自适应放射治疗中的图像变形配准关键技术研究

放射治疗是恶性肿瘤的主要治疗手段之一,随着肿瘤放疗技术的不断发展,因器官运动、变形等引发的问题越来越突出,在此背景下,自适应放射治疗得以广泛推广和应用。本文主要对自适应放射治疗中的图像变形配准关键技术进行分析,重点对CT和信息缺失CBCT图像的变形配准问题进行探讨,以期能够对图像变形配准问题的解决提供一定的指导。     

脉冲1 064 nm激光视网膜损伤动物模型的建立

目的:建立脉冲1 064 nm Nd:YAG激光致视网膜出血性损伤及非出血性损伤动物模型,为治疗药物评价提供技术基础.方法:应用自由振荡脉冲及调Q脉冲1 064 nm激光照射青紫蓝灰兔视网膜,通过在光路中加人透镜获得直径200μm眼底光斑,加入衰减片改变角膜入射激光能量.照射即刻对损伤应用检眼镜进行实时观察,并用眼底相...     

一种同步观察喜树碱与菌根丛枝结构的方法

本文介绍一种用激光共聚焦扫描显微镜同步观察喜树碱、丛枝茵根及其结构的方法.喜树丛枝茵根经甘油浸润和明胶包埋后制成切片,再经酸性品红染色后在激光共聚焦扫描显微镜下观察.波长488mm处激发光下可获得清晰的丛枝茵根透射图像.364 nm处激发光下可获得喜树碱的荧光图像,通过两图像的叠加,在喜树丛枝菌根中得到喜树碱的定位图像.用此方法初步研究了喜树丛枝茼根的喜树碱分布.     

医学图像融合配准技术

图像融合技术在现代医学中扮演着极其重要的角色,是现代医学图像技术研究的重点。图像融合技术中,图像的配准又是其中的重点、难点和热点。本文按照图像变换特性对图像配准进行了分类,对每个类别的不同配准方法(特征点的获取、图像配准的变换等)进行介绍。但是,图像配准是一个尚处在发展阶段的学科,实现配准的精确化、快速化、自动化仍需要进一步的努力。     

基于配准的插值

断层间图像插值是三维重建的一个关键步骤,因为图像上像素之间的间隔常常小于断层图像之间的距离,而在三维重建需要它们有一致的分辨率.由于是同模态断层图像层间插值,对于解决同模态弹性配准问题,Thirion的demons算法比较适合.所以配准采用Demons方法.Demons算法先判断出待配准图像上各个象素点的运动方法,通过对各个象素点的移动来实现非刚性配准.在这个算法中,每张图像都被视为同灰度值轮廓的集合.该算法可以应用于精度要求比较高的体数据插值重建过程.     

使用Radon变换进行二维MRI图像配准

使用了一种基于Radon变换的技术来进行二维的MRI图像配准。MRI的图像配准一般使用灰度配准,而Radon变换一般用于CT图像的重建,虽然现已经存在使用Radon变换进行图像配准,但是比较繁琐,我们对这一配准算法进行了简化。     

激光辐照微藻的显微图像观察及荧光定量分析

利用激光共聚焦扫描显微镜观察激光辐照前后微藻细胞叶绿体自体荧光图像,并对荧光变化进行定量分析。用Nd:YAP激光辐照扁藻、金藻及三角褐指藻。实验结果表明:Nd:YAP激光辐照后,藻细胞荧光光谱峰位不变,但荧光峰值发生较大变化,在激光促长剂量辐照下,几种微藻细胞的荧光强度均比对照组强。激光辐照微藻产生的生理刺激效应可以反映在细胞的荧光特性与强度变化上。激光共聚焦扫描显微镜可以作为微藻激光生物效应研究的一种有效方法。     

检测眼睛的CT

眼底病变多达数百种 ,但现有的检查设备 ,如检眼镜、B超、荧光素眼底血管造影等 ,不是分辨率有限 ,就是有创伤性。所以多年来 ,眼底病学者们一直在努力寻找一种高灵敏度、无创伤、患者又易于接受的检查手段。目前国际上流行一种光学相干断层扫描OCT ,被称为检测眼睛的CT ,它使用近红外的低相干光作为探测源 ,是一种非损伤性、非接触性 ,对眼底视网膜细微结构进行横截面扫描的新的影像学检查方法。它能清晰显示视网膜不同层次的结构并能对视网膜的细微结构进行客观定量的测量和分析 ,并可获得接近 10微米的高分辨率图像。患者只要坐着 ,下…     

