次之;常规MRI序列敏感性差,只有部分病灶显示。结论:CT、MRI和SWI对DAI早期诊断、治疗及评价预后具有重要参考价值,SWI是诊断DAI最有效的首选影像学检查方法。     

颅底孔在多层CT三维重建中的测量研究

探讨颅底MSCT三维重建图像效果及主要孔的正常值,采用多层螺旋CT(MSCT),对200名正常成人(男100名,女100名)进行颅脑扫描,利用电子计算机三维重建程序立体地显示颅底外面的卵圆孔、棘孔、颈动脉管外口、破裂孔、茎乳孔、枕骨大孔,并观察其形态和测出其内径及其性差。     

昆虫脑神经髓结构图谱构建方法综述

脑控制和调节所有动物的行为,昆虫也不例外,构建脑神经髓结构图谱有利于阐明其对行为调控的神经机制。目前,除一些模式昆虫的脑图谱被构建外,大多数昆虫仅针对少数易识别的神经髓(如视叶、触角叶和蕈形体等)进行了三维重建,而对脑内大部分区域还未描述,这与其结构的复杂性有关。随着共聚焦显微成像和计算机三维重建技术的发展,人们有机会获取全脑的结构信息,并构建出三维可视化的图谱,这为研究全脑神经髓的功能提供了重要平台。特别是果蝇全脑神经髓的系统命名法的建立,极大的推动了昆虫脑神经髓结构的研究进展。本文对昆虫脑的结构组成、免疫染色方法、标准脑构建及脑神经髓命名等方面进行综述,提出在构建脑神经髓图谱过程中需要注意的问题及解决办法,这将为推动国内昆虫脑神经髓图谱的构建提供参考。     

CT三维重建技术在肺磨玻璃结节诊断中的研究进展

三维重建技术在肺磨玻璃结节(ground-glass opacity,GGO)鉴别诊断中具有重要作用,比传统的二维CT对于相关图像参数的测量更准确,更完整。近年来国内外学者对三维重建下GGO影像学变量包括结节平均直径、体积及CT值等在非典型腺瘤样增生(atypical adenomatous hyperplasia,AAH)、原位癌(adenocarcinoma in situ,AIS)、微浸润腺癌(minimally invasive adenocarcinoma,MIA)及ⅠA期浸润性腺癌(invasive adenocarcinoma,IAC)的鉴别诊断方面的研究越来越多样化。对作为早期肺癌病灶的GGO,三维重建测量可从其体积-倍增时间、CT值的峰度和偏度、平均CT值及CT值分布的方差等参数来进行术前良恶性评估。本文针对近年来国内外对三维重建处理技术在GGO诊断中的研究予以回顾和总结,旨在对GGO临床诊治予以提示。     

64层螺旋CT三维重建技术对下肢动脉粥样硬化病变的显示

目的探讨64层螺旋CT三维重建技术对下肢动脉病变的显示。方法103例下肢动脉粥样硬化的患者行64层螺旋CT血管造影检查资料在工作站进行多种三维重建,分析其影像表现。结果103例患者均获得了成功的造影图像,2459根下肢动脉血管得到了显示。其中845根(34.4%)下肢动脉血管正常;672根(27.3%)轻度狭窄;327根(13.3%)中度狭窄;292根(11.9%)重度狭窄,323根(13.1%)下肢动脉血管闭塞。结论64层螺旋CT三维重建是又一种无创性的、可靠的、简便的检查下肢动脉粥样硬化的方法,具有较高的临床价值。     

CT、常规MRI序列和SWI诊断弥漫性轴索脑损伤比较

目的：探讨弥漫性轴索损伤（DAI）的CT、常规MRI序列和磁敏感加权成像（SWI）表现与诊断价值。方法：回顾分析42例DAI患者的影像资料,分析、比较CT、MRI和SWI的信号特征及脑内病灶显示率。结果：SWI显示病灶最多、最敏感;脑CT扫描次之;常规MRI序列敏感性差,只有部分病灶显示。结论：CT、MRI和SWI对DAI早期诊断、治疗及评价预后具有重要参考价值,SWI是诊断DAI最有效的首选影像学检查方法。     

东亚飞蝗脑的形态学观察及三维重建

本文主要研究东亚飞蝗Locusta migratoria manilansis(Meyen)脑部的形态结构及其三维重建模型。采用石蜡包埋切片,在光镜下观察了东亚飞蝗脑部的形态结构,其由前脑、中脑和后脑3部分组成。为了获得整只蝗虫的连续、完整的图像数据集,采用冰冻切片技术将冰冻包埋剂(OCT)包埋的飞蝗成虫做连续切片。然后利用图像处理方法对飞蝗脑部的连续切片进行配准、分割,再用三维重建软件Image-Pro Plus(IPP)对分割后的脑部二维图像序列进行三维重建,构建出的飞蝗脑部三维结构模型可以任意旋转,能从不同角度观察。其结果为蝗虫生理和防蝗治蝗提供科学依据。     

Reconstruction of axial tomographic high resolution data from confocal fluorescence microscopy: a method for improving 3D FISH images.

