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 - 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 - BOOK T1 - (PhD Thesis) Brain Perfusion Imaging - Performance and Accuracy Y1 - 2012 A1 - Fan Zhu AB - Title: Brain Perfusion Imaging - Performance and Accuracy Abstract: Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. The purpose of my PhD research is to develop novel methodologies for improving the efficiency and quality of brain perfusion-imaging analysis so that clinical decisions can be made more accurately and in shorter time. This thesis consists of three parts: 1. My research investigates the possibilities that parallel computing brings to make perfusion-imaging analysis faster in order to deliver results that are used in stroke diagnosis earlier. Brain perfusion analysis using local Arterial Input Functions (AIF) technique takes a long time to execute due to its heavy computational load. As time is vitally important in the case of acute stroke, reducing analysis time and therefore diagnosis time can reduce the number of brain cells damaged and improve the chances for patient recovery. We present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose computing on Graphics Processing Units) using the CUDA programming model. Our method aims to accelerate the process without any quality loss. 2. Specific features of perfusion source images are also used to reduce noise impact, which consequently improves the accuracy of hemodynamic maps. 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) makes use of the temporal information in the perfusion source imges to reduce the noise level. Over the entire image, our noise reduction method based on Gaussian process regression gains a 99% contrast-to-noise ratio improvement over the raw image and also improves the quality of hemodynamic maps, allowing a better identification of edges and detailed information. At the level of individual voxels, GPR provides a stable baseline, helps identify key parameters from tissue time-concentration curves and reduces the oscillations in the curves. Furthermore, the results shows that GPR is superior to the alternative techniques compared in this study. 3. My research also explores automatic segmentation of perfusion images into potentially healthy areas and lesion areas which can be used as additional information that assists in clinical diagnosis. Since perfusion source images contain more information than hemodynamic maps, good utilisation of source images leads to better understanding than the hemodynamic maps alone. Correlation coefficient tests are used to measure the similarities between the expected tissue time-concentration curves (from (reference tissue)) and the measured time-concentration curves (from target tissue). This information is then used to distinguish tissues at risk and dead tissues from healthy tissues. A correlation coefficient based signal analysis method that directly spots suspected lesion areas from perfusion source images is presented. Our method delivers a clear automatic segmentation of healthy tissue, tissue at risk and dead tissue. From our segmentation maps, it is easier to identify lesion boundaries than using traditional hemodynamic maps. 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 -