The 5th International Conference on Biomedical ...

BMC Medical Imaging 2016, 16(Suppl 1):65 DOI 10.1186/s12880-016-0164-6

MEETING ABSTRACTS

Open Access

The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Hangzhou, China. 1-8 August 2016 Published: 2 December 2016

01 The application value of three-dimensional rotational angiography of intracranial micro-aneurysms in diagnosis and treatment Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1 Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2 Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):01 Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA. Keywords Three-dimensional rotational angiography, Intracranial micro-aneurysm, Three dimensional reconstruction

Acknowledgments Funding: Shandong Natural Science Fund (Project No.Y2008C102) Laboratory: Shandong Key Laboratory of Advanced Medical Imaging Technologies and Applications.

02 Recent advance of immunology-inspired medical imaging Tao Gong, Qi Mao, Shuguang Zhao, Fang Han College of Information Science and Technology, Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education, Donghua University, Shanghai 201620, China Correspondence: Tao Gong ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):02 Aims In order to improve the medical imaging, some immune computation theories and immune algorithms were reviewed and compared. Materials and methods The immune computation theories include the self and nonself theory, danger theory, artificial immune network etc. The immune algorithms include self/nonself detection algorithm, normal model construction algorithm, clonal selection algorithm, negative selection algorithm, danger model algorithm and hybrid immune algorithm etc. We improved the clonal selection algorithm to attain the optimal threshold for better segmentation of the medical images than the traditional approach. Results The X-ray medical image of the tuberculosis was processed with the improved clonal selection algorithm and noise filtering, and the output medical image of our approach is better for diagnosis than that of traditional image processing methods. Conclusions The immune algorithm can be improved to establish a better medical imaging, and this kind of medical application system is inspired from the human immune system. Acknowledgements Supported by the project grants from National Natural Science Foundation of China (Grand No. 61673007, 61271114, 11572084, 11472061 and 61203325), Natural Science Foundation of Shanghai (Grand No. 13ZR1400200), the Fundamental Research Funds for the Central Universities (DHU Distinguished Young Professor).

© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

BMC Medical Imaging 2016, 16(Suppl 1):65

03 Medical image classification based on guided bagging Keming Mao, Yixian Liu Intelligent multimedia information processing Lab, College of Software, Northeastern University, Shenyang, Liaoning Province, 110004, China Correspondence: Keming Mao ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):03 Aims Traditional medical image classification methods focus on feature representation and classifier design. However, they seldom concerns data selection used for model training, which plays key role for model tuning and parameter optimization. This paper proposes a novel medical image classification method according to guided bagging. Materials and methods First, unsupervised learning is implemented for training image. Clusters are gained based on generative model. Then, at the discriminative model construction stage, training data is sampled covering all the data clusters, with a probability proportional to the density of each cluster. This method employs a well-distributed and balanced training data, and utilizes the virtue of generative and discriminative learning. Results The experiment uses the public available CT lung image dataset for evaluation. 379 lung CT images are contained, which are collected by 50 different CT lung scans. The standard data is described by the instruction of an expert. Experimental evaluations show that our proposed method has better performance in the field of lung nodule CT image classification comparing with traditional ones. Conclusions This paper utilizes the generative and discriminative training model, and a unified classifier is constructed for lung nodule classification. The proposed method is well-designed and the experimental results are preferable. Acknowledgements This paper was supported by National Natural Science Foundation of China (No. 61472073).

04 Cortical bone ultrashort TE study with inversion recovery preparation Yanchun Zhu, Shuo Li, Jie Yang, Nan Fu, Shaode Yu, Rongmao Li, Jing Xiong, Yaoqin Xie Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China Correspondence: Yaoqin Xie ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):04 Aims Efficient improving contrast of cortical bone from surrounding long T2 tissues is important in ultarshort echo time (UTE) imaging. Methods UTE acquisition prepared by adiabatic inversion recovery were developed for this purpose. The effect of TI on cortical bone imaging was evaluated on mature bovine tibial mid-shafts using a 3-T clinical MR scanner. The imaging parameters were: TE/TR = 10 μs/300 ms, TI = 80, 90, 100, 110, 120, 130, and 140ms, FA =45°, Bandwidth = ±62.5 kHz, FOV = 8cm, slice thickness = 7mm, NEX = 2, single slice. Results With TI = 90ms, excellent suppression of long T2 signals was achieved with the CNRcortical-muscle value of 13.49 ± 0.67, and the CNRcortical-marrrow value of 12.26 ± 0.86. Due to different T1s of muscle and fat, some residual signals from fat were presented. Therefore, the CNRmarrow-muscle value was 1.24 ± 0.35. Furthermore, approximate 80% signals from muscle and fat were suppressed.