自适应指数加权互信息配准医学图像的测度及相应算法

为配准医学图像,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential Weighted Mutual Informa- tion,AEWMI)测度,分析表明:通过对互信息(Mutual Information,MI)测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;而指数的权值则可以通过评估待配准图像的质量和分辨率大小来自适应确定。仿真实验结果在验证分析结果的同时也表明,基于本文AEWMI测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。     

肺癌影像引导放疗人工图像配准方法分析

目的分析千伏锥形束CT（KVCBCT）引导肺癌放疗人工图像配准法的重复性。方法选择16例在我院行根治性放疗的非小细胞肺癌患者,每周行KVCBCT在线引导体位校正一次,获取患者KVCBCT影像。图像配准选择肺尖和椎体作为参考标记,在矢状位、冠状位和横断位等中心层面上配准患者KVCBCT影像和计划设计cT影像。比较同一名医生相隔一周两次配准,不同医生之间配准和医生与技术员之间配准结果的差异,用于评价KVCBCT引导肺癌放疗人工图像配准法的重复性。结果同一位医生相隔一周两次配准同一幅KVCBCT影像与计划设计cT影像,配准结果在患者左右（LR）、头脚（sI）和前后（AP）三个方向上,差值大于3mm所占的比例分别为：0,13％和6％。不同医生之间的配准结果在LR、SI和AP三个方向上,差值大于3mm所占的比例分别为：11％,19％和14％。医生与技术员的配准结果在LR、SI和AP三个方向上差值大于3mm所占的比例,分别为：16％,27％和27％。结论KVCBCT引导肺癌放疗人工图像配准法的重复性有待进一步提高。尤其表现为不同医生,医生与技术员之间应用该方法的重复性较差。KVCBCT引导肺癌放疗的图像配准方法需要进一步研究。     

The utility of deformable image registration for small artery visualisation in contrast-enhanced whole body MR angiography

PurposeAn investigation was carried out into the effect of three image registration techniques on the diagnostic image quality of contrast-enhanced magnetic resonance angiography (CE-MRA) images.MethodsWhole-body CE-MRA data from the lower legs of 27 patients recruited onto a study of asymptomatic atherosclerosis were processed using three deformable image registration algorithms. The resultant diagnostic image quality was evaluated qualitatively in a clinical evaluation by four expert observers, and quantitatively by measuring contrast-to-noise ratios and volumes of blood vessels, and assessing the techniques' ability to correct for varying degrees of motion.ResultsThe first registration algorithm (‘AIR’) introduced significant stenosis-mimicking artefacts into the blood vessels' appearance, observed both qualitatively (clinical evaluation) and quantitatively (vessel volume measurements). The two other algorithms (‘Slicer’ and ‘SEMI’), based on the normalised mutual information (NMI) concept and designed specifically to deal with variations in signal intensity as found in contrast-enhanced image data, did not suffer from this serious issue but were rather found to significantly improve the diagnostic image quality both qualitatively and quantitatively, and demonstrated a significantly improved ability to deal with the common problem of patient motion.ConclusionsThis work highlights both the significant benefits to be gained through the use of suitable registration algorithms and the deleterious effects of an inappropriate choice of algorithm for contrast-enhanced MRI data. The maximum benefit was found in the lower legs, where the small arterial vessel diameters and propensity for leg movement during image acquisitions posed considerable problems in making accurate diagnoses from the un-registered images.     

Dynamic Lung Modeling and Tumor Tracking Using Deformable Image Registration and Geometric Smoothing

A greyscale-based fully automatic deformable image registration algorithm, based on an optical flow method together with geometric smoothing, is developed for dynamic lung modeling and tumor tracking. In our computational processing pipeline, the input data is a set of 4D CT images with 10 phases. The triangle mesh of the lung model is directly extracted from the more stable exhale phase (Phase 5). In addition, we represent the lung surface model in 3D volumetric format by applying a signed distance function and then generate tetrahedral meshes. Our registration algorithm works for both triangle and tetrahedral meshes. In CT images, the intensity value reflects the local tissue density. For each grid point, we calculate the displacement from the static image (Phase 5) to match with the moving image (other phases) by using merely intensity values of the CT images. The optical flow computation is followed by a regularization of the deformation field using geometric smoothing. Lung volume change and the maximum lung tissue movement are used to evaluate the accuracy of the application. Our testing results suggest that the application of deformable registration algorithm is an effective way for delineating and tracking tumor motion in image-guided radiotherapy.     

