BLADE技术在3.0T磁共振克服关节扫描中运动伪影上的临床应用

目的:探讨在3.0T磁共振成像过程中,刀锋伪影较正(BLADE)技术在克服关节扫描中运动伪影方面的的的临床应用.方法:20例行四肢关节MR扫描检查的患者(肩关节10例,腕关节10例),在常规行T2WI常规扫描中出现不同程度的运动伪影,然后引入BLADE技术,扫描T2WI.,以能否清晰显示韧带肌腱而无重影为标准,对比常规序列扫描来评价BLADE技术在消除关节扫描过程中图像运动伪影方面的价值.结果:常规T2扫描出现运动伪影,用BLADE技术序列扫描出来的图像运动伪影消失,对比度明显提高.结论:应用BLADE技术进行扫描可以有效消除病人细微运动造成的运动伪影,获得高分辨率无伪影具有临床诊断价值的理想的图像.     

乳腺MR检查的常见伪影及解决方案

目的:分析乳腺MR检查中的常见伪影,探讨合理的解决方案,以提高乳腺MR图像质量及诊断准确性。方法:随机抽取我院100例行乳腺MR检查的图像资料,观察图像中出现的各种伪影及其表现,分析其产生的原因和影响因素。结果:乳腺MR图像的影像细节显示能力较强,图像质量与扫描技术关系密切,影响图像质量的常见伪影有:硬件相关伪影、图像处理相关伪影、运动相关伪影。结论:加强MR设备的使用管理,选择合理的扫描序列及参数,做好乳腺MR扫描的检查前准备及扫描,有助于提高乳腺病变的检出率。     

并行采集技术在老年人肝脏MR检查中的应用价值

目的探讨并行采集PAT技术（Parallel acquisition technique）对改善老年人肝脏磁共振扫描中出现的运动伪影的应用价值。方法对63例常规肝脏MRI检查出现呼吸运动伪影的老年患者（其中TrueFisp序列未出现明显伪影）,行iPAT技术扫描（TrueFisp冠状位、T1 Flash轴位）,对比常规序列扫描并评价iPAT技术对肝脏呼吸运动伪影消除的作用。三位磁共振专家对所得两组图像质量进行独立观察及评价,并进行对比分析。结果常规序列磁共振扫描63例肝脏呼吸运动伪影,其中Ⅰ、Ⅱ、Ⅲ和Ⅳ分别为0、15、33和15例,Ⅲ级以上影响诊断的病例共48例,占76%。采用iPAT技术扫描后,Ⅰ、Ⅱ、Ⅲ和Ⅳ分别为53、5、3和2例,Ⅲ级以下符合诊断要求的病例共58例,占92.1%,两者比较有显著差异（P〈0.05）。结论 iPAT技术可以明显缩短扫描的时间,在克服常规肝脏扫描中产生的呼吸运动伪影有明显的作用,可广泛应用于老年人肝脏常规检查产生的呼吸运动伪影校正中。     

爱克发ADC Compact CR系统常见图像伪影分析

本文以爱克发ADC Compact CR系统为例,分析CR系统常见图像伪影的产生原因。并结合CR系统的工作原理和本人工作经验,介绍各种伪影的处理过程及方法,供大家参考。     

脑血管三维DSA影像的质量控制

目的:探讨脑血管三维DSA的质量控制措施,提高影像质量.方法:对67例影像质量较差的三维DSA图像,通过与旋转DSA图像的比对分析,确定影响脑血管三维DSA图像质量的因素.结果:脑血管三维DSA图像质量与头颅运动、三维模式的选择、延迟方式与时间、对比剂注入的速率与总量、连接管和导管内对比剂充盈状态、影像的后处理、扫描定位的准确性和设备定期调试较准有关.结论:要获得良好的三维DSA影像,需遵循质量控制措施多方面综合考虑.     

智能期相联合SSF技术改善冠状动脉CTA图像运动伪影

目的:评价智能期相选择(Smart phase)和自动追踪冻结(Snap-Shot-Freeze, SSF)技术联合应用在前门控冠状动脉CT血管成像(Coronary CT angiography, CCTA)改善图像运动伪影中的价值。方法:94例疑似心血管疾病的患者行CCTA检查。所有患者图像均采用smart phase、SSF及smart phase+SSF两种技术联合进行后处理。将原始图像(A组)、smart phase图像(B组)、SSF图像(C组)和smart phase+SSF图像(D组)导入GE AW4.6工作站。对四组患者冠状动脉三大分支(右冠状动脉、左前降支及左旋支)图像质量进行主观评价(5分法)和测量每组图像主动脉根部及前胸壁肌肉CT值和标准差,并计算图像的噪声(Noise,N)、信噪比(Signal-to-noise ratio,SNR)和对比噪声比(Contrast-to-noise ratio,CNR)和比较。结果:经smart phase处理后,A组冠状动脉三大分支图像评分均得到了明显提高(P<0.05)。经SSF矫正的A组和B组冠状动脉三大分支图像质量评分均得到明显提高(P<0.05)。Smart Phase联合SSF技术得到的图像评分在所有组中评分最高,与其他组均存在显著差异(P<0.05)。结论:Smart phase联合SSF技术可明显改善前门控CCTA图像运动伪影,值得临床推广应用。     

