TY - JOUR T1 - Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging JF - IEEE Journal of Biomedical and Health Informatics Y1 - 2013 A1 - Fan Zhu A1 - Rodríguez, David A1 - Carpenter, Trevor K. A1 - Atkinson, Malcolm P. A1 - Wardlaw, Joanna M. KW - CT , Pattern Recognition , Perfusion Source Images , Segmentation AB - Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilisation of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in MR perfusion images. VL - 17 IS - 5 ER - TY - JOUR T1 - Computed Tomography Perfusion Imaging Denoising Using Gaussian Process Regression JF - Physics in Medicine and Biology Y1 - 2012 A1 - Fan Zhu A1 - Carpenter, Trevor A1 - Rodríguez, David A1 - Malcolm Atkinson A1 - Wardlaw, Joanna AB - Objective: Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, Computed Tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. Methods: The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Results: Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time- concentration curves and reduces the oscillations in the curve. Conclusion: GPR is superior to the comparable techniques used in this study. ER - TY - CONF T1 - A databank, rather than statistical, model of normal ageing brain structure to indicate pathology T2 - OHBM 2012 Y1 - 2012 A1 - Dickie, David Alexander A1 - Dominic Job A1 - Rodríguez, David A1 - Shenkin, Susan A1 - Wardlaw, Joanna JF - OHBM 2012 UR - http://ww4.aievolution.com/hbm1201/index.cfm?do=abs.viewAbs&abs=5102 ER - TY - JOUR T1 - Parallel perfusion imaging processing using GPGPU JF - Computer Methods and Programs in Biomedicine Y1 - 2012 A1 - Fan Zhu A1 - Rodríguez, David A1 - Carpenter, Trevor A1 - Malcolm Atkinson A1 - Wardlaw, Joanna KW - Deconvolution KW - GPGPU KW - Local AIF KW - Parallelization KW - Perfusion Imaging AB - Background and purpose The objective of brain perfusion quantification is to generate parametric maps of relevant hemodynamic quantities such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) that can be used in diagnosis of acute stroke. These calculations involve deconvolution operations that can be very computationally expensive when using local Arterial Input Functions (AIF). As time is vitally important in the case of acute stroke, reducing the analysis time will reduce the number of brain cells damaged and increase the potential for recovery. Methods GPUs originated as graphics generation dedicated co-processors, but modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its large number of computing cores and constitutes an affordable high performance computing method. In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. We present the serial and parallel implementations of such algorithms and the evaluation of the performance gains using GPUs. Results Our method has gained a 5.56 and 3.75 speedup for CT and MR images respectively. Conclusions It seems that using GPGPU is a desirable approach in perfusion imaging analysis, which does not harm the quality of cerebral hemodynamic maps but delivers results faster than the traditional computation. UR - http://www.sciencedirect.com/science/article/pii/S0169260712001587 ER - TY - CONF T1 - A Parallel Deconvolution Algorithm in Perfusion Imaging T2 - Healthcare Informatics, Imaging, and Systems Biology (HISB) Y1 - 2011 A1 - Zhu, Fan. A1 - Rodríguez, David A1 - Carpenter, Trevor A1 - Malcolm Atkinson A1 - Wardlaw, Joanna KW - Deconvolution KW - GPGPU KW - Parallelization KW - Perfusion Imaging AB - In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. GPUs originated as graphics generation dedicated co-processors, but the modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its huge number of computing cores and constitutes an affordable high performance computing method. The objective of brain perfusion quantification is to generate parametric maps of relevant haemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that can be used in diagnosis of conditions such as stroke or brain tumors. These calculations involve deconvolution operations that in the case of using local Arterial Input Functions (AIF) can be very expensive computationally. We present the serial and parallel implementations of such algorithm and the evaluation of the performance gains using GPUs. JF - Healthcare Informatics, Imaging, and Systems Biology (HISB) CY - San Jose, California SN - 978-1-4577-0325-6 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6061411&tag=1 ER - TY - CONF T1 - RapidBrain: Developing a Portal for Brain Research Imaging T2 - All Hands Meeting 2011, York Y1 - 2011 A1 - Kenton D'Mellow A1 - Rodríguez, David A1 - Carpenter, Trevor A1 - Jos Koetsier A1 - Dominic Job A1 - van Hemert, Jano A1 - Wardlaw, Joanna A1 - Fan Zhu AB - Brain imaging researchers execute complex multistep workflows in their computational analysis. Those workflows often include applications