计算机辅助医学图像三维重建的算法分析

背景：三维重建技术是采用计算机技术对二维医学图像进行边界识别,重新还原出被检组织或器官的三维图像。目的：分忻在不同情况下进行医学图像三维重建时如何进行算法的选择。。方法：采用计算机检索中国期刊全文数据库和Pubmed数据库。中文检索词为“医学图像,三维重建,面绘制,体绘制”,英文检索词为“medicalimages,three—dimensionalreconstruction,surfacerendering,volumerendering”。检索与医学图像三维重建算法相关的文献33篇,从面绘制重置方法和体绘制重置方法的实现原理、实现复杂度、实时显示情况等方面进行分析。结果与结论：目前,医学图像三维重建根据绘制过程中数据描述方法的不同可分为三大类：面绘制方法、体绘制方法和混合绘制方法。通过对面绘制和体绘制方法中不同算法的分析,可以看到面绘制方法在算法效率和实时交互性上是优于体绘制的,虽然面绘制方法在绘制时候会丢失许多细节,使得绘制图像效果不理想,但是由于其算法比较简单,占用内存资源少,所以目前得到了广泛的运用。体绘制方法是对体数据场中的体索进行直接操作,可以绘制出三维数据场中更丰富的信息,因此体绘制方法的绘制效果优于面绘制方法。     

医学图像可视化及加速技术的研究进展和趋势

背景：近年来图像可视化技术蓬勃发展,相应的各种加速算法也层出不穷,但少有文献就此方面进行综述和概括。目的：尝试对医学图像可视化技术及加速技术的研究进展和发展趋势进行归纳和概括。方法：由第一作者检索2000／2009PubMed数据及万方数据库有关医学图像三维重建、科学计算可视化、可视化加速技术等方面的文献。计算机初检得到56篇文献,根据纳入标准保留23篇进一步归纳总结。结果与结论：医学图像可视化技术可以在重建三维图像模型的基础上,进行定性定量分析,便于人们更清楚地认识蕴涵在体数据中的复杂结构,这对于医学研究和临床诊断都具有十分重要的理论意义和应用价值。医学图像可视化技术通常分成面绘制和体绘制这两大类。’由于面绘制技术的缺点和局限,以及计算机技术的迅猛发展,目前人们越来越多地关注体绘制技术及其加速技术。医学图像可视化技术及其加速技术如能与虚拟现实及GPU相结合;’必能发挥更大的作用,真正体现其价值。     

医学图像可视化及加速技术的研究进展和趋势

背景:近年来图像可视化技术蓬勃发展,相应的各种加速算法也层出不穷,但少有文献就此方面进行综述和概括.目的:尝试对医学图像可视化技术及加速技术的研究进展和发展趋势进行归纳和概括.方法:由第一作者检索2000/2009 PubMed数据及万方数据库有关医学图像三维重建、科学计算可视化、可视化加速技术等方面的文献.计算机初检得到56篇文献,根据纳入标准保留23篇进一步归纳总结.结果与结论:医学图像可视化技术可以在重建三维图像模型的基础上,进行定性定量分析,便于人们更清楚地认识蕴涵在体数据中的复杂结构,这对于医学研究和临床诊断都具有十分重要的理论意义和应用价值.医学图像可视化技术通常分成面绘制和体绘制这两大类.由于面绘制技术的缺点和局限,以及计算机技术的迅猛发展,目前人们越来越多地关注体绘制技术及其加速技术.医学图像可视化技术及其加速技术如能与虚拟现实及GPU相结合,必能发挥更大的作用,真正体现其价值.     

基于VTK和ITK环境的超声图像三维重建

背景:医学图像的三维模型,能够准确的三维结构在临床诊断上凸显重要性。目的:对连续多帧的超声图像进行三维结构重建。方法:利用可视化工具VTK和图像的配准分割工具ITK,在VC++的平台下,采取直接体绘制的方法,对连续多帧的DICOM医学超声图像进行了三维重建,并且用户可以利用鼠标与图片进行交互,实现任意角度的旋转。结果与结论:合成体绘制在重建中的效果较优,相对而言更适合超声图像的三维重建。     

基于VTK和ITK环境的超声图像三维重建

背景：医学图像的三维模型,能够准确的三维结构在临床诊断上凸显重要性。目的：对连续多帧的超声图像进行三维结构重建。方法：利用可视化工具VTK和图像的配准分割工具ITK,在VC＋＋的平台下,采取直接体绘制的方法,对连续多帧的DICOM医学超声图像进行了三维重建,并且用户可以利用鼠标与图片进行交互,实现任意角度的旋转。结果与结论：合成体绘制在重建中的效果较优,相对而言更适合超声图像的三维重建。     

医学图像三维重建系统的关键技术研究与设计

背景:医学影像三维可视化技术将二维断层图像转化为三维图像,有利于提高医疗规划的准确性,是当今医学领域研究的热点,在诊断医学、手术规划、模拟仿真等领域都有重要的应用.目的:利用二维医学图像序列重建出三维模型的关键技术,对可视化系统进行总体设计.方法;首先研究现有三维重建技术,包括预处理技术,图像分割和配准可视化算法.其次给出了系统体系结构设计图,各模块中应用到各种三维重建关键技术.结果与结论:根据现有关键技术的研究,选用OpenGL作为可视化开发工具,设计了一种基于PC机的三维医学图像可视化系统.     