口腔内金属材料对磁共振检查的影响

检测口腔内常用金属材料在磁共振检查时是否有伪影和伪影的严重程度。对２１种口腔内常用金属材料做了磁共振成像测试,磁共振仪磁场强度为１．５Ｔ,所用序列是梯度回波。铸金片、银汞合金、银尖等９种材料无伪影;钛合金和金属烤瓷成品有轻度伪影;牙用固位钉、椿钉等１０种材料有严重伪影。部分口腔内金属材料会引起严重伪影,影响图象质量,所以在做口腔颌面部和服部磁共振成像时,须引起重视。     

能谱CT在去除腰椎术后金属伪影中的应用进展

多层计算机断层扫描(Multi-slice computed tomography,MSCT)具有时间及空间分辨率高的特点已经被广泛应用于脊柱术后评价,然而,因为术中植入金属植入物,CT图像由于其成像原理的限制,其图像质量受到金属植入物的严重影响,金属伪影大多来源于量子噪声、散射线和射束硬化效应,降低图像的对比度,使细微解剖结构显示不清,从而使得感兴趣区域的检测能力降低,其图像质量明显下降,甚至使临床医师做出错误的诊断,因此,为了做出更准确的腰椎术后并发症诊断和术后评估,使金属伪影最小化成为关键。能谱CT及其扫描及重建技术可以有效的去除腰椎术后产生的金属伪影,改善图像质量,本文能谱CT在腰椎术后金属伪影去除方面的应用进行综述。     

下肢动脉阻塞性病变多层螺旋CT低剂量扫描成像初探

目的：探讨低剂量CT扫描在下肢动脉脉阻塞性病变诊断中的应用价值。方法：选择127段经DSA确诊的不同部位下肢动脉阻塞性病变行低剂量CT扫描,并采用MPR,VR,MIP等重建方法获得各下肢动脉CTA图像,将CTA图像与DSA图像的诊断结果利用统计学软件SAS8.1行加权Kappa一致性检验,检验水准为：Kaapa.系数大于0.75。结果：所得CTA图像与DSA图像诊断结果的一致性检验的kappa系数为0.8058,两种诊断结果的一致性为优。结论：采用低剂量扫描条件获得高质量的CTA图像在下肢动脉阻塞性病变的诊断上有肯定的价值。     

下肢动脉阻塞性病变多层螺旋CT 低剂量扫描成像初探

目的:探讨低剂量CT扫描在下肢动脉脉阻塞性病变诊断中的应用价值.方法:选择127段经DSA确诊的不同部位下肢动脉阻塞性病变行低剂量CT扫描,并采用MPR,VR,MIP等重建方法获得各下肢动脉CTA图像,将CTA图像与DSA图像的诊断结果利用统计学软件SAS8.1行加权Kappa一致性检验,检验水准为:Kaapa.系数大于0.75.结果:所得CTA图像与DSA图像诊断结果的一致性检验的kappa系数为0.8058,两种诊断结果的一致性为优.结论:采用低剂量扫描条件获得高质量的CTA图像在下肢动脉阻塞性病变的诊断上有肯定的价值.     

Deep-learning-based motion correction in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this article, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts.     

Development of a novel algorithm for metal artifact reduction in digital tomosynthesis using projection-based dual-energy material decomposition for arthroplasty: A phantom study