that have very different user interfaces and sometimes use different data formats. A good example is the brain perfusion quantification workflow used at the BRIC (Brain Research Imaging Centre) in Edinburgh. Rapid provides an easy method for creating portlets for computational jobs, and at the same it is extensible. We have exploited this extensibility with additions that stretch the functionality beyond the original limits. These changes can be used by other projects to create their own portals, but it should be noted that the development of such portals involve a greater effort than the required in the regular use of Rapid for creating portlets. In our case it has been used to provide a user-friendly interface for perfusion analysis that covers from volume JF - All Hands Meeting 2011, York CY - York ER - TY - JOUR T1 - An open source toolkit for medical imaging de-identification JF - European Radiology Y1 - 2010 A1 - Rodríguez, David A1 - Carpenter, Trevor K. A1 - van Hemert, Jano I. A1 - Wardlaw, Joanna M. KW - Anonymisation KW - Data Protection Act (DPA) KW - De-identification KW - Digital Imaging and Communications in Medicine (DICOM) KW - Privacy policies KW - Pseudonymisation KW - Toolkit AB - Objective Medical imaging acquired for clinical purposes can have several legitimate secondary uses in research projects and teaching libraries. No commonly accepted solution for anonymising these images exists because the amount of personal data that should be preserved varies case by case. Our objective is to provide a flexible mechanism for anonymising Digital Imaging and Communications in Medicine (DICOM) data that meets the requirements for deployment in multicentre trials. Methods We reviewed our current de-identification practices and defined the relevant use cases to extract the requirements for the de-identification process. We then used these requirements in the design and implementation of the toolkit. Finally, we tested the toolkit taking as a reference those requirements, including a multicentre deployment. Results The toolkit successfully anonymised DICOM data from various sources. Furthermore, it was shown that it could forward anonymous data to remote destinations, remove burned-in annotations, and add tracking information to the header. The toolkit also implements the DICOM standard confidentiality mechanism. Conclusion A DICOM de-identification toolkit that facilitates the enforcement of privacy policies was developed. It is highly extensible, provides the necessary flexibility to account for different de-identification requirements and has a low adoption barrier for new users. VL - 20 UR - http://www.springerlink.com/content/j20844338623m167/ IS - 8 ER - TY - JOUR T1 - A Grid infrastructure for parallel and interactive applications JF - Computing and Informatics Y1 - 2008 A1 - Gomes, J. A1 - Borges, B. A1 - Montecelo, M. A1 - David, M. A1 - Silva, B. A1 - Dias, N. A1 - Martins, JP A1 - Fernandez, C. A1 - Garcia-Tarres, L. , A1 - Veiga, C. A1 - Cordero, D. A1 - Lopez, J. A1 - J Marco A1 - Campos, I. A1 - Rodríguez, David A1 - Marco, R. A1 - Lopez, A. A1 - Orviz, P. A1 - Hammad, A. VL - 27 IS - 2 ER - TY - JOUR T1 - The interactive European Grid: Project objectives and achievements JF - Computing and Informatics Y1 - 2008 A1 - J Marco A1 - Campos, I. A1 - Coterillo, I. A1 - Diaz, I. A1 - Lopez, A. A1 - Marco, R. A1 - Martinez-Rivero, C. A1 - Orviz, P. A1 - Rodríguez, David A1 - Gomes, J. A1 - Borges, G. A1 - Montecelo, M. A1 - David, M. A1 - Silva, B. A1 - Dias, N. A1 - Martins, JP A1 - Fernandez, C. A1 - Garcia-Tarres, L. VL - 27 IS - 2 ER - TY - Generic T1 - Experience with the international testbed in the crossgrid project T2 - Advances in Grid Computing-EGC 2005 Y1 - 2005 A1 - Gomes, J. A1 - David, M. A1 - Martins, J. A1 - Bernardo, L. A1 - A García A1 - Hardt, M. A1 - Kornmayer, H. A1 - Marco, Jesus A1 - Marco, Rafael A1 - Rodríguez, David A1 - Diaz, Irma A1 - Cano, Daniel A1 - Salt, J. A1 - Gonzalez, S. A1 - J Sánchez A1 - Fassi, F. A1 - Lara, V. A1 - Nyczyk, P. A1 - Lason, P. A1 - Ozieblo, A. A1 - Wolniewicz, P. A1 - Bluj, M. A1 - K Nawrocki A1 - A Padee A1 - W Wislicki ED - Peter M. A. Sloot, Alfons G. Hoekstra, Thierry Priol, Alexander Reinefeld ED - Marian Bubak JF - Advances in Grid Computing-EGC 2005 T3 - LNCS PB - Springer Berlin/Heidelberg CY - Amsterdam VL - 3470 ER - TY - CONF T1 - Organization of the International Testbed of the CrossGrid Project T2 - Cracow Grid Workshop 2005 Y1 - 2005 A1 - Gomes, J. A1 - David, M. A1 - Martins, J. A1 - Bernardo, L. A1 - Garcia, A. A1 - Hardt, M. A1 - Kornmayer, H. A1 - Marco, Rafael A1 - Rodríguez, David A1 - Diaz, Irma A1 - Cano, Daniel A1 - Salt, J. A1 - Gonzalez, S. A1 - Sanchez, J. A1 - Fassi, F. A1 - Lara, V. A1 - Nyczyk, P. A1 - Lason, P. A1 - Ozieblo, A. A1 - Wolniewicz, P. A1 - Bluj, M. JF - Cracow Grid Workshop 2005 ER -