一种新型求交方法在超声医学图像体绘制算法中的应用

会制算法是一种较好的三维重建显示算法,但针对超声医学图像处理时仍存在不足。本文创新的提出了一种反向旋转变化的求交算法,取代了光线与平行四边形求交的繁琐运算,通过对模拟物体和超声心脏图像的重建结果可以看出方法正确且显示的图像具有较高质量。     

基于Visualization Toolkit的脑模型三维重建方法研究

目的利用可视化工具包VisualizationToolkit(VTK)结合VC++实现医学图像三维可视化。方法基于头部CT测量数据,采用MarchingCubes算法和Raycasting算法重建出头模型的表皮和颅骨。结果和结论VTK使用灵活,功能强大,利用它进行图像重建,具有重建步骤简单、效果好、速度快、交互能力强等优点,可以被广泛应用于医学图像的重建中。     

医学图像的三维重建及基于VTK的实现和应用

背景:VTK是一个免费的图像三维重建和处理的专业开发平台,其功能强大,源代码开放,用户可以根据自己的需求灵活的定制和开发。目的:介绍图像三维可视化中常用的面绘制和体绘制两类可视化技术的原理,以及其典型的Marching Cubes和Ray Casting三维重建算法,并对重建的三维医学图像的应用和扩展进行了探讨。方法:基于免费的VTK可视化开发包平台和Visual C++6.0IDE开发工具,使用C++语言,采用真实人体CT数据集,实现CT图像的三维重建和应用扩展。结果与结论:基于VTK平台,采用面绘制和体绘制不同绘制原理实现了医学图像的三维可视化。重建得到的三维医学图像,显示效果清晰直观,并且可以配合进行三维医学图像的测量、虚拟切割等操作,取得了较好的效果。     

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.     

A hybrid framework for 3D medical image segmentation

In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models' output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model's parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.     

Statistical shape models for 3D medical image segmentation: A review

Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images. While 2D models have been in use since the early 1990s, wide-spread utilization of three-dimensional models appeared only in recent years, primarily made possible by breakthroughs in automatic detection of shape correspondences. In this article, we review the techniques required to create and employ these 3D SSMs. While we concentrate on landmark-based shape representations and thoroughly examine the most popular variants of Active Shape and Active Appearance models, we also describe several alternative approaches to statistical shape modeling. Structured into the topics of shape representation, model construction, shape correspondence, local appearance models and search algorithms, we present an overview of the current state of the art in the field. We conclude with a survey of applications in the medical field and a discussion of future developments.     

CAN3D: Fast 3D medical image segmentation via compact context aggregation

Direct automatic segmentation of objects in 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying multiple individual structures with complex geometries within a large volume under investigation. Most deep learning approaches address these challenges by enhancing their learning capability through a substantial increase in trainable parameters within their models. An increased model complexity will incur high computational costs and large memory requirements unsuitable for real-time implementation on standard clinical workstations, as clinical imaging systems typically have low-end computer hardware with limited memory and CPU resources only. This paper presents a compact convolutional neural network (CAN3D) designed specifically for clinical workstations and allows the segmentation of large 3D Magnetic Resonance (MR) images in real-time. The proposed CAN3D has a shallow memory footprint to reduce the number of model parameters and computer memory required for state-of-the-art performance and maintain data integrity by directly processing large full-size 3D image input volumes with no patches required. The proposed architecture significantly reduces computational costs, especially for inference using the CPU. We also develop a novel loss function with extra shape constraints to improve segmentation accuracy for imbalanced classes in 3D MR images. Compared to state-of-the-art approaches (U-Net3D, improved U-Net3D and V-Net), CAN3D reduced the number of parameters up to two orders of magnitude and achieved much faster inference, up to 5 times when predicting with a standard commercial CPU (instead of GPU). For the open-access OAI-ZIB knee MR dataset, in comparison with manual segmentation, CAN3D achieved Dice coefficient values of (mean = 0.87 ± 0.02 and 0.85 ± 0.04) with mean surface distance errors (mean = 0.36 ± 0.32 mm and 0.29 ± 0.10 mm) for imbalanced classes such as (femoral and tibial) cartilage volumes respectively when training volume-wise under only 12G video memory. Similarly, CAN3D demonstrated high accuracy and efficiency on a pelvis 3D MR imaging dataset for prostate cancer consisting of 211 examinations with expert manual semantic labels (bladder, body, bone, rectum, prostate) now released publicly for scientific use as part of this work.     

A 3D image reconstruction technique for spinning targets based on Radar networks

A three-dimensional (3D) image reconstruction method for the spinning target based on the radar network is proposed in this letter and the design concept is sourced from the similarity of the two-dimensional (2D) imaging result with the target’s mapping on the imaging plane. The proposed method has the capacity to generate 3D image of the spinning target by reconstructing the scattering distribution in 3D Cartesian space. Firstly, the inverse Radon transform is applied to obtain the imaging result of the spinning target and the mapping formulas for mapping the air points onto the 2D image plane are derived. In addition, the radar network 3D image reconstruction model for the spinning target is constructed and the corresponding algorithm for solving the reconstruction model is proposed. Finally, some experiment results are given to verify the effectiveness of the proposed method.     

2D probabilistic undersampling pattern optimization for MR image reconstruction

With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high-grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.