Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental environment.There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.AIM To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and MDD.All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest scale.We performed a two-sample t-test between the two groups-SCZ patients and depressive patients.Our purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,DN.RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task.We identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)e.g.It is shared between DEP and SCZ,and includes frontal motor/language and parietal areas.One specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus,several occipital areas,including lingual and fusiform gyrus,as well as parahippocampal gyrus.Finally,component 12 appeared to be unique for the neutral condition.In addition,there have been determined circuits across components,which are either common,or distinct in the preferential processing of the sub-scales of the task.CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Retinal vasculature enhancement using independent component analysis

Retinal vasculature is a network of vessels in the retinal layer. In ophthalmology, information of retinal vasculature in analyzing fundus images is important for early detection of diseases related to the retina, e.g. diabetic retinopathy. However, in fundus images the contrast between retinal vasculature and the background is very low. As a result, analyzing or visualizing tiny retinal vasculature is difficult. There-fore, enhancement of retinal vasculature in digital fundus image is important to provide better visualization of retinal blood vessels as well as to increase accuracy of retinal vasculature segmentation. Fluorescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this research work, the low contrast problem of retinal fundus images ob-tained from fundus camera is addressed. We develop a fundus image model based on probability distribution function of melanin, haemoglobin and macular pigment to represent melanin, retinal vasculature and macular region, respectively. We determine retinal pigments makeup, namely macular pigment, melanin and haemoglobin using independent component analysis. Independent component image due to haemoglobin obtained is used since it exhibits higher contrast retinal vasculature. Contrast of reti-nal vasculature from independent component image due to haemoglobin is compared to those from other enhancement methods. Results show that this approach outperforms other non-invasive enhancement methods, such as contrast stretching, histogram equalization and CLAHE and can be beneficial for retinal vasculature segmentation. Contrast enhancement factor up to 2.62 for a digital retinal fundus image model is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.

Altered intra- and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis

Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity(FC) in upper-limb amputees(ULAs). This observational study included 40 ULAs and 40 healthy control subjects;all participants underwent resting-state functional magnetic resonance imaging. Changes in intra-and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus(precuneus gyrus) within the dorsal attention network and left precentral(precentral gyrus) within the auditory network;but increased in the left Parietal_Inf(inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2(crus Ⅱ of cerebellum) and left Temporal_Mid(middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper(rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping;rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain.

How Many Electrodes Are Really Needed for EEG-Based Mobile Brain Imaging?

A noninvasive method for imaging the human brain during mobile activities could have far reaching benefits for studies of human motor control, for research and treatment of neurological disabilities, and for brain-controlled powered prosthetic limbs or orthoses. Several recent studies have demonstrated that electroencephalography (EEG) can be used to image the brain during locomotion provided that signal processing techniques, such as independent Component Analysis (ICA), are used to parse electrocortical activity from artifact contaminated EEG. However, these studies used high-density 256-channel EEG sensor arrays, which are likely too time-consuming to setup in a clinical or field setting. Therefore, it is important to evaluate how reducing the number of EEG channel signals affects the electrocortical source signals that can be parsed from EEG recorded during standing and walking while concurrently performing a visual oddball discrimination task. Specifically, we computed temporal and spatial correlations between electrocortical sources parsed from high-density EEG and electrocortical sources parsed from reduced-channel subsets of the original high-density EEG. For this task, our results indicate that on average an EEG montage with as few as 35 channels may be sufficient to record the two most dominate electrocortical sources (temporal and spatial R2 > 0.9). Correlations for additional electrocortical sources decreased linearly such that the least dominant sources extracted from the 35 channel dataset had temporal and spatial correlations of approximately 0.7. This suggests that for certain applications the number of EEG sensors used for mobile brain imaging could be vastly reduced, but researchers and clinicians must consider the expected distribution of relevant electrocortical sources when determining the number of EEG sensors necessary for a particular application.

Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process

One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.

