一种基于全局字典学习的小动物低剂量计算机断层扫描降噪方法

在小动物计算机断层扫描(CT)实验中,因需考虑小动物存活率以及实验的连续性等问题,一般较少采用高剂量的X射线进行实验;而低剂量的X射线会导致重建图像被噪声污染,影响图像质量,不利于后续实验分析。为解决此问题,本文介绍了一种基于全局字典学习的降噪方法,并将其应用于提升低剂量小动物CT重建图像质量的研究中。针对真实的小动物CT重建数据,选择高剂量的小动物CT重建图像作为训练样本,利用逐列更新的字典学习算法(K-SVD),构建包含图像信息的全局字典;利用正交匹配追踪算法(OMP)将低剂量重建图像利用全局字典进行稀疏分解,分离噪声,最后将重建图像复原,达到降噪、提升图像质量、降低小动物CT实验的拍摄剂量、提高小动物存活率的目的。实验结果表明,本文提出的方法能够有效减少低剂量动物CT图像的噪声,并能够较好地保留图像细节。     

基于全卷积神经网络的低剂量CT去噪算法

针对低剂量医学CT图像因减少辐射剂量而引入大量噪声，导致图像质量显著下降的问题，提出一种基于残差注意力机制和自适应特征融合的低剂量CT图像去噪算法，该算法使用全卷积神经网络来完成低剂量CT图像去噪。在网络框架中引入残差注意力机制和选择性内核特征融合模块，以过滤噪声信息，提取有效特征并自适应地融合图像特征，避免重建过程中的细节损失，提高图像质量，使去噪后的图像在感知上更接近原始图像。定性和定量实验表明，与现有的算法相比，在真实的临床数据集上，所提出的算法能够有效地抑制噪声，并恢复低剂量CT图像中更详细的纹理。与低剂量CT图像相比，所提出的算法将峰值信噪比提高14.94%，结构相似度提高4.68%，均方根误差降低40.11%，可以满足医学成像的诊断要求。     

扇形束CT局部成像的Lambda优质重建

在CT扫描过程中,由于电离辐射作用会导致病人产生癌症或者基因损伤,所以发展低剂量的成像算法正成为热点研究。作为局部成像算法之一的Lambda成像技术,具有减少剂量与快速成像的优点,但由于奇异值的必然存在,从而降低了成像质量。基于Wang Ge局部成像算法框架,提出利用高斯核函数及卷积性质来构建新的Lambda精确成像技术,并对高斯函数的参数选择进行分析。实验结果表明,应用新算法可确保得到高质量的重建图像,其特点是消除了图像中奇异点处微分不存在的现象,使得重建图像更加连续和光滑,尤其是在重建结果中加入了抑制灰度值平均的调节项,使得图像的分辨率和对比度大大提高,更加有利于临床的诊断。     

低剂量迭代重建技术在放疗定位图像中的应用

目的:探讨新型低剂量迭代重建技术应用于放疗定位图像的可行性。方法:基于体模的实验数据,对CT辐射剂量进行分析。对CT值、低对比度分辨率、噪声、均匀性以及几何畸变各项质量评价参数进行定量的分析。对仿真体模进行迭代重建技术扫描重建,并在放射治疗计划系统中对仿真体模进行模拟剂量计算,分析感兴趣体积的绝对剂量和平面内剂量的Gamma通过率。结果:低剂量迭代重建技术能够在保证图像质量的同时减少约60%的CT扫描辐射剂量。当管电压保持不变时,低剂量迭代重建技术对TPS剂量计算的准确性的影响可以忽略不计,感兴趣体积剂量最大差异0.6%,面剂量的Gamma通过率优于99.82%。低剂量迭代重建技术对图像低对比度分辨率有一定影响,需要进一步结合临床影像进行分析。结论:低剂量迭代重建技术可以应用于放疗定位图像中,但是需要注意图像特性和某些图像质量的改变,建议与PET-CT、超声、核磁等检查手段结合综合考虑确定靶区范围。 【关键词】低剂量迭代重建;放射治疗;定位图像     

CT图像常见伪影分析和处理

CT目前已广泛应用于临床医疗,CT图像工作流程大致分为扫描、采集、重建运算及图像显示,这些流程正常与否直接影响正确图像的产生,不准确、不真实的影像,不能真实反映断层影像,会导致医生无法诊断或误诊,甚至导致医疗事故.     

