Autoantibody to MOG suggests two distinct clinical subtypes of NMOSD

We characterized a unique group of patients with neuromyelitis optica spectrum disorder(NMOSD) who carried autoantibodies of aquaporin-4(AQP4) and myelin-oligodendrocyte glycoprotein(MOG). Among the 125 NMOSD patients, 10(8.0%) were AQP4- and MOG-ab double positive, and 14(11.2%) were MOG-ab single positive. The double-positive patients had a multiphase disease course with a high annual relapse rate(P=0.0431), and severe residual disability(P0.0001). Of the double-positive patients, 70% had MS-like brain lesions, more severe edematous, multifocal regions on spinal magnetic resonance imaging(MRI), pronounced decreases of retinal nerve fiber layer thickness and atrophy of optic nerves. In contrast, patients with only MOG-ab had a higher ratio of monophasic disease course and mild residual disability. Spinal cord MRI illustrated multifocal cord lesions with mild edema, and brain MRIs showed more lesions around lateral ventricles. NMOSD patients carrying both autoantibodies to AQP4 and MOG existed and exhibited combined features of prototypic NMO and relapsing-remitting form of MS, whereas NMOSD with antibodies to MOG only exhibited an "intermediate" phenotype between NMOSD and MS. Our study suggests that antibodies against MOG might be pathogenic in NMOSD patients and that determination of anti-MOG antibodies maybe instructive for management of NMOSD patients.     

Fast and Accurate Semi-Automated Segmentation Method of Spinal Cord MR Images at 3T Applied to the Construction of a Cervical Spinal Cord Template

Objective

To design a fast and accurate semi-automated segmentation method for spinal cord 3T MR images and to construct a template of the cervical spinal cord.

Materials and Methods

A semi-automated double threshold-based method (DTbM) was proposed enabling both cross-sectional and volumetric measures from 3D T₂-weighted turbo spin echo MR scans of the spinal cord at 3T. Eighty-two healthy subjects, 10 patients with amyotrophic lateral sclerosis, 10 with spinal muscular atrophy and 10 with spinal cord injuries were studied. DTbM was compared with active surface method (ASM), threshold-based method (TbM) and manual outlining (ground truth). Accuracy of segmentations was scored visually by a radiologist in cervical and thoracic cord regions. Accuracy was also quantified at the cervical and thoracic levels as well as at C2 vertebral level. To construct a cervical template from healthy subjects’ images (n=59), a standardization pipeline was designed leading to well-centered straight spinal cord images and accurate probability tissue map.

Results

Visual scoring showed better performance for DTbM than for ASM. Mean Dice similarity coefficient (DSC) was 95.71% for DTbM and 90.78% for ASM at the cervical level and 94.27% for DTbM and 89.93% for ASM at the thoracic level. Finally, at C2 vertebral level, mean DSC was 97.98% for DTbM compared with 98.02% for TbM and 96.76% for ASM. DTbM showed similar accuracy compared with TbM, but with the advantage of limited manual interaction.

Conclusion

A semi-automated segmentation method with limited manual intervention was introduced and validated on 3T images, enabling the construction of a cervical spinal cord template.     

Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel,Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.     

Comparison of clinical characteristics between neuromyelitis optica spectrum disorders with and without spinal cord atrophy

Background

Spinal cord lesions is one of the predominant characteristics in patients with neuromyelitis optica spectrum disorders (NMOSD). Interestingly, mounting evidence indicates that spinal cord atrophy (SCA) is one of common clinical features in multiple sclerosis (MS) patients, and correlates closely with the neurological disability. However, Clinical studies related to the SCA aspects of NMOSD are still scarce.

Methods

We retrospectively analyzed 185 patients with NMOSD, including 23 patients with SCA and 162 patients without SCA. Data were collected regarding clinical characteristics, laboratory tests, and magnetic resonance imaging findings.

Results

12.4% of patients had SCA in NMOSD. Patients with SCA had a longer disease duration and higher EDSS at clinical onset and last visit. More importantly, SCA patients were more prone to reach disability milestones (EDSS?≥?6.0). Bowel or bladder dysfunction, movement disorders, and sensory disturbances symptoms were more common in patients with SCA. ESR and CRP were significantly higher in patients with SCA than those without SCA. Patients with SCA were more frequently complicated with cervical cord lesions. However, the ARR, progression index, seropositive rate of NMO-IgG and OCB were similar in the two groups. Futhermore, LETM did not differ significantly between patients with SCA and without SCA in NMOSD patients.