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Conclusions The 2D adiabatic inversion UTE sequence with a TI of 90 ms provided excellent contrast depiction of bovine cortical bone. Due to the T1 difference, muscle and fat longitudinal magnetizations cannot arrive to the null point at the same time. Therefore, simultaneous reduction of long T2 signals is complicated. Acknowledgements Supported by 81501463, 2014A030310360, 2011S013, 2015AA043203, JCYJ20140417113430639, SIAT Innovation Program for Excellent Young Researchers (201302), KQJSCX20160301144248, and Beijing Center for Mathematics and Information Interdisciplinary Sciences.

05 Preliminary research on brain tumor detection in MRI scanning based on wavelet entropy and kernel support vector machine trained by sequential minimal optimization Shuihua Wang1,2,3, Sidan Du4, Zhimin Chen5,6, Preetha Phillips7,8, Shuwen Chen9, Zeyuan Lu10,11, Ping Sun2,12, Zhengchao Dong13,14, Yudong Zhang1,15,16 1 School of Computer Science and Technology, Nanjing Normal University, Nanjing, China; 2Department of Electrical Engineering, The City College of New York, CUNY, New York, USA; 3Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, China; 4School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China; 5College of Engineering, Nanyang Technological University, Singapore 639798, Singapore; 6School of Electronic Information, Shanghai Dianji University, Shanghai, China; 7School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA; 8Davis College of Agriculture, Natural Resources and Design, West Virginia University, Morgantown, WV, 26505, USA; 9State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; 10Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; 11College of Agricultural and Life Sciences, University of Florida, Gainesville, FL 32611, USA; 12Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA; 13Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA; 14Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, Guilin, Guangxi 541004, China; 15State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310027, China; 16School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, M156BH, UK Correspondence: Yudong Zhang ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):05 Aims Brain tumors occur if abnormal cells form and accumulate within the brain. Two types of brain tumors exist as benign tumor and cancerous tumor. In order to detect brain tumors in MRI scanning in a more efficient way, we proposed a novel computer-aided diagnosis (CAD) system. Materials and methods: A 100-image 256x256 T2-weighted MR brain dataset was obtained from the homepage of Harvard Medical School. Among the 100 images, 20 are normal control and 80 are with tumors. Our CAD system was established based on the hybridization of wavelet entropy (WE) and kernel support vector machine (KSVM). Our system firstly used WE to obtain distinguishing features from MR images on all subband coefficients obtained by discrete wavelet transform. 5-level Haar wavelet was utilized to obtain a sixteen-element vector. The vector was fed into the classifier of KSVM that embedded kernel technique into plain support vector machine. The kernel was chosen as the radial basis function (RBF) function. We use grid-searching method to get the optimal RBF scaling factor as 1. KSVM was trained by sequential minimal optimization (SMO) algorithm. Results and Conclusion The 10 repetition of 10-fold stratified cross validation results showed the proposed WE + KSVM method achieved an excellent classification


performance with an average accuracy of 98.80%, an average sensitivity of 99.50%, and an average specificity of 98.63%. The proposed “WE + KSVM” method is a promising brain tumor detection method for MRI scanning. Keywords brain tumor; detection; wavelet entropy; sequential minimal optimization; computer-aided diagnosis; radial basis function; cross validation; kernel support vector machine. Acknowledgements This paper was supported by NSFC (61602250, 61503188), Natural Science Foundation of Jiangsu Province (BK20150983), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010), Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (15-140-30-008K), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), Open Fund of Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, (SDL201608), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), and Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607).