独立性视角下的相频融合领域泛化方法

针对现有的领域泛化(DG)方法对领域特征处理粗糙和泛化能力弱的问题,提出一种基于频域特征独立性这一独特视角解决领域泛化问题的方法。首先,设计频域分解算法,将图像的深度特征快速傅里叶变换(FFT)后,再从相位信息中获得领域无关特征,以提高模型对领域无关特征的识别能力;其次,基于独立性视角,通过对样本的特征赋权,进一步消除频域特征中各属性的相关性,提取最有效领域无关特征,解决样本特征之间相关性带来的泛化能力差的问题;最后,提出幅度融合策略,拉近源域和目标域的距离,进一步提升模型对未知领域的泛化能力。在流行的图像领域泛化的数据集PACS和VLCS上的实验结果表明,所提方法的准确率均值比StableNet分别高0.44、0.59个百分点,且在各个数据集上均取得了优秀的性能。

自主知识体系视域下“海外中共学”基本内涵的重构

目前国内学界界定的“海外中共学”系指海外学界建构的有关中国共产党的知识。这种意义上的“海外中共学”存在了近百年,其对于中国共产党国际形象的建构具有重要作用。基于此,国内学界提出了对海外中共学进行研究的“海外中共学研究”概念。当前,建构中国自主的知识体系是中国哲学社会科学发展的必然趋势。因此,有必要在深刻把握建构中国自主的知识体系这一语境中,通过学科整合,重构“海外中共学”的基本内涵。之所以要重构“海外中共学”基本内涵,主要在于现有的“海外中共学”及“海外中共学研究”等概念内涵,与建构中国自主的知识体系以及中共党史党建学一级学科建设的要求存在一定差距;同时,现有的“海外中共学”等概念内涵也与新时代中国共产党的国际活动、国际交往以及国际形象、国际影响力建构不相匹配。

基于人体图像生成的姿态无关人物识别

人物识别技术能够使机器人具备对用户身份识别的能力,从而有效提高机器人的智能交互水平。人物识别面临的主要挑战之一是姿态的变化对人物身份特征提取的影响。针对该问题,提出基于人体图像生成的姿态无关人物识别方法,通过生成与库中目标人物相同姿态的人体图像,消除姿态变化对人物外观特征造成的影响。该方法首先利用人体分割图将人体区域与背景分离,尽量降低复杂多变的背景对人物外观特征的干扰;然后在目标姿态的引导下生成与目标图像姿态一致的人物图像;最后设计了一个特征融合模块将源图像和生成图像的身份特征进行融合,提取姿态无关的鲁棒身份特征用于人物识别。此外,为更好地区分不同的人物,在训练中生成相同姿态的负样本,对约束模型学习更为细粒的可鉴别性身份特征。人物识别和人体图像生成的实验结果验证了该方法的有效性。

基于三重网络模型的酒精依赖患者静息态动态功能连接分析

目的:探讨酒精依赖(AD)患者静息态三重网络(中央执行网络、突显网络、默认模式网络)动态功能网络连通性(dFNC)的变化。方法:于2020年2月-2021年3月采集15例酒精依赖患者和15例健康志愿者(正常对照组)的静息态fMRI数据。对预处理后的fMRI数据进行独立成分分析以获得大脑网络成分,并通过滑动窗口方法生成大脑网络的动态功能连接矩阵。并对所有dFNC矩阵应用k-means聚类算法确定大脑网络连接模式,获得受试者脑网络的时间属性。最后,使用斯皮尔曼相关分析评估异常时间属性(时间分数、平均停留时间、转换次数)与酒精依赖量表(ADS)评分之间的关系。结果:最终将15例志愿者和12例AD患者纳入本研究,并通过聚类分析得到四种重复出现的功能连接状态。与对照组相比,AD组在弱连接状态下花费时间较长,但在中央执行网络和突显网络之间的强连接状态下花费时间较少(P均<0.05)。双样本t检验结果显示,AD患者中央控制网络内的IC38与IC49之间的功能连接显著增强(P<0.05,FDR校正)。结论:AD患者中央执行网络的功能连接发生了变化,本研究为探索酒精依赖的复杂神经病理学机制提供了一定的见解。

