宫颈癌患者锥形束CT影像剂量计算的算法修正

目的 利用修正算法对宫颈癌患者锥形束CT（CBCT）影像进行修正,探讨CBCT影像剂量计算的准确性。方法 采用CIRS-062电子密度模体分别在Brilliance CT Big Bore 4D-CT模拟定位机及Truebeam加速器机载CBCT上执行CT扫描,获得计划CT（pCT）和CBCT的CT值-相对电子密度曲线。采用直方图匹配算法对CBCT影像的CT值进行修正,得到修正后的CBCT（mCBCT）。将25例宫颈癌患者的调强放疗计划分别移植到模体和患者的pCT、CBCT和mCBCT上进行剂量计算,比较其绝对剂量和剂量分布的差异。结果 模体等中心处,CBCT计算的绝对剂量与pCT计算的绝对剂量偏差为0.87%±0.24%,mCBCT与pCT的偏差为0.05%±0.03%,差异有统计学意义（t=3.625,P<0.05）。患者治疗等中心处,CBCT计算的绝对剂量与pCT计算的绝对剂量偏差为1.05%±0.32%,mCBCT与pCT的偏差为0.18%±0.09%,差异有统计学意义（t=3.023,P<0.05）。靶区剂量分布的剂量体积图显示,mCBCT的剂量分布和pCT的剂量分布相似,而CBCT的剂量分布和pCT的剂量分布差异较明显。结论 CBCT影像经算法修正后,可用于宫颈癌放疗中的剂量计算,并能提高剂量计算的准确性。     

Morphometric analysis of the C57BL/6J mouse brain

Magnetic resonance microscopy (MRM), when used in conjunction with active staining, can produce high-resolution, high-contrast images of the mouse brain. Using MRM, we imaged in situ the fixed, actively stained brains of C57BL/6J mice in order to characterize the neuroanatomical phenotype and produce a digital atlas. The brains were scanned within the cranium vault to preserve the brain morphology, avoid distortions, and to allow an unbiased shape analysis. The high-resolution imaging used a T1-weighted scan at 21.5 microm isotropic resolution, and an eight-echo multi-echo scan, post-processed to obtain an enhanced T2 image at 43 microm resolution. The two image sets were used to segment the brain into 33 anatomical structures. Volume, area, and shape characteristics were extracted for all segmented brain structures. We also analyzed the variability of volumes, areas, and shape characteristics. The coefficient of variation of volume had an average value of 7.0%. Average anatomical images of the brain for both the T1-weighted and T2 images were generated, together with an average shape atlas, and a probabilistic atlas for 33 major structures. These atlases, with their associated meta-data, will serve as baseline for identifying neuroanatomical phenotypes of additional strains, and mouse models now under study. Our efforts were directed toward creating a baseline for comparison with other mouse strains and models of neurodegenerative diseases.     

原发性脑淋巴瘤的CT和MRI观察

目的:探讨原发性脑淋巴瘤的CT、MRI表现。材料与方法:回顾性分析25例未经治疗的原发性脑淋巴瘤的临床、病理及CT、MRI表现。结果:25例患者共计35个病灶,其中16例患者(19个病灶)同时作MRI检查,所有病灶CT平扫为等或稍高密度,T1WI为等、低信号,其中5个病灶伴局灶性高信号;12个病灶在T2WI上为等、低信号;14个病灶DWI为等、高信号。所有病灶均有增强,18个病灶为均匀增强。13例病理检查,病灶表现为瘤细胞密集、高核浆比,病灶内出血坏死少见。结论:免疫功能正常状态原发性脑淋巴瘤的CT、MR表现有一定特征性,但鉴别诊断仍需仔细谨慎。     

基于循环生成对抗网络迁移修正鼻咽癌兆伏级CT图像

目的 观察循环生成对抗网络(CycleGAN)用于对鼻咽癌兆伏级CT(MVCT)图像进行迁移修正的价值。方法 纳入101例于自适应放射治疗前接受计划CT(pCT)及MVCT扫描的鼻咽癌患者，随机将其分为训练集(n=80)和测试集(n=21)。基于CycleGAN对训练集图像进行训练并生成模型，再以模型对测试集MVCT进行迁移修正而生成伪CT(sCT);对pCT进行重采样，使其体素及尺寸与MVCT一致，获得重采样CT(RCT)。勾画肿瘤靶区(GTV)和危及器官并计算剂量，比较MVCT与RCT、sCT与RCT图像的CT值平均绝对误差(MAE)和剂量分布。结果 测试集MVCT与RCT图像之间、sCT与RCT图像之间CT值的MAE分别为132.67(121.84,138.28)HU及76.77(62.71,86.43)HU,差异有统计学意义(Z=-5.466,P<0.001)。除颈部右侧转移淋巴结(GTV-NR-P)D₉₅和左晶状体D_max外，sCT与RCT图像其余剂量参数差异均无统计学意义(P均>0.05)。结论 以CycleGAN修正...     