06 Study on geometric efficiency for MDCT Jingwen Zhuang, Junzheng Zheng, Mei Bai Department of Biomedical Engineering, Xuanwu Hospital of Capital Medical University, Beijing, 100053,China. Correspondence: Mei Bai ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):06 Aims To investigate the dependence of geometric efficiency of a MDCT system on several exposure parameters such as tube voltage, collimation and pitch. Materials and methods Dose profiles in PMMA phantom for Siemens Definition Flash CT and GE Discovery CT750 HD were derived in helical mode using different tube voltages, collimations and pitches. Corresponding geometric efficiencies and weighted geometric efficiencies were calculated. Kruskal-Wallis test was performed to test the differences between weighted geometric efficiencies using different exposure parameters and the Spearman’s correlation coefficient was calculated to determine the correlation between different exposure parameters and weighted geometric efficiencies. Results With larger collimation the weighted geometric efficiency can be improved by 30%, while combined with larger pitch the weighted geometric efficiency can be reached to about 70%. Weighted geometric efficiencies had positive correlation with beam collimation and pitch (p < 0.05) for both CT scanners, while there was no significant difference between weighted geometric efficiencies with different tube potentials (p > 0.05). Conclusions The decrease of geometric efficiency leads to the increase of patient radiation dose. It is necessary to improve the geometric efficiency and reduce the burden of patients by optimal setting beam collimation and pitch for CT scans. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No.81372923). Keywords multidetector computed tomography; geometric efficiency; radiation dose

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07 Study of white matter in adolescent patients with depression by MR-diffusion tensor imaging Ning Mao12#, Xinnuan Mu3#, Cong Xu4, Yulu Song3, Xiaolei Song 3, Bin Wang3*, Haizhu Xie1* 1 Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People’s Republic of China; 2Department of Radiology, Peking University People’s Hospital, Beijing, 100191, People’s Republic of China; 3Department of Radiology, Binzhou Medical University Hospital, Binzhou, Shandong, 256603, People’s Republic of China; 4Department of Nephrology, Yantai Chinese medicine hospital, Yantai, Shandong, 264000, People’s Republic of China Correspondence: Bin Wang ([email protected]); Haizhu Xie ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):07 #

Ning Mao, Xinnuan Mu contribute equally to this work. Aims To explore the changes of the white matter in adolescent depression by using method of Tract-Based Spatial Statistcs (TBSS). Materials and methods We have applied TBSS to 35 depressed adolescents and 40 matched control to exam WM microstructure. With TBSS, we have concluded the fractional anisotropy (FA), axial diffusivity (AD), radical diffusivity (RD) and mean diffusivity (MD) of adolescent patients with depression and controls. Results Research found unusual WM structure among adolescent depression. Our analysis showed that the FA values are lower (P < 0.01), the RD and MD values are elevated (P < 0.01), and the AD values are Invariant (P > 0.05) in the patients’ body of the corpus callosum (CC). There is a contrary relationship between the severity of depression and FA values in the body of the CC(P < 0.01). Conclusion Our study showed that WM abnormalities are occurred in the pathophysiology of depression. What’s more, our research suggested that these changes occurred in the early stages of the disease. Keywords Adolescent Depression; diffusion tensor imaging; white matter

08 A landmark-based approach for mid-sagittal plane detection in 3D brain MR images Ke Gan, Daisheng Luo College of Electronics and Information Engineering, Sichuan University, Chengdu, China Correspondence: Ke Gan ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):08 Aims This paper presents a fully automated approach for the mid-sagittal plane (MSP) detection in 3D brain MR images. This method detects the MSP by accurately identifying highly-visible anatomical landmarks in the brain. Materials and methods The proposed method is landmark-based, this involves a training phase, which is performed once for a particular set of data, using some spatially aligned images with known anatomical landmark locations. The center points of the anterior commissure (AC), posterior commissure (PC) and midbrain-pons junction (MPJ) were manually delineated on the training images by an expert. In the detecting phase, the intensity of the testing image was normalized and transform into the same space as the training images. The image feature of AC, PC, MPJ obtained in the training stage were used to match the AC, PC, MPJ in the testing image. To accelerate the matching, the landmark detection was conducted in the neighborhood of the mean AC, PC, MPJ positions in the normalized space. An refinement procedure was carried out to