治疗计划系统中CT值及图像质量保证的若干问题讨论

目的：了解CT扫描时影响CT值的相关务件；讨论不同扫描参数扫描得到的CT重建图像可能产生的误差；对三维治疗计划系统使用的CT值及图像的质量保证的若干问题进行探讨。材料和方法：对Siemens Somatom Plus4螺旋CT设置不同扫描电压和电流，测量Gammex的RMI467体模中不同材料的密度值与CT值关系。测量CT孔径位置以及高密度材料治疗床板可能对CT值产生的影响。测量包含不同浓度造影剂的水溶液的CT值及相应的等效密度，初步对鼻咽癌病人加入造影剂前后的CT图像相对剂量计算的影响进行了估计。扫描一包含空腔的有机玻璃板，改变扫描层厚和螺距，对比ADAC三维治疗计划系统重建的CT图像与真实形态的差别。此外对使用的数字化仪以及打印设备进行了图像输入和输出的检查，检查了系统的文字和图像信息的登记和输出。结果：给出了不同电压和电流下CT值与密度的关系：对造影剂、CT治疗床等相关因素对CT值的影响给出了定量讨论；对有机玻璃模体的扫描结果表明：由于部分容积效应和阶梯状伪影的影响，CT图像中显示的空腔形态发生了改变。其他对治疗计划系统非剂量学质量保证措施也在计划系统使用前进行了检查。结论：对于三维治疗计划系统，正确输入密度—CT值是保证剂量计算正确的条件之一；了解CT扫描时可能带来的图像误差可以有效减小靶区勾画的误差；三维治疗计划系统使用前必须有详细的质量保证。     

肝脏低剂量计算机断层灌注的研究进展

肝脏计算机断层(CT)灌注成像是一种定量的无创性检测肝脏功能的影像学检查技术,主要用于肝脏肿瘤的诊断、鉴别诊断以及肝脏慢性疾病状态下的肝脏功能评价。过去,肝脏CT灌注由于辐射剂量高而限制了其在临床的应用。近年来,低剂量CT技术的开发和应用已经可以在保证图像质量的前提下大辐度降低患者的辐射剂量。本文将从X射线源、重建算法以及设备改进及扫描参数优化三个方面介绍低剂量CT灌注的研究进展。目前肝脏低剂量CT灌注的研究较少,仍有许多问题需要解决,但其在临床的应用前景将十分令人期许。     

低剂量CT心肌灌注鲁棒去卷积参数成像方法

针对低剂量CT心肌灌注成像,提出一种鲁棒的去卷积心肌血流参数估计方法。新方法利用CT心肌灌注序列图像中蕴含的血流信号相关性与冗余性,引入一种低秩稀疏正则化约束,以保证去卷积求解模型的鲁棒性。XCAT低剂量CT心肌灌注仿真实验结果表明,新方法相比现有的方法,对心肌灌注参数图像中的噪声与伪影有良好的抑制效果,同时较好地保持了心肌缺血诊断信息,可为临床心肌缺血诊断提供更优质的影像参考。     

金标伪影对射波刀剂量计算及分布的影响

目的:分析金标伪影对射波刀剂量计算及分布的影响。方法:采用能谱CT的GSI扫描技术和MARS重建技术获取Lucy模体的原始CT图像和去除金标伪影后的CT图像,利用射波刀Multiplan?划系统对两组CT图像进行等中心计划设计计算,分析金标伪影对剂量计算及分布的影响。结果:金标伪影使CT图像伪影区域的CT值发生改变,最大可达63.22%,金标伪影低估了金标周围（伪影区域）正常重要组织的最大剂量值,高估了金标周围（伪影区域）正常重要组织的最小剂量值,高估了PTV的剂量覆盖率。结论:使用能谱CT可降低金标伪影对射波刀剂量计算及分布的影响。 【关键词】射波刀;金标伪影;剂量计算;剂量分布     

64层CT造影断层图像虚拟技术和CT仿真结肠镜的临床应用

背景：虚拟内窥镜能够充分显示结肠的解剖形态以及病变部位,并从狭窄、梗阻处两端观察肠腔的解剖和病变。结合三维图像还可了解肠壁以及腔外的情况,更有利于肿瘤的定性以及分期诊断。 目的：探讨64层CT造影断层图像虚拟技术和64层螺旋CT三维成像与仿真内镜在结肠肿瘤诊断中的应用价值。 方法：应用Philips/Brilliance 64 CT对10例术后病理标本证实的结肠癌(8例)和结肠息肉(2例)进行容积扫描。Mimics软件用Marching Cubes算法对肠管进行面绘制及用虚拟内镜法重建三维图像及基于CT造影二维图像对大肠及周围结构等各种组织进行三维重建,并与Brilliance workspace工作站三维成像和仿真内镜结果相比较。 结果与结论：10例三维成像效果良好,虚拟内镜与CT仿真内镜显示基本一致。虚拟结肠镜结合多结构数字模型重建可以提供更多信息,有助于病变的准确定位,能准确反映其复杂的解剖结构及空间毗邻关系。说明64层CT造影断层图像虚拟技术能达到与CT仿真内镜结合三维成像同样的敏感性和特异性,加上各种组织三维重建技术可以提供较仿真内镜更丰富的信息。     