Conclusions

Patients with SCA might have longer disease duration, more severe clinical disability, and more frequently complicated with cervical spinal cord lesions. SCA might be predictive of the more severe neurologic dysfunction and worse prognosis in NMOSD. Inflammation contributes to the development of SCA in NMOSD.

     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

Increasing the Contrast of the Brain MR FLAIR Images Using Fuzzy Membership Functions and Structural Similarity Indices in Order to Segment MS Lesions

Segmentation is an important step for the diagnosis of multiple sclerosis (MS). This paper presents a new approach to the fully automatic segmentation of MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance (MR) images. With the aim of increasing the contrast of the FLAIR MR images with respect to the MS lesions, the proposed method first estimates the fuzzy memberships of brain tissues (i.e., the cerebrospinal fluid (CSF), the normal-appearing brain tissue (NABT), and the lesion). The procedure for determining the fuzzy regions of their member functions is performed by maximizing fuzzy entropy through Genetic Algorithm. Research shows that the intersection points of the obtained membership functions are not accurate enough to segment brain tissues. Then, by extracting the structural similarity (SSIM) indices between the FLAIR MR image and its lesions membership image, a new contrast-enhanced image is created in which MS lesions have high contrast against other tissues. Finally, the new contrast-enhanced image is used to segment MS lesions. To evaluate the result of the proposed method, similarity criteria from all slices from 20 MS patients are calculated and compared with other methods, which include manual segmentation. The volume of segmented lesions is also computed and compared with Gold standard using the Intraclass Correlation Coefficient (ICC) and paired samples t test. Similarity index for the patients with small lesion load, moderate lesion load and large lesion load was 0.7261, 0.7745 and 0.8231, respectively. The average overall similarity index for all patients is 0.7649. The t test result indicates that there is no statistically significant difference between the automatic and manual segmentation. The validated results show that this approach is very promising.     

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network

PurposeWe introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.MethodsWe collected 105 patients’ Computed Tomography (CT) scans that diagnosed locally advanced cervical cancer and treated with radiotherapy in one hospital. Seven organs, including the bladder, bone marrow, left femoral head, right femoral head, rectum, small intestine and spinal cord were defined as OARs. The annotated contours of the OARs previously delineated manually by the patient’s radiotherapy oncologist and confirmed by the professional committee consisted of eight experienced oncologists before the radiotherapy were used as the ground truth masks. A multi-class segmentation model based on U-Net was designed to fulfil the OARs segmentation task. The Dice Similarity Coefficient (DSC) and 95th Hausdorff Distance (HD) are used as quantitative evaluation metrics to evaluate the proposed method.ResultsThe mean DSC values of the proposed method are 0.924, 0.854, 0.906, 0.900, 0.791, 0.833 and 0.827 for the bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord, respectively. The mean HD values are 5.098, 1.993, 1.390, 1.435, 5.949, 5.281 and 3.269 for the above OARs respectively.ConclusionsOur proposed method can help reduce the inter-observer and intra-observer variability of manual OARs delineation and lessen oncologists’ efforts. The experimental results demonstrate that our model outperforms the benchmark U-Net model and the oncologists’ evaluations show that the segmentation results are highly acceptable to be used in radiation therapy planning.     

Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

BackgroundAccurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images.MethodsThe proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour.ResultsExperimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy.ConclusionsThe geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.     

Cervical Spinal Cord Atrophy Profile in Adult SMN1-Linked SMA

PurposeThe mechanisms underlying the topography of motor deficits in spinal muscular atrophy (SMA) remain unknown. We investigated the profile of spinal cord atrophy (SCA) in SMN1-linked SMA, and its correlation with the topography of muscle weakness.ResultsCSA measurements revealed a significant cord atrophy gradient mainly located between C3 and C6 vertebral levels with a SCA rate ranging from 5.4% to 23% in SMA patients compared to controls. RD was significantly lower in SMA patients compared to controls in the anterior-posterior direction with a maximum along C4 and C5 vertebral levels (p-values < 10⁻⁵). There were no correlations between atrophy measurements, strength and disability scores.ConclusionsSpinal cord atrophy in adult SMN1-linked SMA predominates in the segments innervating the proximal muscles. Additional factors such as neuromuscular junction or intrinsic skeletal muscle defects may play a role in more complex mechanisms underlying weakness in these patients.     