further adjust the detected landmarks. Finally, the formulation of the 3D MSP equation was estimated by the detected landmarks. Results The proposed method was applied to 30 T1-weighted brain MR images. All testing results were visually inspected and judged to be correct without obvious error. The directional difference of plane normal (DDPN) between automated detection and manual labeling has been evaluated, the average DDPN we achieved was 2.83°. Conclusions The promising results indicate this method can be potentially useful in clinical applications. 09 Medical image classification by multiple classifier learning Keming Mao, Zhuofu Deng Intelligent multimedia information processing Lab, College of Software, Northeastern University, Shenyang, Liaoning Province, 110004, China Correspondence: Keming Mao ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):09 Aims Medial image classification is a difficult task for its high similarity inter-class and low similarity intra-class. Traditional methods usually devise a single classifier. While, in this paper we focus on learning a multiple classifier for each type for medical image classification. Materials and methods The proposed method designs a boosted learning framework. First, an initial classifier is constructed according to the feature distribution of training image set. Then, more classifiers are trained in an iterative way. The overall performance can be enhanced successively. Moreover, the optimal weights can be gained on each individual classifier. Results The experiment uses the public available CT lung image dataset for evaluation. 379 lung CT images are contained in this dataset, which are collected by 50 different CT lung scans. The standard data is described by the instruction of an expert. Experimental evaluations show that the proposed method outperforms traditional methods with application to lung nodule CT image classification task. Conclusions This paper utilizes the boosted learning, combine multiple classifier for CT lung image classification. The proposed method exploits the feature representation distribution, and the experimental results are preferable. Acknowledgements This paper was supported by National Natural Science Foundation of China (No. 61472073).

10 Comparison between conventional and golden ratio based radial trajectories: an eddy currents study Jie Yang, Yanchun Zhu, Shuo Li, Nan Fu, Shaode Yu, Rongmao Li, Yaoqin Xie Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China Correspondence: Yanchun Zhu ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):10 Aims Golden ratio based k-space trajectories are widely used in dynamic MRI since it provides approximate uniform k-space distribution. However, rapidly switching gradient induces eddy currents, generating artifacts in image. The golden ratio (GR) based radial strategy was compared with conventional radial strategy on 0.7T open superconducting MRI system. Materials and methods In conventional radial strategy, a constant angle increment Φuniform = 180°/ P between neighboring profiles. In GR based radial strategy, the azimuthal spacing is ΦGR = 180°/1.618 = 111.2°. First, a simulation was carried out to the comparison between ideal and net gradient of Shepp-Logan phantom

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with two radial strategies respectively. Second, a pure water phantom was sampled by Cartesian, conventional and GR based radial trajectory on the 0.7T open superconducting MRI system. Three orthogonal planes were acquired. SNR was compared between these three sampling trajectories, and images from Cartesian trajectory were used as reference. Results Result of simulation has illustrated that the impact of eddy currents on the ideal gradient with GR based radial strategy is more apparent. The result of a pure phantom shows that SNR values of both radial strategies (conventional: 40.76 GR:16.11) are far smaller than Cartesian strategy (145.15). Conclusions Eddy currents artifacts are more serious in GR based radial trajectory. Rapid switching gradient in GR based radial strategy induces more eddy currents than conventional radial strategy, which may limit the application of GR based trajectories especially in high magnet filed system. Acknowledgements Supported by 81501463, 81671853, 2014A030310360, 2011S013, JCYJ201500731154850923, KQCX20140521115045441, JCYJ20140417113430585, JCYJ20140417113430639.

11 ROI segmentation by localizing region-based active contours Zhenghao Shi1, Jiejue Ma1, Minghua Zhao1, Yonghong Liu2, Yongchao Wang1 1 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China; 2Xianyang Hospital, Yan’an University, Xianyang, 712000, China Correspondence: Zhenghao Shi ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):11 Introduction Accurate segmentation of Region of interest (Region of Interest, ROI) has an important place in medical image analysis, and still remains a challenge task because of the complex background and structure. Materials and methods This paper proposed a novel active contour model based on localizing region for ROI segmentation in capsule endoscopy images. Features in regions centered on an active contour were used to compute the local region descriptors. For calculating the local energies, the image was separated by the initial circular shape curve into two parts: interior and exterior. And then each local region is fitted with a model to optimize the energies. Results Experiments show that in term of the average over segmentation rate, the proposed method is 2.8%, whereas traditional snake and GVF snake model are 3.2% and 3%, respectively; In term of the average under segmentation rate, the proposed method is 2.4%, whereas traditional snake and GVF snake model are 2.9% and 2.6%, respectively. All results demonstrated the superior of the proposed method to other existing methods in ROI segmentation. Acknowledgement This work is partially supported by the grant from the National Natural Science Foundation of China (No.61401355 and 61202198).

