Download or read book Novel Denoising Methods for Dynamic Positron Emission Tomography written by and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Positron emission tomography (PET) provides inherently quantitative information about physiological and molecular processes, endowing it with great clinical and research potential. This is particularly true of dynamic PET imaging. Unfortunately, PET, and especially dynamic PET, suffers from unfavorable noise properties, limiting it diagnostically and quantitatively. Denoising methods that improve image quality and thus increase diagnostic accuracy and improve estimates of quantitative parameters could be of great benefit, particularly if they are simple, accurate, and easily implemented on a wide range of PET tracer studies. The aim of this thesis is to develop and evaluate two novel denoising methods for dynamic PET imaging: HighlY constrained back-Projection-Local Reconstruction (HYPR-LR), which has recently been applied to dynamic PET data with promising results, and spatio-temporal expectation maximization (STEM) filtering, a newly developed 4-dimensional iterative filtering process. An implementation of HYPR-LR is presented that provides the maximum amount of noise reduction that is possible without introducing any significant bias. This is accomplished using multiple time-dependent temporally summed composite images that account for the kinetics of the tracer being studied. The potential of HYPR-LR to improve dynamic PET imaging is demonstrated using phantom, simulated, and human data, with a focus on quantitative parametric images. The newly proposed STEM filtering combines two well established image processing techniques: 4-dimensional Gaussian smoothing followed by EM deconvolution. In principle, this approach should provide substantial reductions in noise while introducing little bias. STEM filtering is also evaluated using phantom, simulated, and human data, with a focus on parametric images. The potential of HYPR-LR and STEM filtering to improve PET imaging of [I-124] labeled agents is also studied. [I-124] could be a valuable radionuclide for PET imaging, but its use is often limited by noise because of dosimetry concerns and relatively few decays by positron emission. Finally, the impact of a more traditional means of controlling image noise at the cost of bias, varying the number of iterations performed during EM reconstruction, on the diagnosis of temporal lobe epilepsy is studied. This also serves as an illustration of how HYPR-LR and STEM filtering might be evaluated in a clinical context.

Download or read book Autograph Letter Signed fragment from C B LeRow Vassar College Poughkeepsie to Richard Grant White written by and published by . This book was released on 1876 with total page 2 pages. Available in PDF, EPUB and Kindle. Book excerpt: Page 3 only of a letter. Refers to act 4, scene 3 of Julius Caesar involving Brutus and Messala. Signed "Miss C.B. LeRow (Teacher of Elocution and Rhetoric)."

Download or read book Artificial Intelligence and PET Imaging Part 1 An Issue of PET Clinics written by Babak Saboury and published by Elsevier Health Sciences. This book was released on 2021-09-21 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and PET Imaging, Part 1, An Issue of PET Clinics, E-Book

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2013 written by Kensaku Mori and published by Springer. This book was released on 2013-09-20 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 8149, 8150, and 8151 constitutes the refereed proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, held in Nagoya, Japan, in September 2013. Based on rigorous peer reviews, the program committee carefully selected 262 revised papers from 789 submissions for presentation in three volumes. The 95 papers included in the first volume have been organized in the following topical sections: physiological modeling and computer-assisted intervention; imaging, reconstruction, and enhancement; registration; machine learning, statistical modeling, and atlases; computer-aided diagnosis and imaging biomarkers; intraoperative guidance and robotics; microscope, optical imaging, and histology; cardiology, vasculatures and tubular structures; brain imaging and basic techniques; diffusion MRI; and brain segmentation and atlases.

Download or read book Improving PET Image Quality by System Modeling Anatomical Information and Geometry Design written by Kuang Gong and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. Though PET has high specificity and sensitivity compared with other imaging modalities, the signal-to-noise ratio (SNR) of PET image is still low due to the limits in the spatial resolution and number of coincidence-photons detected during a given scan time. This thesis focuses on improving PET image quality by developing new methods for accurate system modeling and incorporating anatomical information from MR (Magnetic Resonance), and novel geometry design. The first part of the thesis develops a new method for system modeling. An accurate system matrix is essential in PET for reconstructing high quality images. For PET scanners with a long axial field of view (FOV), inter-crystal penetration and scatters not only result in radial elongation, but also reduce axial resolution and four-dimensional blurring kernels are needed to model the sinogram blurring. Estimation of the 4D axial-variant sinogram blurring matrix is an ill-conditioned problem due to the large number of unknowns. Here we propose a rank-one approximation for each row of the sinogram blurring matrix to improve the stability of the axial-variant blurring matrix estimation. The second part focus on incorporating prior information into PET denoising and reconstruction. With the recent development of combined PET/MR systems, we can improve the PET image quality by including MR information. Here we combine both the PET and MRI information to improve the quality of dynamic PET reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. The proposed method is applied to direct reconstruction for both linear and nonlinear models. Apart from prior MR information, prior patients' high count data can also be used to improve PET image quality through the deep neural network framework as shown in this work. Finally, to improve the sensitivity of PET imaging, we study novel PET scanner geometries. Here we focus on brain imaging applications and propose a compact 'helmet' design based on the analysis of different system designs. As the proposed geometry has irregular geometry, system modeling based on multi-ray-on-the-fly calculation is used. Results show that the proposed geometry can achieve four-fold higher sensitivity and also resolve smaller features than existing cylindrical brain PET scanners.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2018 written by Alejandro F. Frangi and published by Springer. This book was released on 2018-09-13 with total page 918 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2019 written by Dinggang Shen and published by Springer Nature. This book was released on 2019-10-10 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.