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

ObjectivesThe accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.Materials and MethodsTo address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.ResultsWe compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).ConclusionExperimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.     

Stereotactic radiotherapy for spinal hemangioblastoma – disease control and volume analysis in long-term follow up

BackgroundThis retrospective analysis evaluated the long-term outcome of spinal stereotactic body radiotherapy (SBRT) treatment for hemangioblastomas.Materials and methodsBetween 2010 and 2018, 5 patients with 18 Von-Hippel Lindau-related pial-based spinal hemangioblastomas were treated with fractionated SBRT. After precisely registering images of all relevant datasets, we delineated the gross tumor volume, spinal cord (including intramedullary cysts and/or syrinxes), and past radiotherapy regions. A sequential optimization algorithm was used for dose determinations, and patients received 25–26 Gy in five fractions or 24 Gy in three fractions. On-line image guidance, based on spinal bone structures, and two orthogonal radiographs were provided. The actuarial nidus control, surgery-free survival, cyst/syrinx changes, and progression-free survival were calculated with the Kaplan-Meier method. Toxicities were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events v5.0.ResultsThe median follow-up was 5 years after SBRT. Patients displayed one nidus progression, one need of neurosurgery, and two cyst/syrinx progressions directly connected to symptom worsening. No SBRT-related complications or acute adverse radiation-related events occurred. However, one asymptomatic radiological sign of myelopathy occurred two years after SBRT. All tumors regressed; the one-year equivalent tumor volume reduction was 0.2 mL and the median volume significantly decreased by 28% (p = 0.012). Tumor volume reductions were not correlated with the mean (p = 0.19) or maximum (p = 0.16) dose.ConclusionsSBRT for pial-based spinal hemangioblastomas was an effective, safe, viable alternative to neurosurgery in asymptomatic patients. Escalating doses above the conventional dose-volume limits of spinal cord tolerance showed no additional benefit.     

MR 脑图像海马自动分割法在AD 早期诊断中的应用研究

目的:研究磁共振(Magnetic resonance,MR)脑图像中海马的自动分割方法及海马的形态学分析方法,为阿尔茨海默病(Alzheimer’s disease,AD)的早期诊断提供依据。方法:对20例AD患者和60名正常对照者行MRI T1 WI 3D容积扫描,建立海马的三维主动表观模型,并以此模型对每个个体脑部磁共振图像上的海马进行自动识别和三维分割,分别建立正常对照组和AD组的海马统计形状模型,比较AD组与正常对照组间海马形状的差异性。结果:海马三维分割方法与手动分割方法在海马体积测量上无统计学差别(P>0.05);AD患者海马头部发生萎缩(P<0.05)。结论:基于主动表观模型的MR脑图像海马自动识别和三维分割法是准确可靠的;海马头部萎缩可作为AD诊断的依据之一。     

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods.     

脊髓损伤扩散峰度成像参数与脊髓型颈椎病患者神经功能评分的相关性研究

目的:研究扩散峰度成像(DKI)参数与脊髓型颈椎病(CSM)患者神经功能评分的相关性及临床意义。方法:选取2018年12月至2019年6月本院收治的CSM患者37例作为研究组及健康志愿者的30例作为对照组,采用GE3.0磁共振机分别对两组人员行磁共振成像(MRI)及DKI扫描,观察其影像学特征及DKI参数的变化情况,并分析DKI参数值与临床行为评分的相关性。结果:所有研究对象的MRI图像均符合诊断要求。志愿者颈髓形态完整、信号均匀;不同年龄组颈髓平均弥散各向异性分数(FA)值、平均弥散峰度(MK)值比较差异无统计学意义(P>0.05)。根据MRI的T2加权图像上椎管受压程度及脊髓信号改变,将实验组分为A、B、C组,对照组与各实验组的MK值、FA值比较差异有统计学意义(P<0.05)。实验组FA值与mJOA评分呈显著正相关(r=0.34),与NDI评分呈负相关(r=-0.38);MK值与mJOA评分呈正相关(r=0.67),与NDI评分呈负相关(r=-0.46)。结论:DKI序列对CSM诊断具有参考较高价值,其参数与临床行为评分关系密切,能够评估早期CSM患者的脊髓损伤情况,并为诊断和治疗提供参考。     

Validation of 3D spino-pelvic muscle reconstructions based on dedicated MRI sequences for fat-water quantification