Interplane bulk motion analysis and removal based on normalized cross‐correlation in optical coherence tomography angiography

Bulk motion seriously degrades the image quality of optical coherence tomography angiography (OCTA). Conventional correction methods focus on in‐plane displacement, while the bulk motion component perpendicular to B‐scans also introduces noise. This work first presents an evaluation of this component using a specific scan protocol and an approximate expression derived from peak‐normalized cross‐correlation values, and then quantitatively assesses how interplane bulk motion noise reduce the sensitivity of cross‐sectional angiograms. Finally, we developed a repetitive bulk motion correction method based on the estimated displacements and redundant volume scans. The correction does not require registration and angiogram reconstruction of low flow sensitivity frames, and the results of in vivo mice skin OCTA imaging experiments show that the proposed method can effectively reduce bulk motion noise caused by cardiac and respiratory motion and occasional shaking, and improve OCTA image quality, which has practical significance for clinical OCTA diagnosis and analysis.     

Image Quality Improvement in Adaptive Optics Scanning Laser Ophthalmoscopy Assisted Capillary Visualization Using B-spline-based Elastic Image Registration

Purpose

To investigate the effect of B-spline-based elastic image registration on adaptive optics scanning laser ophthalmoscopy (AO-SLO)-assisted capillary visualization.

Methods

AO-SLO videos were acquired from parafoveal areas in the eyes of healthy subjects and patients with various diseases. After nonlinear image registration, the image quality of capillary images constructed from AO-SLO videos using motion contrast enhancement was compared before and after B-spline-based elastic (nonlinear) image registration performed using ImageJ. For objective comparison of image quality, contrast-to-noise ratios (CNRS) for vessel images were calculated. For subjective comparison, experienced ophthalmologists ranked images on a 5-point scale.

Results

All AO-SLO videos were successfully stabilized by elastic image registration. CNR was significantly higher in capillary images stabilized by elastic image registration than in those stabilized without registration. The average ratio of CNR in images with elastic image registration to CNR in images without elastic image registration was 2.10 ± 1.73, with no significant difference in the ratio between patients and healthy subjects. Improvement of image quality was also supported by expert comparison.

Conclusions

Use of B-spline-based elastic image registration in AO-SLO-assisted capillary visualization was effective for enhancing image quality both objectively and subjectively.     

Double-step registration of in vivo stereophotogrammetry with both in vitro 6-DOFs electrogoniometry and CT medical imaging

Standard registration techniques of bone morphology to motion analysis data often lead to unsatisfactory motion simulation because of discrepancies during the location of anatomical landmarks in the datasets. This paper describes an iterative registration method of a three-dimensional (3D) skeletal model with both 6 degrees-of-freedom joint kinematics and standard motion analysis data. The method is demonstrated in this paper on the lower limb. The method includes two steps. A primary registration allowed synchronization of in vitro kinematics of the knee and ankle joints using flexion/extension angles from in vivo gait analysis. Results from primary registration were then improved by a so-called advanced registration, which integrated external constraints obtained from experimental gait pre-knowledge. One cadaver specimen was analyzed to obtain both joint kinematics of knee and ankle joints using 3D electrogoniometry, and 3D bone morphology from medical imaging data. These data were registered with motion analysis data from a volunteer during the execution of locomotor tasks. Computer graphics output was implemented to visualize the results for a motion of sitting on a chair. Final registration results allowed the observation of both in vivo motion data and joint kinematics from the synchronized specimen data. The method improved interpretation of gait analysis data, thanks to the combination of realistic 3D bone models and joint mechanism. This method should be of interest both for research in gait analysis and medical education. Validation of the overall method was performed using RMS of the differences between bone poses estimated after registration and original data from motion analysis.     

In Vivo Analysis of Heterogeneous Brain Deformation Computations for Model-Updated Image Guidance

Neurosurgical image-guidance has historically relied on the registration of the patient and preoperative imaging series with surgical instruments in the operating room (OR) coordinate space. Recent studies measuring intraoperative tissue motion have suggested that deformation-induced misregistration from surgical loading is a serious concern with such systems. In an effort to improve registration fidelity during surgery, we are pursuing an approach which uses a predictive computational model in conjunction with data available in the OR to update the high resolution preoperative image series. In previous work, we have developed an in vivo experimental system in the porcine brain which has been used to investigate a homogeneous finite element rendering of consolidation theory as a tissue deformation model. In this paper, our computational approach has been extended to include heterogeneous tissue property distributions determined from an image-to-grid segmentation scheme. Results produced under two different loading conditions show that heterogeneity in the stiffness properties and interstitial pressure gradients varied over a range of physiologically reasonable values account for 1-3% and 5-8% of the predicted tissue motion, respectively, while homogeneous linear elasticity is responsible for 60-70% of the surgically-induced motion that has been recoverable with our model-based approach.     

