International Journal of Biomedical Imaging最新文献_第10页

The Capabilities and Limitations of Clinical Magnetic Resonance Imaging for Detecting Kidney Stones: A Retrospective Study 临床磁共振成像检测肾结石的能力和局限性:一项回顾性研究

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-11-01 DOI: 10.1155/2016/4935656

El-Sayed H. Ibrahim, Joseph G. Cernigliaro, M. Bridges, R. Pooley, W. Haley

{"title":"The Capabilities and Limitations of Clinical Magnetic Resonance Imaging for Detecting Kidney Stones: A Retrospective Study","authors":"El-Sayed H. Ibrahim, Joseph G. Cernigliaro, M. Bridges, R. Pooley, W. Haley","doi":"10.1155/2016/4935656","DOIUrl":"https://doi.org/10.1155/2016/4935656","url":null,"abstract":"The purpose of this work was to investigate the performance of currently available magnetic resonance imaging (MRI) for detecting kidney stones, compared to computed tomography (CT) results, and to determine the characteristics of successfully detected stones. Patients who had undergone both abdominal/pelvic CT and MRI exams within 30 days were studied. The images were reviewed by two expert radiologists blinded to the patients' respective radiological diagnoses. The study consisted of four steps: (1) reviewing the MRI images and determining whether any kidney stone(s) are identified; (2) reviewing the corresponding CT images and confirming whether kidney stones are identified; (3) reviewing the MRI images a second time, armed with the information from the corresponding CT, noting whether any kidney stones are positively identified that were previously missed; (4) for all stones MRI-confirmed on previous steps, the radiologist experts being asked to answer whether in retrospect, with knowledge of size and location on corresponding CT, these stones would be affirmed as confidently identified on MRI or not. In this best-case scenario involving knowledge of stones and their locations on concurrent CT, radiologist experts detected 19% of kidney stones on MRI, with stone size being a major factor for stone identification.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2016 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/4935656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64404153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

An Analytical Approach for Fast Recovery of the LSI Properties in Magnetic Particle Imaging 磁颗粒成像中快速恢复LSI性能的分析方法

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-10-01 DOI: 10.1155/2016/6120713

H. Jabbari, Jungwon Yoon

{"title":"An Analytical Approach for Fast Recovery of the LSI Properties in Magnetic Particle Imaging","authors":"H. Jabbari, Jungwon Yoon","doi":"10.1155/2016/6120713","DOIUrl":"https://doi.org/10.1155/2016/6120713","url":null,"abstract":"Linearity and shift invariance (LSI) characteristics of magnetic particle imaging (MPI) are important properties for quantitative medical diagnosis applications. The MPI image equations have been theoretically shown to exhibit LSI; however, in practice, the necessary filtering action removes the first harmonic information, which destroys the LSI characteristics. This lost information can be constant in the x-space reconstruction method. Available recovery algorithms, which are based on signal matching of multiple partial field of views (pFOVs), require much processing time and a priori information at the start of imaging. In this paper, a fast analytical recovery algorithm is proposed to restore the LSI properties of the x-space MPI images, representable as an image of discrete concentrations of magnetic material. The method utilizes the one-dimensional (1D) x-space imaging kernel and properties of the image and lost image equations. The approach does not require overlapping of pFOVs, and its complexity depends only on a small-sized system of linear equations; therefore, it can reduce the processing time. Moreover, the algorithm only needs a priori information which can be obtained at one imaging process. Considering different particle distributions, several simulations are conducted, and results of 1D and 2D imaging demonstrate the effectiveness of the proposed approach.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2016 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/6120713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64459066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-09-01 DOI: 10.1155/2016/7570406