ABCnet: Adversarial bias correction network for infant brain MR images

Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The “ground-truth” bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods.     

SLIR: Synthesis,localization, inpainting,and registration for image-guided thermal ablation of liver tumors

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed.     

Clinical Value of Image Fusion from MR and PET in Patients with Head and Neck Cancer

Purpose  This study aimed to evaluate the clinical value of image fusion from magnetic resonance (MR) combined with positron emission tomography (PET) imaging, using 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) in head and neck cancer. Methods  Sixty-five consecutive patients underwent MR and FDG-PET scans before or after the treatment of known or suspected head and neck cancer. T1-weighted and T2-weighted images were first assessed by MR interpretation, and then, the fused images of T2-weighted images from MR and PET were evaluated in a blind manner. Diagnostic performance was compared. Procedures  For initial staging, in 48 patients, malignant tumors were histologically confirmed in 45 patients. The interpretation sensitivities of MR alone and fused images for primary tumors were 98% and 100%, respectively. For lymph node metastasis, the sensitivity and specificity of both methods were 85% and 92%, respectively. Of 15 patients with suspected recurrence, ten patients had recurrent tumors, three patients developed second malignant tumors, and two patients had no recurrence. For these patients, the overall sensitivity of MR alone was 67%, whereas that of the fused images was 92%. Eight additional lesions were accurately diagnosed by image fusion only. In two patients with lymph node metastasis from unknown origin, the primary site was not detected in one patient, while tonsilar cancer was identified only by image fusion interpretation. Conclusion  Image fusion from MR with PET might be useful in evaluating head and neck cancer, especially in suspected recurrent cases rather than in fresh cases.     

Template images for nonhuman primate neuroimaging: 1. Baboon

Coregistration of functional brain images across many subjects offers several experimental advantages and is widely used for studies in humans. Voxel-based coregistration methods require a high-quality 3-D template image, preferably one that corresponds to a published atlas. Template images are available for human, but we could not find an appropriate template for neuroimaging studies in baboon. Here we describe the formation of a T1-weighted structural MR template image and a PET blood flow template, derived from 9 and 7 baboons, respectively. Custom software aligns individual MR images to the MRI template; human supervision is needed only to initially estimate any gross rotational misalignment. In these aligned individual images, internal subcortical fiducial points correspond closely to a photomicrographic baboon atlas with an average error of 1.53 mm. Cortical test points showed a mean error of 1.99 mm compared to the mean location for each point. Alignment of individual PET blood flow images directly to the PET template was compared to a two-step alignment process via each subject's MR image. The two transformations were identical within 0.41 mm, 0.54 degrees, and 1.0% (translation, rotation, and linear stretch; mean). These quantities provide a check on the validity of the alignment software as well as of the template images. The baboon structural MR and blood flow PET templates are available on the Internet (purl.org/net/kbmd/b2k) and can be used as targets for any image registration software.     

Neonatal brain image segmentation in longitudinal MRI studies

In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.     

Expression of vascular endothelial growth factor in hepatocellular carcinoma and the surrounding liver: correlation with MR imaging and angiographically assisted CT

We summarize and discuss our previous research results on the correlation between findings on magnetic resonance (MR) imaging and angiographically assisted computed tomography (CT) and the intensity of vascular endothelial growth factor (VEGF) expression in hepatocellular carcinoma (HCC) and in the surrounding nontumorous liver. MR images (n = 22), CT during arterial portography (n = 20), and CT hepatic arteriography (n = 17) were retrospectively correlated quantitatively and qualitatively with VEGF expression in HCCs and in the surrounding liver assessed by western blotting. HCC-to-liver contrast-to-noise ratio correlated with VEGF expression index (VEGF^IND) values of HCCs inversely on opposed-phase, T1-weighted, spoiled gradient recalled-echo (GRE) images, directly on T2-weighted, fast spin-echo images, and marginally and inversely on gadolinium-enhanced hepatic arterial-phase GRE images. On T2-weighted fast spin-echo images, standard deviation ratio of HCCs correlated directly with VEGF^IND values of HCCs. By CT hepatic arteriography, the contrast-enhancement index of HCCs showed a moderate inverse correlation with VEGF^IND values of HCCs, and the contrast-enhancement index of the liver showed marginal, moderate direct correlation with VEGF^IND values in the liver. Heterogeneities of HCCs on images correlated directly with VEGF^IND values of HCCs on opposed-phase T1-weighted GRE images, T2-weighted fast spin-echo images, hepatic arterial-phase GRE images, equilibrium-phase GRE images, and CT hepatic arteriogram. Our results may reflect that MR signal intensity, hepatic arterial vascularity, and heterogeneity of HCCs on CT or MR images are closely related to the intensity of VEGF expression in HCC as upregulated by hyper- or hypoxia in HCCs. Although the real effects of our results on radiologic practice are debatable at this moment, we believe that our results may help future radiologic practice in conjunction with biomolecular or genetic treatment for HCCs.     