Fluorescent confocal laser scanning microscopy allows an improved imaging of microscopic objects in three dimensions. However, the resolution along the axial direction is three times worse than the resolution in lateral directions. A method to overcome this axial limitation is tilting the object under the microscope, in a way that the direction of the optical axis points into different directions relative to the sample. A new technique for a simultaneous reconstruction from a number of such axial tomographic confocal data sets was developed and used for high resolution reconstruction of 3D-data both from experimental and virtual microscopic data sets. The reconstructed images have a highly improved 3D resolution, which is comparable to the lateral resolution of a single deconvolved data set. Axial tomographic imaging in combination with simultaneous data reconstruction also opens the possibility for a more precise quantification of 3D data. The color images of this publication can be accessed from http://www.esacp.org/acp/2000/20-1/heintzmann.++ +htm. At this web address an interactive 3D viewer is additionally provided for browsing the 3D data. This java applet displays three orthogonal slices of the data set which are dynamically updated by user mouse clicks or keystrokes.     

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

PurposeTo study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed.Materials and methodsThe RSEMD method was tested on in-silico, Catphan^®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver.ResultsIn all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms.ConclusionsIn this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.     

Semiautomatic algorithm for lymph node analysis corrected for partial volume effects in combined positron emission tomography-computed tomography

Positron emission tomography-computed tomography (PET-CT) is superior compared to stand-alone PET in evaluation of malignancies. Few studies have employed high-resolution structural information to correct PET. We designed a semiautomatic algorithm using CT and PET to obtain a partial volume corrected (PVC) standardized uptake value (SUV) and a combined morphologic and functional parameter (multimodal SUV) for lymph node assessment. Lesions were segmented by a semiautomatic algorithm in CT images. Lesion volume was used for PVC and for calculating the multimodal SUV. The method was applied to 47 lymph nodes (30 patients) characterized as suspicious in 18F-fluorodeoxyglucose-PET-CT. In phantoms, PVC improved significantly the measured uptake of the lesion. In patients, 36 lymph nodes could be segmented without problems; in 11 lesions, a manual interaction was necessary. SUVs before PVC (mean 1.29) increased significantly (p < .0005) after PVC (mean 2.8). If SUV 2.5 was used as a threshold value to distinguish between benign and malignant lesions, 11 of the 47 lesions changed from benign to malignant after the PVC. The mean multimodal SUV was 0.39 mL for the benign lesions and 4.47 mL for the malignant lesions. In this work we presented a method for quantitative analysis of lymph nodes in PET-CT. PVC leads to significant differences in SUV.     

Symmetry-adapted spherical harmonics method for high-resolution 3D single-particle reconstructions

Three-dimensional (3D) reconstruction is the last and an essential step toward high-resolution structural determination in single-particle cryo-electron microscopy (cryoEM). We have implemented a new algorithm for reconstructing 3D structures of macromolecular complexes with icosahedral symmetry from cryoEM images. Icosahedral symmetry-adapted functions (ISAFs) are used to interpolate structural factors in the reciprocal space to generate a 3D reconstruction in spherical coordinates. In our implementation, we introduced a recursive method for deriving higher order ISAFs from three lower order seed functions. We demonstrate improvements of our new method in both the noise suppression and the effective resolution in 3D reconstruction over the commonly used Fourier-Bessel synthesis method introduced by Crowther et al. three decades ago. Our 3D reconstruction method can be extended to macromolecular complexes with other symmetry types and is thus likely to impact future high-resolution cryoEM single-particle reconstruction efforts in general.     

Attenuation correction for rotating multi-segment slant-hole SPECT in breast imaging

Rotating multi-segment slant-hole (RMSSH) SPECT is suitable for detecting small and low-contrast breast lesions since it has much higher detection efficiency compared with parallel-hole SPECT for the same resolution and can image the breast at a closer distance.Our RMSSH SPECT image reconstruction extends a previous rotation-shear transformation based 3D iterative reconstruction method to include non-uniform attenuation compensation. Reconstructed RMSSH SPECT images with attenuation compensation show much improved quantitative accuracy and less image artifacts than without. We conclude that attenuation compensation provides RMSSH SPECT images with improved quality and quantitative accuracy.     

Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study

PURPOSE: To explore the effects of computed tomography (CT) slice thickness and reconstruction algorithm on quantification of image features to characterize tumors using a chest phantom. MATERIALS AND METHODS: Twenty-two phantom lesions of known sizes (10 and 20 mm), shapes (spherical, elliptical, lobulated, and spiculated), and densities [-630, -10, and +100 Hounsfield Unit (HU)] were inserted into an anthropomorphic thorax phantom and scanned three times with relocations. The raw data were reconstructed using six imaging settings, i.e., a combination of three slice thicknesses of 1.25, 2.5, and 5 mm and two reconstruction kernels of lung and standard. Lesions were segmented and 14 image features representing lesion size, shape, and texture were calculated. Differences in the measured image features due to slice thickness and reconstruction algorithm were compared using linear regression method by adjusting three confounding variables (size, density, and shape). RESULTS: All 14 features were significantly different between 1.25 and 5 mm slice images. The 1.25 and 2.5 mm slice thicknesses were better than 5 mm for volume, density mean, density SD gray-level co-occurrence matrix (GLCM) energy and homogeneity. As for the reconstruction algorithm, there was no significant difference in uni-dimension, volume, shape index 9, and compactness. Lung reconstruction was better for density mean, whereas standard reconstruction was better for density SD. CONCLUSIONS: CT slice thickness and reconstruction algorithm can significantly affect the quantification of image features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used interchangeably. Sharper and smoother reconstructions significantly affect the density-based features.     

Automated and Accurate Detection of Soma Location and Surface Morphology in Large-Scale 3D Neuron Images

Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.     

PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods

PurposeThis work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.MethodsPETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.ResultsPETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.ConclusionsPETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.