Download or read book Post reconstruction Image Denoising and Artefact Removal for Low Count Positron Emission Tomography written by Casper da Costa-Luis and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Wavelet Phase Filter for Emission Tomography written by and published by . This book was released on 1995 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography (PET). Wavelet methods can smooth out noise while preserving significant features of images. Mallat et al. proposed a wavelet based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, the authors explore the properties of wavelet phase, with a focus on reconstruction of emission tomography images. Specifically, they show that the wavelet phase of regular Poisson noise under a Haar-type wavelet transform converges in distribution to a random variable uniformly distributed on [0, 2[pi]). They then propose three wavelet-phase-based denoising schemes which exploit this property: edge tracking, local phase variance thresholding, and scale phase variation thresholding. Some numerical results are also presented. The numerical experiments indicate that wavelet phase techniques show promise for wavelet based denoising methods.

Download or read book Attenuation Correction Methods for Positron Emission Tomography written by Chen-Hsien Wu and published by . This book was released on 1998 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Event Driven Motion Compensation in Positron Emission Tomography Development of a Clinically Applicable Method written by and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Positron emission tomography (PET) is a well-established functional imaging method used in nuclear medicine. It allows for retrieving information about biochemical and physiological processes in vivo. The currently possible spatial resolution of PET is about 5 mm for brain acquisitions and about 8 mm for whole-body acquisitions, while recent improvements in image reconstruction point to a resolution of 2 mm in the near future. Typical acquisition times range from minutes to hours due to the low signal-to-noise ratio of the measuring principle, as well as due to the monitoring of the metabolism of the patient over a certain time. Therefore, patient motion increasingly limits the possible spatial resolution of PET. In addition, patient immobilisations are only of limited benefit in this context. Thus, patient motion leads to a relevant resolution degradation and incorrect quantification of metabolic parameters. The present work describes the utilisation of a novel motion compensation method for clinical brain PET acquisitions. By using an external motion tracking system, information about the head motion of a patient is continuously acquired during a PET acquisition. Based on the motion information, a newly developed event-based motion compensation algorithm performs spatial transformations of all registered coincidence events, thus utilising the raw data of a PET system - the so-called `list-modeþ data. For routine acquisition of this raw data, methods have been developed which allow for the first time to acquire list-mode data from an ECAT Exact HR+ PET scanner within an acceptable time frame. Furthermore, methods for acquiring the patient motion in clinical routine and methods for an automatic analysis of the registered motion have been developed. For the clinical integration of the aforementioned motion compensation approach, the development of additional methods (e.g. graphical user interfaces) was also part of this work. After development, optimisation and integ.

Download or read book Reconstruction Quantification and Standardization Methods in Positron Emission Tomography written by and published by . This book was released on 2008 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Congress on Intelligent Systems written by Mukesh Saraswat and published by Springer Nature. This book was released on 2022-06-30 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of selected papers presented at the Second Congress on Intelligent Systems (CIS 2021), organized by Soft Computing Research Society and CHRIST (Deemed to be University), Bengaluru, India during September 4 – 5, 2021. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It covers topics such as Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro fuzzy systems.

Download or read book A Fast Nonlinear Method for Parametric Imaging of Positron Emission Tomography Data written by S. Raymond Golish and published by . This book was released on 2002 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Method for Correcting Head Motion Artifact in Positron Emission Tomography written by Osama Mawlawi and published by . This book was released on 1999 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Electron Radiography written by Arthur I. Berman and published by . This book was released on 1950 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Dictionary based Denoising Method Toward a Robust Segmentation of Noisy and Densely Packed Nuclei in 3D Biological Microscopy Images written by Lamees Nasser Khalafallah Mahmoud and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cells are the basic building blocks of all living organisms. All living organisms share life processes such as growth and development, movement, nutrition, excretion, reproduction, respiration and response to the environment. In cell biology research, understanding cells structure and function is essential for developing and testing new drugs. In addition, cell biology research provides a powerful tool to study embryo development. Furthermore, it helps the scientific research community to understand the effects of mutations and various diseases. Time-Lapse Fluorescence Microscopy (TLFM) is one of the most appreciated imaging techniques which can be used in live-cell imaging experiments to quantify various characteristics of cellular processes, i.e., cell survival, proliferation, migration, and differentiation. In TLFM imaging, not only spatial information is acquired, but also temporal information obtained by repeating imaging of a labeled sample at specific time points, as well as spectral information, that produces up to five-dimensional (X, Y, Z + Time + Channel) images. Typically, the generated datasets consist of several (hundreds or thousands) images, each containing hundreds to thousands of objects to be analyzed. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel 3D denoising algorithm, based on unsupervised dictionary learning and sparse representation, that can both enhance very faint and noisy nuclei, in addition, it simultaneously detects nuclei position accurately. Furthermore, our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. The framework of the proposed method comprises image denoising, nuclei detection, and segmentation. In the denoising step, an initial dictionary is constructed by selecting random patches from the raw image then an iterative technique is implemented to update the dictionary and obtain the final one which is less noisy. Next, a detection map, based on the dictionary coefficients used to denoise the image, is used to detect marker points. Afterward, a thresholding-based approach is proposed to get the segmentation mask. Finally, a marker-controlled watershed approach is used to get the final nuclei segmentation result. We generate 3D synthetic images to study the effect of the few parameters of our method on cell nuclei detection and segmentation, and to understand the overall mechanism for selecting and tuning the significant parameters of the several datasets. These synthetic images have low contrast and low signal to noise ratio. Furthermore, they include touching spheres where these conditions simulate the same characteristics exist in the real datasets. The proposed framework shows that integrating our denoising method along with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, two datasets from the cell tracking challenge are extensively tested. Across all datasets, the proposed method achieved very promising results with 96.96% recall for the C.elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99.3%).