In this study, a novel dual-energy (DE) material decomposition reconstruction algorithm (DEMDRA) was developed using projection data with the aim of reducing metal artifacts during digital tomosynthesis (DT) for implants. Using the three-material decomposition method and decomposition projection data specific for each material, a novel DEMDRA was implemented to reduce metal artifacts via weighted hybrid reconstructed images [maximum likelihood expectation maximization (MLEM) and shift-and-add (SAA)]. Pulsed X-ray exposures with rapid switching between low and high tube potential kVp were used for DE-DT imaging, and the images were compared using conventional filtered back projection (FBP), MLEM, the simultaneous algebraic reconstruction technique total variation (SART-TV), virtual monochromatic processing, and metal artifact reduction (MAR)-processing algorithms. The reductions in metal artifacts were compared using an artifact index (AI), Gumbel distribution of the largest variations, and the artifact spread functions (ASFs) for prosthesis phantom. The novel DEMDRA yielded an adequately effective overall performance in terms of the AI, and the resulting images yielded good results independently of the type of metal used in the prosthetic phantom, as well as good noise artifact removal, particularly at greater distances from metal objects. Furthermore, the DEMDRA represented the minimum in the model of largest variations. Regarding the ASF analysis, the novel DEMDRA yielded superior metal artifact reduction when compared with conventional reconstruction algorithms with and without MAR processing. Finally, the DEMDRA was particularly useful for reducing high-frequency artifacts.     

Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Comparison of ring artifact removal methods using flat panel detector based CT images

Background

Ring artifacts are the concentric rings superimposed on the tomographic images often caused by the defective and insufficient calibrated detector elements as well as by the damaged scintillator crystals of the flat panel detector. It may be also generated by objects attenuating X-rays very differently in different projection direction. Ring artifact reduction techniques so far reported in the literature can be broadly classified into two groups. One category of the approaches is based on the sinogram processing also known as the pre-processing techniques and the other category of techniques perform processing on the 2-D reconstructed images, recognized as the post-processing techniques in the literature. The strength and weakness of these categories of approaches are yet to be explored from a common platform.

Method

In this paper, a comparative study of the two categories of ring artifact reduction techniques basically designed for the multi-slice CT instruments is presented from a common platform. For comparison, two representative algorithms from each of the two categories are selected from the published literature. A very recently reported state-of-the-art sinogram domain ring artifact correction method that classifies the ring artifacts according to their strength and then corrects the artifacts using class adaptive correction schemes is also included in this comparative study. The first sinogram domain correction method uses a wavelet based technique to detect the corrupted pixels and then using a simple linear interpolation technique estimates the responses of the bad pixels. The second sinogram based correction method performs all the filtering operations in the transform domain, i.e., in the wavelet and Fourier domain. On the other hand, the two post-processing based correction techniques actually operate on the polar transform domain of the reconstructed CT images. The first method extracts the ring artifact template vector using a homogeneity test and then corrects the CT images by subtracting the artifact template vector from the uncorrected images. The second post-processing based correction technique performs median and mean filtering on the reconstructed images to produce the corrected images.

Results

The performances of the comparing algorithms have been tested by using both quantitative and perceptual measures. For quantitative analysis, two different numerical performance indices are chosen. On the other hand, different types of artifact patterns, e.g., single/band ring, artifacts from defective and mis-calibrated detector elements, rings in highly structural object and also in hard object, rings from different flat-panel detectors are analyzed to perceptually investigate the strength and weakness of the five methods. An investigation has been also carried out to compare the efficacy of these algorithms in correcting the volume images from a cone beam CT with the parameters determined from one particular slice. Finally, the capability of each correction technique in retaining the image information (e.g., small object at the iso-center) accurately in the corrected CT image has been also tested.

Conclusions

The results show that the performances of the algorithms are limited and none is fully suitable for correcting different types of ring artifacts without introducing processing distortion to the image structure. To achieve the diagnostic quality of the corrected slices a combination of the two approaches (sinogram- and post-processing) can be used. Also the comparing methods are not suitable for correcting the volume images from a cone beam flat-panel detector based CT.     

Image Corruption Detection in Diffusion Tensor Imaging for Post-Processing and Real-Time Monitoring

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called “corrected Inter-Slice Intensity Discontinuity” (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.     

Relevance of motion artifact in electromyography recordings during vibration treatment

Electromyography readings (EMGs) from quadriceps of fifteen subjects were recorded during whole body vibration treatment at different frequencies (10–50 Hz). Additional electrodes were placed on the patella to monitor the occurrence of motion artifact, triaxial accelerometers were placed onto quadriceps to monitor motion. Signal spectra revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with the accelerometer data. EMG total power was compared to that associated with vibration harmonics narrow bands, before and during vibration. On average, vibration associated power resulted in only 3% (±0.9%) of the total power prior to vibration and 29% (±13.4%) during vibration. Often, studies employ surface EMG to quantitatively evaluate vibration evoked muscular activity and to set stimulation frequency. However, previous research has not accounted for motion artifacts. The data presented in this study emphasize the need for the removal of motion artifacts, as they consistently affect RMS estimation, which is often used as a concise muscle activity index during vibrations. Such artifacts, rather unpredictable in amplitude, might be the cause of large inter-study differences and must be eliminated before analysis. Motion artifact filtering will contribute to thorough and precise interpretation of neuromuscular response to vibration treatment.     

Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

Quantifying near metal visibility using dual energy computed tomography and iterative metal artifact reduction in a fracture phantom

PurposeTo quantitatively assess CT image quality and fracture visibility using virtual monochromatic imaging and iterative metal artifact reduction (iMAR) in a femoral bone fracture phantom with different fixation implants.MethodsA custom made phantom was scanned at 120-kVp and 140-kVp single-energy and 100/150-kVp dual-energy. Three stainless steel and two titanium implants with different thicknesses were placed on the phantom containing simulated one and two mm fractures. Single-energy CT images were reconstructed with and without iMAR, while DECT images were reconstructed at monochromatic energies between 70 and 190 keV. Non-metal scans were used as a reference. A Fourier power spectrum method and fracture model were used to analyze several anatomical areas.ResultsCT-value deviations of titanium implants were much lower compared to stainless steel implants. These deviations decreased for both DECT and iMAR. Fracture visibility, measured with the fracture model, improved the most when DECT was used while artifact reduction benefitted more from iMAR. The optimal monochromatic energy for metal artifact reduction, based on CT-value deviation, varied for each metal between 130 and 150 keV. The fracture model provided a signal-to-noise ratio for the near metal fracture visibility, providing the optimal keV.ConclusioniMAR and high keV monochromatic images extracted from DECT both reduce metal artifacts caused by different metal fixation implants. Quantitative femoral phantom results show that DECT is superior to iMAR regarding fracture visualization adjacent to metal fixation implants. The introduction of new artifacts when using iMAR impedes its value in near metal fixation implant imaging.     

An empirical artifact correction for proton computed tomography

Purpose: To reduce image artifacts of proton computed tomography (pCT) from a preclinical scanner, for imaging of the relative stopping power (RSP) needed for particle therapy treatment planning using a simple empirical artifact correction method.Methods: We adapted and employed a correction method previously used for beam-hardening correction in x-ray CT which makes use of a single scan of a custom-built homogeneous phantom with known RSP. Exploiting the linearity of the filtered backprojection operation, a function was found which corrects water-equivalent path lengths (RSP line integrals) in experimental scans using a prototype pCT scanner. The correction function was applied to projection values of subsequent scans of a homogeneous water phantom, a sensitometric phantom with various inserts and an anthropomorphic head phantom. Data were acquired at two different incident proton energies to test the robustness of the method.Results: Inaccuracies in the detection process caused an offset and known ring artifacts in the water phantom which were considerably reduced using the proposed method. The mean absolute percentage error (MAPE) of mean RSP values of all inserts of the sensitometric phantom and the water phantom was reduced from 0.87% to 0.44% and from 0.86% to 0.48% for the two incident energies respectively. In the head phantom a clear reduction of artifacts was observed.Conclusions: Image artifacts of experimental pCT scans with a prototype scanner could substantially be reduced both in homogeneous, heterogeneous and anthropomorphic phantoms. RSP accuracy was also improved.     

Phantom based qualitative and quantitative evaluation of artifacts in MR images of metallic hip prostheses

PurposeTo develop methods for qualitative and quantitative evaluation of MRI artifacts near metallic prostheses, and to compare the efficiency of different artifact suppression techniques with different types of hip prostheses.MethodsThree hip prostheses of cobalt-chromium, stainless steel, and titanium were embedded in agarose gel together with a rectilinear grid. Coronal MR images of the prostheses were acquired on a 1.5T scanner. Three pulse sequences were evaluated; TSE: a high-bandwidth turbo spin echo; VAT: TSE with view angle tilting, SEMAC: TSE with both VAT and slice distortion correction (6, 10 or 16 z-phase-encoding steps). Through-plane distortions were assessed as the length of visible gridlines, in-plane artifacts as the artifact area, and total artifacts by subtraction of an ideal, undistorted image from the actual image.ResultsVAT reduced in-plane artifacts by up to 50% compared to TSE, but did not reduce through-plane artifacts. SEMAC reduced through-plane artifacts by 60–80% compared to TSE and VAT. SEMAC in-plane artifacts were from 20% higher (6 encoding steps) to 50% lower (16 steps) than VAT. Total artifacts were reduced by 60–80% in the best sequence (SEMAC, 16 steps) compared to the worst (TSE). The titanium prosthesis produced 3–4 times lower artifact scores than the other prostheses.ConclusionsA rectilinear grid phantom is useful for qualitative and quantitative evaluation of artifacts provoked by different MRI protocols and prosthesis models. VAT and SEMAC were superior to TSE with high bandwidth. A proper number of z-encoding steps in SEMAC was critical. The titanium prosthesis caused least artifacts.