浅论计算机字体的著作权保护

计算机字体库制作过程有三个阶段，计算机字体制作只是其中之一。具有独创性的单个计算机字体是著作权法中的“作品”，应该与单个计算机字体的集合即字库分别拥有著作权。但也应该承认，并不是所有的单个计算机字体都具备独创性。

探讨多媒体计算机对高等教育信息化的促进

随着科学产业的发展,信息产业已被广泛应用于多个领域。近年来,我国越来越重视高等教育,多媒体教学工具的使用也促进了新的教学改革。并且在教学中,使用多媒体信息技术有助于高等教育的计算机化发展,所以就要求在信息技术迅速发展的背景下对高等教育进行适当改革,并将信息和通信技术广泛应用于高等教育。文章介绍了教育信息的意义和多媒体教学技术的内涵,然后分析计算机在教育信息中的作用,并提出多媒体计算机技术在教学中的应用的优化措施。

^(18)F-FDG PET/CT在弥漫大B细胞淋巴瘤中的研究进展

弥漫大B细胞淋巴瘤(diffuse large B cell lymphoma,DLBCL)是最为多见的一类成人淋巴瘤,是在临床表现、组织形态和预后生存等方面具有明显异质性的淋巴造血系统恶性肿瘤。氟代脱氧葡萄糖正电子发射计算机断层扫描(fluorine-18-fluorodeoxyglucose positron emission computed tomography,^(18)F-FDG PET/CT,简称PET/CT)现已成为淋巴瘤早期诊断与分期、疗效评估、指导治疗、预后预测的重要手段。本文对^(18)F-FDG PET/CT在弥漫大B淋巴瘤中诊疗的研究进展作一综述,从开发新型显像剂、PET/MR在淋巴瘤中应用的角度出发,总结优势与不足,并对未来的研究进行展望。

Deep learning model based on PET/CT and combination with Cox proportional hazard model for predicting progression of lung invasive adenocarcinoma after surgery

Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.

CT容积扫描在颈椎病诊断中的应用体会

目的探讨CT容积扫描在颈椎病诊断中的应用价值。方法回顾性分析我院2013年6月至2014年9月592例颈椎病患者,分析容积扫描在各型颈椎病CT表现中的应用效果。结果各型颈椎病容积扫描均可很好显示,且以颈型、神经根型最常见。结论 CT容积扫描诊断颈椎病,图像直观,方便准确,此法应广泛推广。

大跨度浅埋隧道岩体的工程地质研究

目的以我国跨度最大的公路隧道韩家岭浅埋隧道为背景,讨论通过不连续面网络图模拟真实岩体的几何特征,建立工程适用的计算模型问题,探讨地表不连续面研究成果对于隧道稳定性分析和设计的意义.方法采用蒙特卡洛方法建立不连续面模拟的数学模型,应用计算机随机绘制拟开挖岩体不连续面三维网络图.结果介绍了隧道岩体相关工程地质工作,给出了基于统计原理的岩体不连续面三维计算机模拟方法.结论应用“岩体不连续面数据处理系统”进行数据的计算机处理,可以随机模拟不同不连续面产状参数,绘制出拟开挖岩体不连续面三维网络图,建立工程适用的计算模型.设计初期由拟开挖隧道附近公路路堑边坡得到的工程地质研究结果与隧道施工期间得到的隧道围岩工程地质研究结果基本一致,表明采用地表不连续面工程地质工作研究结果进行隧道设计的可能性.