Technical note: Sinogram merging to compensate for truncation of projection data in tomotherapy imaging

An advantage of helical tomotherapy radiation therapy systems is that on-line megavoltage computed tomography (CT) images can be reconstructed to verify patient positioning. One limitation of such systems is that the field-of-view (FOV) of the photon fan-beam is limited by the aperture size of the binary multileaf collimator (MLC) used to modulate treatment beams. For patients larger than the FOV the acquired sinograms will be truncated causing artifacts in the resultant megavoltage CT images. Computer simulations are used to demonstrate that such artifacts can be eliminated or at least reduced by merging appropriately acquired truncated fan-beam sinograms to form a nontruncated parallel-beam sinogram. The necessary fan-beam sinograms are acquired with the patient translated to different offset locations within the gantry. The parallel-beam sinogram is then used to reconstruct the final CT image. The increase in patient dose due to the acquisition of more than one fan-beam sinogram can be reduced by using properly designed binary MLC fields to block redundant projection rays.     

Fast reconstruction with uniform noise properties in halfscan computed tomography

The hybrid algorithms developed recently for the reconstruction of fan-beam images possess computational and noise properties superior to those of the fan-beam filtered backprojection (FFBP) algorithm. However, the hybrid algorithms cannot be applied directly to a halfscan fan-beam sinogram because they require knowledge of a fullscan fan-beam sinogram. In this work, we developed halfscan-hybrid algorithms for image reconstruction in halfscan computed tomography (CT). Numerical evaluation indicates that the proposed halfscan-hybrid algorithms are computationally more efficient than are the widely used halfscan-FFBP algorithms. Also, the results of quantitative studies demonstrated clearly that the noise levels in images reconstructed by use of the halfscan-hybrid algorithm are generally lower and spatially more uniform than are those in images reconstructed by use of the halfscan-FFBP algorithm. Such reduced and uniform image noise levels may be translated into improvement of the accuracy and precision of lesion detection and parameter estimation in noisy CT images without increasing the radiation dose to the patient. Therefore, the halfscan-hybrid algorithms may have significant implication for image reconstruction in conventional and helical CT.     

Dose reduction for kilovotage cone-beam computed tomography in radiation therapy

Kilovotage cone-beam computed tomography (kV-CBCT) has shown potentials to improve the accuracy of a patient setup in radiotherapy. However, daily and repeated use of CBCT will deliver high extra radiation doses to patients. One way to reduce the patient dose is to lower mAs when acquiring projection data. This, however, degrades the quality of low mAs CBCT images dramatically due to excessive noises. In this work, we aim to improve the CBCT image quality from low mAs scans. Based on the measured noise properties of the sinogram, a penalized weighted least-squares (PWLS) objective function was constructed, and the ideal sinogram was then estimated by minimizing the PWLS objection function. To preserve edge information in the projection data, an anisotropic penalty term was designed using the intensity difference between neighboring pixels. The effectiveness of the presented algorithm was demonstrated by two experimental phantom studies. Noise in the reconstructed CBCT image acquired with a low mAs protocol was greatly suppressed after the proposed sinogram domain image processing, without noticeable sacrifice of the spatial resolution.     

低剂量与常规剂量CT扫描在头颅检查中的对比研究

目的对比分析低剂量与常规剂量头颅CT扫描的辐射剂量比以及低剂量扫描对图像质量的影响。方法对120例头颅患者作头颅CT扫描,分为220mA、100mA、80mA3种剂量扫描,评价3种剂量扫描的图像质量;分别记录3种剂量扫描CT剂量容积(CTDIvol)和剂量长度乘积DLP(mGy.cm)。结果与常规剂量220mA扫描比较,100mA扫描对头颅图像的观察诊断没有影响;80mA扫描时图像颗粒增粗,组织结构分辨率下降,影响观察诊断以220mA为标准,低剂量组80mA和100mA的CTDIvoI、DLP分别是常规剂量组的36.7%、36.7%和45.9%、45.9%。结论100mA低剂量CT扫描对头颅细微显示没有明显影响,不影响观察诊断,完全可应用于头颅扫描方法。     