J. Klock, E. Iuanow, Bilal H. Malik, N. Obuchowski, J. Wiskin, M. Lenox

{"title":"Anatomy-Correlated Breast Imaging and Visual Grading Analysis Using Quantitative Transmission Ultrasound™","authors":"J. Klock, E. Iuanow, Bilal H. Malik, N. Obuchowski, J. Wiskin, M. Lenox","doi":"10.1155/2016/7570406","DOIUrl":"https://doi.org/10.1155/2016/7570406","url":null,"abstract":"Objectives. This study presents correlations between cross-sectional anatomy of human female breasts and Quantitative Transmission (QT) Ultrasound, does discriminate classifier analysis to validate the speed of sound correlations, and does a visual grading analysis comparing QT Ultrasound with mammography. Materials and Methods. Human cadaver breasts were imaged using QT Ultrasound, sectioned, and photographed. Biopsies confirmed microanatomy and areas were correlated with QT Ultrasound images. Measurements were taken in live subjects from QT Ultrasound images and values of speed of sound for each identified anatomical structure were plotted. Finally, a visual grading analysis was performed on images to determine whether radiologists' confidence in identifying breast structures with mammography (XRM) is comparable to QT Ultrasound. Results. QT Ultrasound identified all major anatomical features of the breast, and speed of sound calculations showed specific values for different breast tissues. Using linear discriminant analysis overall accuracy is 91.4%. Using visual grading analysis readers scored the image quality on QT Ultrasound as better than on XRM in 69%–90% of breasts for specific tissues. Conclusions. QT Ultrasound provides accurate anatomic information and high tissue specificity using speed of sound information. Quantitative Transmission Ultrasound can distinguish different types of breast tissue with high resolution and accuracy.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"38 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/7570406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64525727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing 基于归一化卷积和去噪的视网膜眼底图像增强

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-09-01 DOI: 10.1155/2016/5075612

Peishan Dai, Hanwei Sheng, Jianmei Zhang, Ling Li, Jing Wu, Min Fan

{"title":"Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing","authors":"Peishan Dai, Hanwei Sheng, Jianmei Zhang, Ling Li, Jing Wu, Min Fan","doi":"10.1155/2016/5075612","DOIUrl":"https://doi.org/10.1155/2016/5075612","url":null,"abstract":"Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2016 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/5075612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64410629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns 利用归一化壁厚时间模式从MRI图像中检测心功能异常

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-03-01 DOI: 10.1155/2016/4301087

M. Wael, El-Sayed H. Ibrahim, A. Fahmy

{"title":"Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns","authors":"M. Wael, El-Sayed H. Ibrahim, A. Fahmy","doi":"10.1155/2016/4301087","DOIUrl":"https://doi.org/10.1155/2016/4301087","url":null,"abstract":"Purpose. To develop a method for identifying abnormal myocardial function based on studying the normalized wall motion pattern during the cardiac cycle. Methods. The temporal pattern of the normalized myocardial wall thickness is used as a feature vector to assess the cardiac wall motion abnormality. Principal component analysis is used to reduce the feature dimensionality and the maximum likelihood method is used to differentiate between normal and abnormal features. The proposed method was applied on a dataset of 27 cases from normal subjects and patients. Results. The developed method achieved 81.5%, 85%, and 88.5% accuracy for identifying abnormal contractility in the basal, midventricular, and apical slices, respectively. Conclusions. A novel feature vector, namely, the normalized wall thickness, has been introduced for detecting myocardial regional wall motion abnormality. The proposed method provides assessment of the regional myocardial contractility for each cardiac segment and slice; therefore, it could be a valuable tool for automatic and fast determination of regional wall motion abnormality from conventional cine MRI images.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2016 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/4301087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64378449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping 基于异构对象原型的数字微滴喷射技术

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-02-15 DOI: 10.1155/2016/5057347

Na Li, Jiaquan Yang, Chunmei Feng, Jianfei Yang, Li-ya Zhu, Aiqing Guo

引用次数: 7

Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment 多核环境下超声医学图像的并行数字水印处理

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-02-11 DOI: 10.1155/2016/9583727

Liang Khor Hui, Siau-Chuin Liew, J. Zain

引用次数: 12

3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation 利用三维造影剂分布估计心脏结构的三维/二维配准

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-01-22 DOI: 10.1155/2016/7690391