不同假体设计与安装参数对全髋术后关节活动度的影响

目的研究全髋关节假体安装参数、不同头颈直径比和颈干角对假体撞击和髋关节活动范围的影响,分析髋臼杯位相安全范围及杯颈前倾角优化组合。方法建立人工全髋关节三维可视化计算机模型,将同时满足髋关节屈曲≥110°、屈曲90°内旋≥30°、后伸≥30°、外旋≥40°定义为日常生活活动范围普通标准,而将屈曲≥120°、屈曲90°内旋≥45°、后伸≥30°、外旋≥40°定义为严格标准,头颈比变化范围为2.00～2.92,股骨假体前倾角变化范围为0°～30°,髋臼假体外展角变化范围为10°～60°,髋臼前倾角变化范围为0°～70°,颈干角设定为130°和135°;计算满足上述两种活动范围,臼杯外展角每变化5°,相应的髋臼假体前倾角安装参数,并对数据进行分析。结果头颈比>2.37时,安全范围面积增大明显;严格标准条件下的安全范围比普通标准的小。颈干角130°,普通标准和严格标准活动度下,髋臼杯前倾角(Y)与股骨假体前倾角(X)的关系分别为Y1=-0.8164X1+41.914,Y2=-0.8028X2+47.094;允许的最小髋臼外展角(IAmin)和头颈比(HNR)的关系分别为IAmin'1=168.13HNR1-2.504,IAmin'2=213.79HNR2-2.272;当颈干角从135°移向130°,杯安全范围移向杯前倾角较小的区域,面积也有所减小。结论髋臼杯位相安全范围随着头颈比增大而增大;头颈比>2.37是比较合适的。髋关节活动范围要求越高,臼杯位相安全范围就越小,但可以通过增大头颈比来纠正。允许的最小髋臼外展角随着头颈比增大而变小;杯前倾角与股骨前倾角呈负相关。颈前倾角的增大和颈干角的变小均使得杯安全范围移向杯前倾角较小的区域,杯外展角随着颈干角增大而变小。

Research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer

Dual-layer spectral detector CT is a new spectrum CT imaging technology based on detector being able to obtain both images similar to true plain and spectral images in one time scanning.The reconstructed multi-parameter spectral images can not only improve image quality,enhance tissue contrast,increase the visualization and detection ability of occult lesions,but also provide qualitative and quantitative analysis of the lesions,so as to provide more imaging information and multi-dimensional diagnostic basis.The research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer were reviewed in this article.

腺苷导致ST段压低而体层成像显示灌注正常的患者的临床结局

在输注腺苷的过程中出现ST段压低的大多数患者均有可逆性灌注缺损,这可被单光子发射计算机体层成像(SPECT)灌注显影所检测到。但是在灌注显影正常的患者中也可偶见ST段压低。

Preoperative CT radiomics models for predicting composition of in vivo urinary calculi

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7∶3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A—E corresponding to group A—E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860—0.948 in training set and of 0.856—0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25%,83.79%,46.23%and 0.596 in training set,respectively,while was 80.63%,75.26%,43.48%and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease:Correlations with blood eosinophil level

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA%)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA%)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA%of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LD grade 4,middle lobe of right lung WT grade 5,upper lobe of right lung WA grade 4,middle lobe of right lung WA grade 5 and lower lobe of left lung WA grade 3 in low EOS group were all higher than those in high EOS group(all P<0.05).Except for the upper lobe of right lung LD grade 4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P<0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA%,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Automated body composition analysis system based on chest CT for evaluating content of muscle and adipose

Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.

0D-1D coupling model and 3D fluid-structure interaction model based on coronary CT angiography for displaying hemodynamic characteristics of coronary artery stenosis

Objective To observe value of 0D-1D coupling model and 3D fluid-structure interaction(FSI)model based on coronary CT angiography(CCTA)for displaying hemodynamic characteristics of coronary artery stenosis.Methods Based on CCTA data of the stenosed left anterior descending branch(LAD)in a patient with coronary heart disease,an 0D-1D coupling model and 3D FSI model were built,respectively.Then hemodynamic characteristic indexes,including the pressure,flow velocity and wall shear stress(WSS)were obtained in every 0.01 s during 1 s at 5 sampling points(i.e.sampling point 1—5)using these 2 models,respectively,and the consistencies of the results between models were evaluated with Spearman correlation coefficient r s.Results The time consuming for construction of 0D-1D coupling model and 3D FSI model was 0.033 min and 704 min,respectively.Both models showed basically distribution of the pressure,flow velocity and WSS of the stenosed LAD.For more details,the pressure at the stenosed segment of LAD and the proximal segment of stenosis were both higher,which gradually decreased at the distal segment of stenosis,and the flow velocity at the proximal segment of stenosis was in a relatively slow and uniform condition,with significantly increased flow velocity and WSS at the stenosed segment.Compared with 3D FSI model,0D-1D vascular coupling model was relatively unrefined and lack of distal flow lines when displaying blood flow velocity.For sampling point 2 at the stenosed segment of LAD,no significant consistency for pressure between 2 models was found(P=0.118),but strong consistency for the flow velocity and WSS(r s=0.730,0.807,both P<0.05).The consistencies of pressure,flow velocity and WSS between 2 models at the proximal and distal segment of stenosis,i.e.1,3—5 sampling points were week to moderate(r s=0.237—0.669,all P<0.05).Conclusion 0D-1D coupling model exhibited outstanding computational efficiency and might provide relatively reasonable results,while 3D FSI model showed higher accuracy for details and streamline when simulating LAD stenosis.