Ultra-low dose CT attenuation correction for PET/CT

A challenge for positron emission tomography/computed tomography (PET/CT) quantitation is patient respiratory motion, which can cause an underestimation of lesion activity uptake and an overestimation of lesion volume. Several respiratory motion correction methods benefit from longer duration CT scans that are phase matched with PET scans. However, even with the currently available, lowest dose CT techniques, extended duration cine CT scans impart a substantially high radiation dose. This study evaluates methods designed to reduce CT radiation dose in PET/CT scanning. We investigated selected combinations of dose reduced acquisition and noise suppression methods that take advantage of the reduced requirement of CT for PET attenuation correction (AC). These include reducing CT tube current, optimizing CT tube voltage, adding filtration, CT sinogram smoothing and clipping. We explored the impact of these methods on PET quantitation via simulations on different digital phantoms. CT tube current can be reduced much lower for AC than that in low dose CT protocols. Spectra that are higher energy and narrower are generally more dose efficient with respect to PET image quality. Sinogram smoothing could be used to compensate for the increased noise and artifacts at radiation dose reduced CT images, which allows for a further reduction of CT dose with no penalty for PET image quantitation. When CT is not used for diagnostic and anatomical localization purposes, we showed that ultra-low dose CT for PET/CT is feasible. The significant dose reduction strategies proposed here could enable respiratory motion compensation methods that require extended duration CT scans and reduce radiation exposure in general for all PET/CT imaging.     

基于MRI的合成CT生成方法综述

放射治疗是治疗肿瘤的重要手段之一,而CT是目前放射治疗的主要基准影像。与CT相比,MRI具有良好的软组织对比度,且对人体完全无害,近年来也越来越会多地被用于软组织的勾画以及引导放射治疗。由于CT扫描的电离辐射影响患者的健康,同时扫描CT和MRI加重患者的经济负担,CT和MRI的配准融合会引入系统误差等弊端,所以仅使用MRI的放射治疗受到研究者的广泛关注。然而,由于MRI与电子密度无联系,不能直接用于剂量计算以及基于X光的患者摆位验证,因此需要研究相关算法,根据MRI图像得到患者组织的电子密度信息或者HU值,即生成合成CT或伪CT。将生成合成CT或伪CT的方法分为3类:基于体素、基于图谱和混合的方法。根据所应用的方法,对序列图像、应用数量以及解剖部位等进行总结和分析。该研究是放射治疗中一个崭新的方向,既可避免使用传统CT带来的电离辐射,又可以应用高分辨率的MRI影像对肿瘤进行监测,使患者得到更精确的放射治疗。     

基于深度学习的低剂量CT图像去噪

目的:提出一种基于深度学习的方法用于低剂量CT（LDCT）图像的噪声去除。方法:首先进行滤波反投影重建,然后利用多尺度并行残差U-net（MPR U-net）的深度学习模型对重建后的LDCT图像进行去噪。实验数据采用LoDoPaB-CT挑战赛的医学CT数据集,其中训练集35 820张图像,验证集3 522张图像,测试集3 553张图像,并采用峰值信噪比（PSNR）与结构相似性系数（SSIM）来评估模型的去噪效果。结果:LDCT图像处理前后PSNR分别为28.80、38.22 dB,SSIM分别为0.786、0.966,平均处理时间为0.03 s。结论:MPR U-net深度学习模型能较好地去除LDCT图像噪声,提升PSNR,保留更多图像细节。     

Tomographic motion detection and correction directly in sinogram space

Patient motion, especially respiratory motion, results in various artefacts such as blurring and streaks in tomographic images. The interplay of the movement of the beam aperture and variations of organ anatomy during delivery can create 'hot' and 'cold' spots throughout the field in intensity-modulated radiation therapy (IMRT). Detection and correction of patient motion is extremely important in tomographic imaging and IMRT. Tomographic projection data (sinogram) encode not only the patient anatomy information, but also the intra-scanning motion information. In this paper, we developed an algorithm to detect and correct the in-plane respiratory motion directly in sinogram space. The respiratory motion is modelled as time-varying scaling along the x and y directions. Its effects on the sinogram are discussed. Based on the traces of some nodal points in the sinogram, the intra-scanning motion is determined. The motion correction is also implemented in sinogram space. The motion-corrected sinogram is used for reconstruction by the filtered back-projection (FBP) method. Computer simulations validate the motion detection and correction algorithm. The reconstructed images from the motion-corrected sinogram eliminate the majority of the artefacts. The method could be applied to projection data used in CT and ECT, as well as in tomotherapy delivery modification and dose reconstruction.