12 Accuracy and effectiveness of the respiratory self-gating signal in 3D cardiac cine MRI Shuo Li1, 2, Yanchun Zhu2, Jie Yang2, Song Gao1, Nan Fu2, Shaode Yu2, Yaoqin Xie2 1 Medical Imaging Physics Laboratory, Peking University, Beijing, 100191, China; 2Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China Correspondence: Yaoqin Xie ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):12 Aims Cardiac and respiratory self-gated free-breathing steady-state free procession (SSFP) has been proposed as an alternative to conventional SSFP for cardiac cine magnetic resonance imaging. In this


technique, the acquired k-space data within the given respiratory gating windows are used for image reconstruction. Therefore, the accuracy of respiratory self-gating (RSG) signal is important. Materials and methods The self-gated free-breathing 3D SSFP technique was performed on a 1.5T GE HDx scanner. Twenty five healthy volunteers were included. The imaging parameters were: TR/TE = 3.5/1.3 ms, flip angle = 40°, bandwidth = ±125 kHz, slice thickness = 7 mm (no gap), number of slices = 1214, number of profiles = 5000. RSG and respiratory bellow (RB) triggers were compared by correlation and t-test analyses. The percentage of respiratory signal intensity within gating windows was calculated. Results The respiratory cycle duration is 3314.7 ± 1072.6 ms. For all cases, the correlation coefficient between RSG and RB triggers is greater than 0.99, the P value of t-test is greater than 0.90. The percentage of RSG signal intensity within gating windows was 66.1 ± 4.1% compared with 60.1 ± 3.4% for RB. Conclusions There was an excellent correlation between RSG and RB triggers. There was no significant difference between two methods. RSG signal can well synchronize with RB signal and provide approximately the same respiratory cycle duration. Acknowledgements Supported by 2015AA043203, 81501463, 2011S013, 2014A030310360, JCYJ201500731154850923, JCYJ20130401170306812, JCYJ20140417113430585, JCYJ20140417113430639.

13 Changes in fiber bundles with aging: a tractography-based MRI study Yaping Wang1,3, Guixue Liu1,3, Wensheng Li1,2,3 1 Department of Human Anatomy and Histoembryology, Shanghai Medical College, Fudan University, Shanghai 200032, China; 2Digital Medical Research Center, Shanghai of Basic Medical Sciences, Fudan University, Shanghai 200032, China; 3Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China Correspondence: Wensheng Li ([email protected]) BMC Medical Imaging 2016, 16(Suppl 1):13 Aims The aim of this work was to investigate changes in fiber bundles with aging by utilizing quantified diffusion magnetic resonance imaging (MRI). Materials and methods A total of 125 normal subjects were separated into 5 groups (group 1: 16-30 years old, n = 20; group 2: 31-45 years old, n = 34; group 3: 46-60 years old, n = 24; group 4: 61-75 years old, n = 22; group 5: 7690 years old, n = 25). All subjects underwent diffusion tensor imaging (DTI) and T1-weighted MRI in a 3T scanner, and DTI Studio software was used to process all DTI data and for tracing fiber bundles. Statistics for the total fiber number of brain and for the fiber density (FD) of 3 regions of interest (ROIs), namely the corpus callosum, cingulate, mesencephalon were gathered and analyzed using SPSS software. Results Significant differences were observed in total fiber number among all age groups (p < 0.05). In group 1, a significant difference was found between the FD of left and right cingulate (p < 0.05). Significant differences were found in comparisons of the FD of left and right cingulate (p < 0.05), and the downward trend of the left cingulate was found to be faster than that of right cingulate. Furthermore, significant differences were found between the FD of corpus callosum and cingulate (p < 0.05). Conclusions Thus, we can use quantitative MR DTI to study changes in brain fiber bundles. Acknowledgments This study was supported by the National Science and Technology Support Program (No.2015BAK31B01). Keywords diffusion tensor imaging; tractography; fiber bundle; aging

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14 Ultrasonographic assessment of bony roof ratio in infant hip joints Changyu Tu ([email protected]) Department of Ultrasound Diagnosis, Women and Children’s Hospital of Linyi, Linyi City, Shandong 276001, China BMC Medical Imaging 2016, 16(Suppl 1):14 Aims This paper conducted research on a new method of ultrasound pediatric hip - bone top ratio measurement. Material and methods 390 cases of pediatric hip (hip) ultrasound examination were selected since March 2011 to August 2016 according to Graf method of measuring the size of the angle α. They were divided into three groups: Group 1, α angle ≥60 °, 130 cases; Group 2, α angle