肝豆状核变性患者默认模式网络变化及模型构建

目的:基于独立成分分析(ICA),探讨肝豆状核变性(WD)患者默认模式网络(DMN)功能连接(FC)的改变及其与临床神经精神特征之间的关系。方法:将2021年1月-2021年12月在本院就诊的85例肝豆状核变性患者和年龄、性别相匹配的85例健康志愿者(HC组)纳入本研究。对每例被试采用统一肝豆状核变性评分量表(UWDRS)进行评估,包括神经功能症状(UWDRS-N)和精神症状(UWDRS-P)评分,并根据UWDRS评分将患者分为神经精神症状重度组(>10分)和轻度组(≤10分)。使用3.0T磁共振BOLD序列采集静息态fMRI数据,采用ICA方法提取DMN内各体素的FC值,并在HC组与WD组之间进行比较,对有差异脑区的FC值与临床量表评分进行Pearson相关性分析。以DMN内所有体素的FC值为特征变量采用支持向量机(SVM)的方法构建分类模型,包括正常组与WD组以及轻度与重度WD组。结果:与对照组比较,WD组的DMN内表现出广泛的FC值减低,包括前默认模式网络(aDMN)内的左内侧前额叶皮层(L_MPFC)和左侧前扣带回(L_ACC),以及后DMN(pDMN)内的左侧角回(L_ANG)、楔前叶(PCUN)、左侧顶下小叶(L_IPG)和左侧后扣带回(L_PCC)。aDMN内的L_MPFC、L_ACC和pDMN内的PCUN的FC值与UWDRS-N评分呈负相关,aDMN内的L_MPFC、L_PCC和pDMN内的L_IPG的FC值与UWDRS-P评分呈负相关。采用SVM构建的二分类器,在鉴别WD与HC组时的符合率为80.23%,AUC为0.865;在鉴别轻度与重度WD组的符合率为70.89%,AUC为0.723。结论:WD患者的默认模式网络中存在广泛的功能连接减低,其可能是导致患者出现神经精神症状(如高阶认知障碍)的潜在神经病理机制。基于DMN的FC值构建的SVM分类器可提高对WD疾病及其病情转归的评估效能。

基于独立成分分析对伴脑室周围白质损伤的痉挛型脑性瘫痪患儿脑功能网络的研究

目的:使用独立成分分析(ICA)方法探讨伴脑室周围白质损伤(PWMI)的痉挛型脑性瘫痪(SCP)患儿网络内和网络间功能连接改变。方法:对28例伴PWMI的SCP患儿(患儿组)及性别、年龄匹配的19例正常儿童(对照组)行fMRI,应用ICA方法提取2组网络成分,并用SPM12及NBS软件比较2组网络内和网络间功能连接差异,同时应用Pearson或Spearman相关性分析验证患儿组功能连接差异脑区与临床变量之间的相关性。结果:与对照组相比,患儿组默认模式网络(DMN)内左侧额中回、左侧楔前叶及右侧角回,vAN内右侧中央后回,VNm内双侧舌回功能连接减低。以上功能连接差异脑区与临床变量均未发现显著相关性。结论:SCP与大脑网络功能连接异常有关,推测网络功能连接改变可能成为早期识别和诊断SCP的影像学生物指标。

Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images

The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.

Synthetically Evaluation System for Multi-source Image Fusion and Experimental Analysis

Study on the evaluation system for multi-source image fusion is an important and necessary part of image fusion. Qualitative evaluation indexes and quantitative evaluation indexes were studied. A series of new concepts, such as independent single evaluation index, union single evaluation index, synthetic evaluation index were proposed. Based on these concepts, synthetic evaluation system for digital image fusion was formed. The experiments with the wavelet fusion method, which was applied to fuse the multi-spectral image and panchromatic remote sensing image, the IR image and visible image, the CT and MRI image, and the multi-focus images show that it is an objective, uniform and effective quantitative method for image fusion evaluation.