多层螺旋CT血管造影在诊断下肢动脉硬化闭塞症中的应用价值

目的探讨多层螺旋CT血管造影三维重建在诊断下肢动脉硬化闭塞症中的应用价值。方法对24例临床怀疑下肢动脉硬化的患者行16层螺旋CT下肢动脉血管成像(CTA),采用多平面重建(MPR)、最大密度投影(MIP)和容积再现(VR)四种图像后处理方法。结果 CTA清晰显示了24例患者的双下肢动脉及主要分支,有23例患者可见不同程度和范围的狭窄或闭塞。其中15例行DSA检查,175段血管中14段狭窄程度与CTA不同。结论 16层螺旋CT血管造影是一种安全、可靠、无创的检查手段,能够清晰显示下肢动脉硬化狭窄及闭塞的程度及范围,在诊断下肢动脉硬化闭塞症中具有重要的应用价值。

腹内疝CT诊断的探讨

目的加强对腹内疝CT表现的认识,提高腹内疝的CT诊断水平。方法回顾性分析16例经手术证实的腹内疝患者的临床及CT资料。结果 16例术前CT检查均表现为小肠梗阻,6例发生绞窄。10例术前考虑为腹内疝。术后证实16例患者均有腹内疝,其中大网膜裂孔疝1例,肠系膜裂孔疝3例,盲肠周围疝1例,肠粘连束带内疝10例,胃切除术吻合口后疝1例。各型腹内疝CT的共有征象和个体征象与手术结果存在相关性。结论仔细察查CT图像上小肠的走行及梗阻部位的形态,可以对本病给予明确诊断或提示。

21世纪医学科学发展的前景

就生物医学工程的发展进程进行了回顾,通过事实证明:放射医学、超声医学、激光医学、计算机远程医疗技术等先进的医疗技术和仪器设备都是医学工程研发的成果,由此说明20世纪生物医学工程的发展,显著提高了医学诊断和治疗水平,有力地推动着医学科学的进步,并对21世纪生物医学工程的发展前景进行了展望。

骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现

目的:探讨骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现及临床特点,旨在提高对该病的认识。方法:回顾性分析12例经病理证实的骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI资料并复习相关文献,总结其影像学特征。结果:病灶位于鼻口咽3例,舌1例,颌下1例,鼻腔鼻窦3例,颈根部1例,胸腔2例,空肠1例。4例同时行CT及MRI平扫+增强检查,5例行CT平扫+增强检查,3例行MRI平扫+增强检查。病变为浸润性生长的软组织肿块,体积较大,密度或信号不均,增强后强化程度不一,3例DWI高b值扩散受限呈混杂高/稍高信号,对应ADC图为低信号。12例均伴坏死,2例伴钙化,12例均伴邻近结构受压移位,9例伴周围组织受侵,5例伴骨质破坏,5例病灶内部或边缘见血管影,2例MRI检查见流空血管影,3例伴淋巴结转移。结论:骨外尤文肉瘤/外周原始神经外胚层肿瘤的CT及MRI表现具有一定的特征:病灶多表现为体积较大的软组织肿块,占位效应明显,囊变坏死多见,钙化少见,易侵犯周围组织,病灶周围或内部常可见流空血管影。