Automated separation of gray and white matter from MR images of the human brain

A simple automatic procedure for segmentation of gray and white matter in high resolution 1.5T T1-weighted MR human brain images was developed and validated. The algorithm is based on histogram shape analysis of MR images that were corrected for scanner nonuniformity. Calibration and validation was done on a set of 80 MR images of human brains. The automatic method's values for the gray and white matter volumes were compared with the values from thresholds set twice by the best three of six raters. The automatic procedure was shown to perform as good as the best rater, where the average result of the best three raters was taken as reference. The method was also compared with two other histogram-based threshold methods, which yielded comparable results. The conclusion of the study thus is that automated threshold based methods can separate gray and white matter from MR brain images as reliably as human raters using a thresholding procedure.     

Reduction of susceptibility-induced signal losses in multi-gradient-echo images: Application to improved visualization of the subthalamic nucleus

T2-weighted gradient echo (GE) images yield good contrast of iron-rich structures like the subthalamic nuclei due to microscopic susceptibility induced field gradients, providing landmarks for the exact placement of deep brain stimulation electrodes in Parkinson's disease treatment. An additional advantage is the low radio frequency (RF) exposure of GE sequences. However, T2-weighted images are also sensitive to macroscopic field inhomogeneities, resulting in signal losses, in particular in orbitofrontal and temporal brain areas, limiting anatomical information from these areas. In this work, an image correction method for multi-echo GE data based on evaluation of phase information for field gradient mapping is presented and tested in vivo on a 3 Tesla whole body MR scanner. In a first step, theoretical signal losses are calculated from the gradient maps and a pixelwise image intensity correction is performed. In a second step, intensity corrected images acquired at different echo times TE are combined using optimized weighting factors: in areas not affected by macroscopic field inhomogeneities, data acquired at long TE are weighted more strongly to achieve the contrast required. For large field gradients, data acquired at short TE are favored to avoid signal losses. When compared to the original data sets acquired at different TE and the respective intensity corrected data sets, the resulting combined data sets feature reduced signal losses in areas with major field gradients, while intensity profiles and a contrast-to-noise (CNR) analysis between subthalamic nucleus, red nucleus and the surrounding white matter demonstrate good contrast in deep brain areas.     

Combining atlas-based parcellation of regional brain data acquired across scanners at 1.5 T and 3.0 T field strengths

Longitudinal brain morphometric studies designed for data acquisition at a single MRI field strength can be seriously limited by system replacements from lower to higher field strength. Merging data across field strengths has not been endorsed for a variety of reasons, yet the ability to combine such data would broaden longitudinal investigations. To determine whether structural T1-weighted MRI data acquired across MR field strengths could be merged, parcellations of archival SPGR data acquired in 114 individuals at 1.5 T and at 3.0 T within 3 weeks of each other were compared. The first set of analyses examined 1) the correspondence between regional tissue volumes derived from data collected at 1.5 T and 3.0 T and 2) whether there were systematic differences for which a correction factor could be determined and applied to improve measurement agreement. Comparability of regional volume determination at 1.5 T and 3.0 T was assessed with intraclass correlation (ICC) computed on volumes derived from the automated and unsupervised SRI24 atlas registration and parcellation method. A second set of analyses measured the reliability of the registration and quantification using the same approach on longitudinal data acquired in 69 healthy adults at a single field strength, 1.5 T, at an interval < 2 years. The mainstay of the analyses was based on the SRI24 method; to examine the potential of merging data across field strengths and across image analysis packages, a secondary set of analyses used FreeSurfer instead of the SRI24 method. For both methods, a regression-based linear correction function significantly improved correspondence. The results indicated high correspondence between most selected cortical, subcortical, and CSF-filled spaces; correspondence was lowest in the globus pallidus, a region rich in iron, which in turn has a considerable field-dependent effect on signal intensity. Thus, the application of a regression-based correction function that improved the correspondence in regional volume estimations argues well for the proposition that selected T1-weighted regional anatomical brain data can be reliably combined across 1.5 T and 3.0 T field strengths with the application of an appropriate correction procedure.     

A brain tumor segmentation framework based on outlier detection

This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is done simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium contrast agent in the T1-weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect abnormal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions. Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement.     