ObjectivesTo evaluate a protocol, including MRI acquisition with dedicated sequences for fat-water quantification and semi-automatic segmentation, for 3D geometry measurement and fat infiltration of key muscles of the spino-pelvic complex.Materials and methodsMRI protocol: two axial acquisitions from the thoraco-lumbar region to the patella were obtained: one T1-weighted and one based on the Dixon method, permitted to evaluate the proportion of fat inside each muscle. Muscle reconstruction: with Muscl’X software, 3D reconstructions of 18 muscles or groups of muscles were obtained identifying their contours on a limited number of axial images 3D references were obtained only on T1 acquisitions identifying the contour of the muscles on all axial images. Evaluation: for two volunteers, three operators completed reconstructions three times across three sessions. Each reconstruction was projected on the reference to calculate the ‘point to surface’ error. Mean and maximal axial section, muscle volume, and muscle length calculated from the reconstructions were compared to reference values, and intra- and inter-operator variability for those parameters were evaluated.Results2xRMS ‘point to surface’ error was below 3 mm, on average. The agreement between the two methods was variable between muscles [–4.50; 8.00%] for the mean axial section, the length and the volume. Intra- and inter-operator variability were less than 5% and comparison of variability for the Fat and T1 reconstructions did not reveal any significant differences.DiscussionExcellent inter- and intra-operator reliability was demonstrated for 3D muscular reconstruction using the DPSO method and Dixon images that allowed generation of patient-specific musculoskeletal models.     

Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network

Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm.     

CROP – The Clinico-Radiologico-Ophthalmological Paradox in Multiple Sclerosis: Are Patterns of Retinal and MRI Changes Heterogeneous and Thus Not Predictable?

Background

To date, no direct scientific evidence has been found linking tissue changes in multiple sclerosis (MS) patients, such as demyelination, axonal destruction or gliosis, with either steady progression and/or stepwise accumulation of focal CNS lesions. Tissue changes such as reduction of the retinal nerve fiber layer (RNFL) and the total macular volume (TMV), or brain- and spinal cord atrophy indicates an irreversible stage of tissue destruction. Whether these changes are found in all MS patients, and if there is a correlation with clinical disease state, remains controversial. The objective of our study was to determine, whether there was any correlation between the RNFL or TMV of patients with MS, and: (1) the lesion load along the visual pathways, (2) the ratios and absolute concentrations of metabolites in the normal-appearing white matter (NAWM), (3) standard brain atrophy indices, (4) disease activity or (5) disease duration.

Methods

28 MS patients (RRMS, n = 23; secondary progressive MS (SPMS), n = 5) with moderately-high disease activity or long disease course were included in the study. We utilised: (1) magnetic resonance imaging (MRI) and (2) -spectroscopy (MRS), both operating at 3 Tesla, and (3) high-resolution spectral domain-OCT with locked reference images and eye tracking mode) to undertake the study.

Results

There was no consistency in the pattern of CNS metabolites, brain atrophy indices and the RNFL/TMV between individuals, which ranged from normal to markedly-reduced levels. Furthermore, there was no strict correlation between CNS metabolites, lesions along the visual pathways, atrophy indices, RNFL, TMV, disease duration or disability.

Conclusions

Based on the findings of this study, we recommend that the concept of ‘clinico-radiologico paradox’ in multiple sclerosis be extended to CROP–‘clinico-radiologico-ophthalmological paradox’. Furthermore, OCT data of MS patients should be interpreted with caution.     

以急性脑干综合征为首发表现的NMOSD的临床和MRI诊断分析

目的:探讨以急性脑干综合征(ABS)为首发表现的视神经脊髓炎谱系疾病(NMOSD)的临床和MRI表现,以提高对该病的诊断水平。方法:回顾性分析17例首发表现为ABS的NMOSD患者临床资料,包括脑脊液常规、生化及寡克隆区带,血清水通道蛋白4抗体(AQP4-IgG),头颅与脊髓MRI表现,并分析其特点。结果:共纳入男性3例,女性14例,发病年龄20~43岁,平均发病年龄33.5岁,88.2%患者以恶心、呕吐、顽固性呃逆等胃肠症状就诊,发作病程7天~47周,平均8周。脑脊液检查多呈轻中度炎性反应,2例白细胞计数>50×10^6/L。脑脊液蛋白平均0.32 g/L (0.15~1.17 g/L),OBs检测阳性率为11.8%,血清AQP4-IgG阳性率为76.5%。64.7%病例早期MRI表现延髓背侧中央导水管周围异常信号,无明显强化;脊髓未见受累。结论:中青年女性以ABS为首发症状时应警惕NMOSD的可能,脑脊液检查、血清AQP4抗体阳性以及MRI表现具有一定的特征性,有助于早期诊断。     

Is Time to Reach EDSS 6.0 Faster in Patients with Late-Onset versus Young-Onset Multiple Sclerosis?