运动行为研究的体育学学科独立性—基于体育学学科发展的历史与现实考察

在“双一流”建设中,学科建设是重要的基础性内容。从学科演进的历史看,学科分类是基于研究对象的问题域形成的。随着对研究对象的科学认知不断深入,落实到具体的学术实践,即以问题为导向,建立具有学科独立性的方法论体系。基于对体育学学科发展的历史与现实考察,从体育学学科独立性角度提出:①体育学的学科定位应从研究“人体”的生物学层面拓展出来;②应形成体育学学科研究对象的独立性;③研究运动情境中人的运动行为是体育学方法论的立论基点,从身体认知的行为叙事建立身体感,以此为基石与相应的学科研究构成互证证据链结构的论证,是解决运动行为“真实性”判断实践问题的有效途径;④体育学研究以“运动行为志”和“运动行为意象分析”2种方法学技术研究人的运动行为,提供了多元互证证据链结构叙事中证实或证伪的学术依据,开启了一条体育学逼近运动行为真相的“方法学路径”。此外,以学术实践成果“抢花炮的人文田野调查”为个案,展示了体育学新方法论的学术创新意义。

基于rs-fMRI对遗忘型轻度认知功能障碍患者脑默认网络改变的研究

目目的的:探索遗忘型轻度认知功能障碍(aMCI)患者静息态默认网络(DMN)内功能连接(FC)改变及其与认知功能的关系。方法:根据纳入标准和排除标准,对40例未经治疗的aMCI患者(aMCI组)及40例认知功能正常的志愿者(对照组)行静息态功能磁共振成像(rs-fMRI)检查,对照组的性别、年龄、受教育年限与aMCI组相匹配。应用独立成分分析(ICA)方法提取2组受试者的DMN,并用SPM 12软件比较2组间DMN内FC差异,同时应用Pearson或Spearman相关性分析验证aMCI组差异脑区zFC值与神经心理学评分的相关性。结果:与对照组比较,aMCI组FC增强脑区位于右侧海马旁回、左侧岛叶、左侧扣带回后部、左侧楔叶、右侧岛盖部额下回、左侧缘上回、左侧楔前叶及右侧中央前回;FC减弱脑区位于左侧额中回、右侧缘上回、右侧扣带回前部、右侧角回及颞中回。相关性分析显示,aMCI组左侧额中回(r=0.439,P=0.005)、右侧颞中回(r=0.422,P=0.007)、右侧海马旁回(r=0.468,P=0.002)及左侧楔前叶zFC值(r=0.482,P=0.002)与MMSE评分呈正相关;右侧扣带回前部zFC值与AVLT延迟记忆评分(r=0.362,P=0.022)、DST顺背评分(r=0.503,P=0.001)呈正相关;右侧中央前回zFC值与TMT-B评分(r=-0.450,P=0.004)呈负相关。结论论:aMCI患者DMN内发生FC异常改变与记忆力、注意力、执行功能及视听功能缺陷相关,推测DMN内FC改变可能成为早期识别和诊断AD的影像学生物指标。

正常人眼睫状肌不同调节负荷下的大脑皮层激活状态

目的 探讨正常人眼不同调节负荷下的大脑皮层激活状态、功能脑网络及脑区间有效连接关系。方法 选择正常受试者33例,进行任务态功能核磁共振扫描,基于MATLAB平台的SPM12、GroupICATv4.0a软件包进行影像数据预处理和后续分析。使用单样本t检验分析正常人群调节相关脑激活模式;运用Informax算法将数据分解为独立的空间成分,提取任务对应的成分,分析相关的功能脑网络;基于动态因果模型分析任务激活区之间的有效连接,明确相应脑区的有效连接关系。应用体素水平阈值P <0.001,FWE团块水平P≤0.05为多重比较校正方法。结果 正常人眼-3D和-6D调节刺激下的大脑激活信号表明,支配睫状肌的神经信号所涉及的大脑皮层区域广泛,有枕叶、颞叶、小脑、边缘叶、丘脑、顶叶和额叶,且正常人眼不同调节负荷下大脑激活的区域和强度均有变化;参与调节任务态相关的功能脑网络,主要为视觉网络、小脑网络和注意网络;各脑区间有效连接分析初步表明,参与调节的回路有2个,分别是枕叶、小脑与顶叶组成一个神经环路,枕叶、小脑和额叶组成一个神经环路,丘脑为信息传导中介。结论 参与大脑加工调节信号的区域较广,涉及多个脑区,多个脑网络,2个神经回路;不同调节负荷下脑激活的区域和强度均有差异。

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared （IR） and visible image fu- sion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis （ICA） is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can de- press the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fu- sion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the pro- posed method can provide better perception effect.