血管外皮细胞瘤与孤立性纤维瘤的诊断和鉴别诊断

目的血管外皮细胞瘤和孤立性纤维瘤同属纤维母细胞/肌纤维母细胞来源肿瘤的中间性肿瘤,病理和组织学方面重叠表现多,术前误诊率高。本文分析各自影像学特征,探讨其影像学诊断和鉴别诊断的价值。方法回顾性分析经手术病理证实的9例血管外皮细胞瘤和8例孤立性纤维瘤病例,术前经螺旋CT或MR检查,仔细复习CT和MRI扫描结果并和手术病理作回顾性对照分析。结果9例血管外皮细胞瘤病灶中,位于颅内3例,四肢4例,腹膜后1例,腹股沟1例;肿瘤直径4.8-13.5 cm,平均6.7 cm;瘤内见蚯蚓和结节状异常信号6例,不同程度坏死9例;所有肿瘤实质成分T1WI为等或略低信号,T2WI为略高信号;其中,6例行动态增强,动脉期显著强化5例,中等程度强化1例,所有病例门脉期强化程度与血管相仿。8例孤立性纤维瘤中,位于胸部4例,四肢2例,腹腔1例,盆腔1例;肿瘤直径4.0-22.5 cm,平均9.3 cm;密度或信号较均匀5例,不同程度坏死3例,伴点状钙化1例。CT平扫密度与肌肉密度相仿,内可见两种不同密度的实质成分;CT动脉期呈地图样或线样葡行强化,门脉期持续强化,强化趋于均匀;MRSE T1WI呈等信号,T2WI为略高信号,T2 WI脂肪抑制为高信号;MR动脉期明显强化,门脉期持续强化。结论血管外皮细胞瘤和孤立性纤维瘤具有较明显的影像学特征,影像学可以诊断和鉴别多数病例。     

DWI-T2WI融合图像判断Ⅰ期子宫内膜癌肌层浸润深度

目的探讨DWI与T2WI融合图像在判定Ⅰ期子宫内膜癌肌层浸润深度中的价值。方法对28例Ⅰ期子宫内膜癌患者行常规MR及DWI检查。采用T2WI和T2WI-DWI融合图像分别测量肌层浸润深度及肌层厚度,计算二者比值,并将该比值与术后病理标本测量的肌层浸润深度进行比较。应用ROC曲线评价两种方法对肿瘤是否浸润肌层及是否浸润深肌层的诊断效能。结果术后病理诊断无肌层浸润3例,浅肌层浸润13例,深肌层浸润12例。对于评估肿瘤是否浸润肌层及深肌层,T2WI-DWI融合图像诊断效能优于T2WI,差异有统计学意义(P<0.05)。结论 MR对术前评估子宫内膜癌肌层浸润深度有一定价值;T2WI-DWI融合图像较常规T2WI判断更准确。     

Normalized mutual information based registration using k-means clustering and shading correction

In this paper the influence of intensity clustering and shading correction on mutual information based image registration is studied. Instead of the generally used equidistant re-binning, we use k-means clustering in order to achieve a more natural binning of the intensity distribution. Secondly, image inhomogeneities occurring notably in MR images can have adverse effects on the registration. We use a shading correction method in order to reduce these effects. The method is validated on clinical MR, CT and PET images, as well as synthetic MR images. It is shown that by employing clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.     

Nodular regenerative hyperplasia of the liver in Budd–Chiari syndrome: CT and MR features

We report the imaging findings of spiral computed tomography (CT), magnetic resonance (MR) imaging, and MR angiography in a patient with nodular regenerative hyperplasia of the liver associated with Budd–Chiari syndrome. Spiral CT showed multiple enhancing nodules during the hepatic arterial and portal venous phases. MR images showed multiple hyperintense nodules on T1-weighted images and hypointense or isointense nodules on T2-weighted images. MR angiography showed thrombotic occlusion of three hepatic veins, suggesting Budd–Chiari syndrome. Received: 25 June 1999/Revision accepted: 22 September 1999     

对比集合序列与常规序列头部MR图像质量

目的 对比分析MRI集合（MAGiC）序列与常规序列头部图像质量。方法 对96人进行头部常规序列及MAGiC序列MR扫描,比较常规序列T1 FSE、T2 FSE、T1 Flair、T2 Flair图像与MAGiC序列重建MAGiC T1、MAGiC T2、MAGiC T1 Flair、MAGiC T2 Flair图像的质量和SNR。结果 常规序列与MAGiC序列图像的整体质量评分、伪影评分、病灶检出评分差异均无统计学意义（P均>0.05）。MAGiC T1、MAGiC T2、MAGiC T1 Flair、MAGiC T2 Flair图像的SNR均高于相应常规序列图像（P均<0.01）。结论 MAGiC序列与常规序列扫描所获头部图像质量相当,且MAGiC序列图像的SNR更高。