MethodsThis cross-sectional cohort study was conducted to identify LOMS and YOMS patients’ with relapsing remitting course at MS diagnosis. Time (years) to reach sustained EDSS 6.0 was compared between LOMS and AOMS patients. Cox proportional hazards model was used to evaluate the demographic and clinical predictors of time to EDSS 6.0 in these cohorts.ResultsLOMS and YOMS cohorts comprised 99 (10.7%) and 804 (89.3%) patients respectively. Spinal cord presentation at MS onset was more common among LOMS patients (46.5% vs. 32.3%). The proportions of LOMS and YOMS patients reaching EDSS 6.0 during the follow-up period were 19.2% and 15.7% respectively. In multivariable Cox proportional hazards model, older age at MS onset (adjusted hazard ratio (aHR) = 3.96; 95% CI: 2.14–7.32; p < 0.001), male gender (aHR = 1.85; 95% CI: 1.22–2.81; p = 0.004) and spinal cord presentation at onset (aHR = 1.47; 95% CI: 0.98–2.21; p = 0.062) were significantly associated with shorter time to EDSS 6.0.ConclusionsLOMS patients attained EDSS 6.0 in a significantly shorter period that was influenced by male gender and spinal cord presentation at MS onset.     

Fast and Accurate Registration of the Proximal Femurs in Bilateral Hip Joint Images by Using the Random Sub-Sample Points

Background and ObjectiveAs is known, point clouds representing the objects are frequently used in object registration. Although the objects can be registered by using all the points in the corresponding point clouds of the objects, the registration process can also be achieved with a smaller number of the landmark points selected from the entire point clouds of the objects. This paper introduces a research study focusing on the fast and accurate rigid registration of the bilateral proximal femurs in bilateral hip joint images by using the random sub-sample points. For this purpose, Random Point Sub-sampling (RPS) was analyzed and the reduced point sets were used for an accurate registration of the bilateral proximal femurs in coronal hip joint magnetic resonance imaging (MRI) slices.MethodsIn registration, bilateral proximal femurs in MRI slices were registered rigidly by performing a process consisting of three main phases named as MR image preprocessing, proximal femur registration over the random sub-sample points and MR image postprocessing. In the stage of the MR image preprocessing, segmentation maps of the bilateral proximal femurs are obtained as region of interest (RoI) images from the entire MRI slices and then, the edge maps of the segmented proximal femurs are extracted. In the registration phase, the edge maps describing the proximal femur surfaces are represented as point clouds initially. Thereafter, the RPS is performed on the proximal femur point clouds and the number of points representing the proximal femurs is reduced at different ratios. For the registration of the point clouds, the Iterative Closest Point (ICP) algorithm is performed on the reduced sets of points. Finally, the registration procedures are completed by performing MR image postprocessing on the registered proximal femur images.ResultsIn performance evaluation tests performed on healthy and pathological proximal femurs in 13 bilateral coronal hip joint MRI slices of 13 Legg-Calve-Perthes disease (LCPD) patients, bilateral proximal femurs were successfully registered with very small error rates by using the reduced set of points obtained via the RPS and promising results were achieved. The minimum error rate was observed at RPS rate of 30% as the value of 0.41 (±0.31)% on all over the bilateral proximal femurs evaluated. When the range of RPS rate of 20-30% is considered as the reference, the elapsed time in registration can be reduced by almost 30-40% compared to the case where all the proximal femur points were included in registration. Additionally, it was observed that the RPS rate should be selected as at least 25% to achieve a successful registration with an error rate below 1%.ConclusionIt was concluded from the observed results that a more successful and faster registration can be accomplished by selecting fewer points randomly from the point sets of proximal femurs instead of using all the points describing the proximal femurs. Not only an accurate registration with low error rates was performed, but also a faster registration process was performed by means of the limited number of points that are sub-sampled randomly from the whole point sets.