帕金森病执行控制网络对运动调节作用的动态功能连接研究

目的:基于静息态功能MRI(rs-fMRI)探讨帕金森病(PD)患者执行控制网络与运动相关脑网络之间动态功能网络连接性(dFNC)的变化及其临床意义。方法:采集50例PD患者和50例健康志愿者(对照组)的脑rs-fMRI数据,采用独立成分分析法(ICA)提取执行控制网络、感觉运动网络、小脑网络和基底神经节网络的成分,基于滑动窗口相关法,采用k-means聚类法识别各网络中可重复出现的瞬时功能连接模式,评估各独立组分之间功能连接性的动态变化,计算PD患者动态功能连接的时间特性,即时间分数、平均滞留时间、转换次数与帕金森病统一评分量表的第Ⅲ部分(UPDRS-Ⅲ)评分之间的关系。结果:经动态分析得到执行控制网络、感觉运动网络、小脑网络和基底神经节网络的5种重复出现的功能连接状态,其中状态3的停留时间更长(t=2.192,P<0.05),整体呈现较为紧密的连接状态。PD组与对照组之间在状态3时的网络间功能连接有显著差异(t=2.745,P<0.05)。相比于对照组,PD组中执行控制网络(IC25)与基底神经节网络(IC19)和小脑网络(IC28)之间的功能连接减少(t=-2.436,P<0.05;t=-2.012,P<0.05),感觉运动网络(IC31、10、16)与基底神经节网络(IC19)之间的功能连接减少(t=-2.907,P<0.05;t=-3.653,P<0.05;t=-2.005,P<0.05),感觉运动网络(IC31、10、16)和小脑网络(IC28)之间的功能连接减少(t=-3.459,P<0.05;t=-4.748,P<0.05;t=-3.452,P<0.05),感觉运动网络内部(IC10与IC31、IC16)的功能连接增强(t=2.407,P<0.05;t=4.596,P<0.05)。此外,PD组和对照组的时间分数在状态2、3和4时存在显著差异(P=0.041;P=0.001;P=0.003),平均滞留时间在状态3和4时存在显著差异(P=0.003;P=0.001)。状态3的时间分数和平均滞留时间与临床运动功能评分呈负相关(r=-0.395,P=0.025;r=-0.481,P=0.010)。结论:PD患者执行控制网络与运动相关脑网络间功能连接的变化及其时间变异性,不仅证实了大脑网络连接模式的时变性和小脑参与PD的运动过程,还说明高级别控制系统功能缺陷与PD运动症状的产生有关,有助于进一步明确PD发生运动障碍的潜在机制。

基于PBL的翻转课堂在影像科实习带教中的应用

目的探讨基于以问题为导向的教学法(problem-based learning,PBL)的翻转课堂教学法在医学影像学见习教学中的应用价值。方法选择贵州医科大学2020年6—9月、2021年6—9月接收的58名全日制大学本科医学影像专业实习学生作为研究对象,随机分为对照组和研究组,对照组采用传统教学模式,研究组采取基于PBL的翻转课堂教学模式,对比分析两组学生在教学三个月后的专业理论知识、临床实际操作技能的掌握情况及对实习教学满意度评分。结果研究组的阅片成绩、理论成绩及教学满意度调查分数均高于对照组,差异有统计学意义(P<0.05)。结论基于PBL的翻转课堂在影像学实习带教中具有更好的教学效果,在教学的同时培养了学生自主学习的能力。