Impact of motion induced artifacts on automatic registration of lung tumors in Tomotherapy

PurposeTomotherapy MV-CT acquisitions of lung tumors lead to artifacts due to breathing-related motion. This could preclude the reliability of tumor based positioning. We investigate the effect of these artifacts on automatic registration and determine conditions under which correct positioning can be achieved.Materials and methodsMV-CT and 4D-CT scans of a dynamic thorax phantom were acquired with various motion amplitudes, directions, and periods. For each acquisition, the average kV-CT image was reconstructed from the 4D-CT data and rigidly registered with the corresponding MV-CT scan in a region of interest. Different kV–MV registration strategies have been assessed.ResultsAll tested registration methods led to acceptable registration errors (within 1.3 ± 1.2 mm) for motion periods of 3 and 6 s, regardless of the motion amplitude, direction, and phase difference. However, a motion period of 5 s, equal to half the Tomotherapy gantry period, induced asymmetric artifacts within MV-CT and significantly degraded the registration accuracy.ConclusionsAs long as the breathing period differs from 5 s, positioning based on averaged images of the tumor provides information about its daily baseline shift, and might therefore contribute to reducing margins, regardless of the registration method.     

In Vivo Analysis of Heterogeneous Brain Deformation Computations for Model-Updated Image Guidance

Abstract

Neurosurgical image-guidance has historically relied on the registration of the patient and preoperative imaging series with surgical instruments in the operating room (OR) coordinate space. Recent studies measuring intraoperative tissue motion have suggested that deformation-induced misregistration from surgical loading is a serious concern with such systems. In an effort to improve registration fidelity during surgery, we are pursuing an approach which uses a predictive computational model in conjunction with data available in the OR to update the high resolution preoperative image series. In previous work. we have developed an in vivo experimental system in the porcine brain which has been used to investigate a homogeneous finite element rendering of consolidation theory as a tissue deformation model. In this paper, our computational approach has been extended to include heterogeneous tissue property distributions determined from an image-to-grid segmentation scheme. Results produced under two different loading conditions show that heterogeneity in the stiffness properties and interstitial pressure gradients varied over a range of physiologically reasonable values account for 1-3% and 5-8% of the predicted tissue motion, respectively, while homogeneous linear elasticity is responsible for 60-70% of the surgically-induced motion that has been recoverable with our model-based @approach.     

Model-based simulation of dynamic magnetic resonance imaging signals

This paper presents a model-based method to efficiently simulate dynamic magnetic resonance imaging signals. Using an analytical spatiotemporal object model, the method can approximate time-varying k-space signals such as those from objects in motion and/or during dynamic contrast enhancement. Both rigid-body and non-rigid-body motions can be simulated using the proposed method. In addition, it can simulate data with arbitrary data sampling order and/or non-uniform k-space trajectory. A set of simulated images were compared with real data acquired from a rat model on a 4.7 T scanner to verify the model. The efficient simulation method is expected to be useful for rapid testing of various imaging and image analysis algorithms such as image reconstruction, image registration, motion compensation, and kinetic parameter mapping.     

Registration and functional analysis of CT dynamic image sequences for the follow-up of patients with hepatic tumors undergoing antiangiogenic therapy

ObjectiveA new approach for evaluating tumor response to antiangiogenic treatment using dynamic contrast-enhanced perfusion computed tomography (CT) was provided.Patients and methodsFive patients, with hepatic tumors, were examined before and a few weeks after therapy. Following injection of a contrast agent, dynamic image acquisitions were obtained during two minutes, with eight axial sections by volume. To analyze these functional data, we proposed an image processing pipeline. We first applied a rigid registration, based on a blockmatching method, to correct for respiratory motion. We then calculated parametric image volumes, using adapted reference kinetics. These image volumes allowed us to differentiate between the various contrast enhancement kinetics (arterial/venous, healthy/pathological tissues), following the injection of contrast agent.ResultsThe registration was validated qualitatively (by visual inspection of registered image volumes) and quantitatively (using criteria based on the estimation of a respiratory motion component). Parametric image volumes allowed us to differentiate healthy tissues from tumor tissues, and to display necrotic regions, which occurred in four patients, after therapy.DiscussionThe proposed approach allows us to compensate for respiratory motion in a region localized around the tumor and could be further extended by a nonrigid registration.ConclusionThe robust computation of parametric image volumes enables a local and precise display of the tumor response to an antiangiogenic therapy.