M. Hoffmann, Christopher Kowalewski, A. Maier, K. Kurzidim, Norbert Strobel, J. Hornegger

{"title":"3-D/2-D Registration of Cardiac Structures by 3-D Contrast Agent Distribution Estimation","authors":"M. Hoffmann, Christopher Kowalewski, A. Maier, K. Kurzidim, Norbert Strobel, J. Hornegger","doi":"10.1155/2016/7690391","DOIUrl":"https://doi.org/10.1155/2016/7690391","url":null,"abstract":"For augmented fluoroscopy during cardiac catheter ablation procedures, a preoperatively acquired 3-D model of the left atrium of the patient can be registered to X-ray images. Therefore the 3D-model is matched with the contrast agent based appearance of the left atrium. Commonly, only small amounts of contrast agent (CA) are used to locate the left atrium. This is why we focus on robust registration methods that work also if the structure of interest is only partially contrasted. In particular, we propose two similarity measures for CA-based registration: The first similarity measure, explicit apparent edges, focuses on edges of the patient anatomy made visible by contrast agent and can be computed quickly on the GPU. The second novel similarity measure computes a contrast agent distribution estimate (CADE) inside the 3-D model and rates its consistency with the CA seen in biplane fluoroscopic images. As the CADE computation involves a reconstruction of CA in 3-D using the CA within the fluoroscopic images, it is slower. Using a combination of both methods, our evaluation on 11 well-contrasted clinical datasets yielded an error of 7.9+/-6.3 mm over all frames. For 10 datasets with little CA, we obtained an error of 8.8+/-6.7 mm. Our new methods outperform a registration based on the projected shadow significantly (p<0.05).","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"abs/1601.06062 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/7690391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64533171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Insight into the Molecular Imaging of Alzheimer's Disease 洞察阿尔茨海默病的分子成像

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-01-10 DOI: 10.1155/2016/7462014

A. Arora, Neeta Bhagat

{"title":"Insight into the Molecular Imaging of Alzheimer's Disease","authors":"A. Arora, Neeta Bhagat","doi":"10.1155/2016/7462014","DOIUrl":"https://doi.org/10.1155/2016/7462014","url":null,"abstract":"Alzheimer's disease is a complex neurodegenerative disease affecting millions of individuals worldwide. Earlier it was diagnosed only via clinical assessments and confirmed by postmortem brain histopathology. The development of validated biomarkers for Alzheimer's disease has given impetus to improve diagnostics and accelerate the development of new therapies. Functional imaging like positron emission tomography (PET), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), and proton magnetic resonance spectroscopy provides a means of detecting and characterising the regional changes in brain blood flow, metabolism, and receptor binding sites that are associated with Alzheimer's disease. Multimodal neuroimaging techniques have indicated changes in brain structure and metabolic activity, and an array of neurochemical variations that are associated with neurodegenerative diseases. Radiotracer-based PET and SPECT potentially provide sensitive, accurate methods for the early detection of disease. This paper presents a review of neuroimaging modalities like PET, SPECT, and selected imaging biomarkers/tracers used for the early diagnosis of AD. Neuroimaging with such biomarkers and tracers could achieve a much higher diagnostic accuracy for AD and related disorders in the future.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"12 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2016-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/7462014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64521764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems. c臂CT系统膝关节成像中过度曝光伪影的运动学校正。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2016-01-01 Epub Date: 2016-07-19 DOI: 10.1155/2016/2502486

Johannes Rausch, Andreas Maier, Rebecca Fahrig, Jang-Hwan Choi, Waldo Hinshaw, Frank Schebesch, Sven Haase, Jakob Wasza, Joachim Hornegger, Christian Riess

{"title":"Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems.","authors":"Johannes Rausch, Andreas Maier, Rebecca Fahrig, Jang-Hwan Choi, Waldo Hinshaw, Frank Schebesch, Sven Haase, Jakob Wasza, Joachim Hornegger, Christian Riess","doi":"10.1155/2016/2502486","DOIUrl":"https://doi.org/10.1155/2016/2502486","url":null,"abstract":"<p><p>Objective. To demonstrate a novel approach of compensating overexposure artifacts in CT scans of the knees without attaching any supporting appliances to the patient. C-Arm CT systems offer the opportunity to perform weight-bearing knee scans on standing patients to diagnose diseases like osteoarthritis. However, one serious issue is overexposure of the detector in regions close to the patella, which can not be tackled with common techniques. Methods. A Kinect camera is used to algorithmically remove overexposure artifacts close to the knee surface. Overexposed near-surface knee regions are corrected by extrapolating the absorption values from more reliable projection data. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates. Results. Artifacts at both knee phantoms are reduced significantly in the reconstructed data and a major part of the truncated regions is restored. Conclusion. The results emphasize the feasibility of the proposed approach. The accuracy of the cross-calibration procedure can be increased to further improve correction results. Significance. The correction method can be extended to a multi-Kinect setup for use in real-world scenarios. Using depth cameras does not require prior scans and offers the possibility of a temporally synchronized correction of overexposure artifacts. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates. </p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2016 ","pages":"2502486"},"PeriodicalIF":7.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/2502486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34749419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12