Positron Emission Particle Tracking : A Comprehensive Guide

• Autore: Windows-Yule Kit
• Pubblicazione: Bristol : , : Institute of Physics Publishing, , 2022; ©2022
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (699 pages)
• Soggetto topico: Positron beams; Tracers (Chemistry)
• Altri autori: ParkerDavid MangerSamuel NicuşanAndrei L HeraldMatthew T
• Nota di contenuto: Intro -- Preface -- Acknowledgement -- Author biographies -- Kit Windows-Yule -- Leonard Nicuşan -- Matthew T Herald -- Samuel Manger -- David Parker -- Chapter 0 Using the book -- 0.1 The 'User' -- 0.2 The 'Researcher' -- 0.3 The 'Developer' -- 0.4 The 'Expert' -- Chapter 1 Imaging particulate and multiphase systems -- 1.1 Particulate and multiphase systems: why do they matter? -- 1.2 The importance of imaging -- 1.3 Particle and flow imaging: an overview -- References -- Chapter 2 The fundamentals of PEPT -- 2.1 Positron emission… -- 2.2 …particle tracking -- 2.2.1 Interactive example: PEPT-an idealised case -- PEPT: an idealised case -- Monte Carlo line of response generation -- Triangulate tracer's location -- Spatial error versus number of LoRs used -- 2.3 A more realistic picture -- 2.3.1 Issue 1: false coincidences -- 2.3.2 Issue 2: positron flight -- 2.3.3 Issue 3: imperfect detectors -- 2.3.4 Issue 4: finite detection rate -- 2.3.5 The real picture -- 2.3.6 Interactive example: sources of error in PEPT -- Sources of error in PEPT -- Monte Carlo line of response generation -- Adding noise: scattered events and spread -- Triangulate tracer's location -- Spatial error versus noise ratio -- 2.4 Not just particle tracking -- References -- Chapter 3 A history of PEPT -- 3.1 Adding the 'P': from PET to PEPT (origins to 1989) -- 3.2 MWPC PEPT (1989-1999) -- 3.3 Out with the old, in with the new (1999-2002) -- 3.4 Positron cameras of all shapes and sizes (2002-present) -- 3.5 PEPT elsewhere -- 3.6 The future-SuperPEPT, MicroPEPT and beyond -- References -- Chapter 4 Comparison with other techniques -- 4.1 Quasi-two-dimensional techniques -- 4.1.1 Particle tracking velocimetry (PTV) -- 4.1.2 Particle imaging velocimetry (PIV) -- 4.1.3 Photoelastic imaging -- 4.1.4 X-ray radiography -- 4.2 Tomographic techniques -- 4.2.1 X-ray computed tomography. ; 4.2.2 Single-photon emission computed tomography (SPECT) -- 4.2.3 Positron emission tomography (PET) -- 4.2.4 Electrical capacitance tomography (ECT) -- 4.2.5 Magnetic resonance imaging (MRI) -- 4.2.6 Refractive index matched scanning (RIMS) -- 4.3 Three-dimensional particle-tracking techniques -- 4.3.1 Radioactive particle tracking (RPT) -- 4.3.2 Magnetic particle tracking (MPT) -- 4.4 Non-imaging techniques -- Angle of repose (AOR) tests -- Tapped density tests-the Carr index and Hausner ratio -- Flow through an orifice -- Shear testing -- Powder rheometry -- 4.5 Numerical simulation -- 4.5.1 The discrete element method (DEM) -- A simple discrete element method simulation -- Define system of differential equations -- Numerically solving the ODE: Euler method -- Numerically solving the ODE: adaptive integration schemes -- 4.5.2 Computational fluid dynamics (CFD) -- 4.5.3 The Monte Carlo method -- A simple Monte Carlo simulation -- How many random samples? -- 4.6 Other techniques -- References -- Chapter 5 Tracers and detectors -- 5.1 Creating tracers -- 5.1.1 Introduction-the ideal tracer -- 5.1.2 Positron-emitting nuclides and direct activation -- 5.1.3 Indirect activation -- 5.1.4 Handling and coating -- 5.2 Detector systems -- 5.2.1 Introduction -- 5.2.2 Scintillation detectors -- 5.2.3 PEPT system geometries -- 5.2.4 Examples of PEPT systems -- 5.2.5 Future developments -- 5.3 Modelling PEPT systems -- 5.3.1 How it works -- 5.3.2 Existing GATE models -- 5.3.3 Modelling a PEPT detector -- 5.3.4 Defining a PEPT tracer -- 5.3.5 Recreating an experiment -- 5.3.6 Studying a PEPT system using GATE -- References -- Chapter 6 Pre-processing: PEPT data and algorithms -- 6.1 Understanding PEPT data -- 6.1.1 Interactive example: PEPT data format -- PEPT Data Format -- Initialise raw line of response data -- Visualising a sample of LoRs. ; Temporal resolution? -- 6.2 Available algorithms -- 6.2.1 The Birmingham algorithm -- 6.2.2 Interactive example: the Birmingham algorithm -- Interactive PEPT analysis example using the Birmingham method [1] -- This Jupyter Notebook -- Initialise raw line of response data -- Find minimum distance point -- Remove the farthest lines of response -- Iteratively remove the farthest LoRs and recompute MDP -- Complete Birmingham Method code -- High-performance Birmingham Method implementation -- 6.2.3 The line-density method -- 6.2.4 Interactive example: the line-density method -- Interactive PEPT analysis example using the line density algorithm [4] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Fit 1D Gaussians around the peak -- Complete line density method code -- 6.2.5 The G-means clustering algorithm -- 6.2.6 Interactive example: the G-means algorithm -- Interactive PEPT analysis example using the clustering (G-means) algorithm [6] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- High pass filter -- Cluster voxels with G-means -- Complete clustering (G-means) algorithm code -- Multiple particle tracking -- 6.2.7 Feature-point identification (FPI) -- 6.2.8 Interactive example: FPI -- Interactive PEPT analysis example using the feature point identification algorithm [17] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Subtract convolved matrix and blur -- Extract voxel peaks -- Complete FPI algorithm code -- Multiple particle tracking -- High-performance FPI algorithm implementation -- 6.2.9 Spatiotemporal B-spline reconstruction (SBSR) -- 6.2.10 Voronoi tesselation method -- 6.2.11 Interactive example: Voronoi tesselation. ; Interactive PEPT analysis example using the Voronoi tesselation method [30] -- This Jupyter Notebook -- Initialise raw line of response data -- Discretise the lines of response -- Voronoi tesselation -- Gather points under consideration (PUCs) -- Local filtering based on the local outlier factor -- Global filtering -- Clustering the remaining PUCs -- Extract final tracer locations -- Complete Voronoi tesselation algorithm code -- 6.2.12 The triangulation method -- 6.2.13 Interactive example: triangulation method -- Interactive PEPT analysis example using the triangulation method [37] -- This Jupyter Notebook -- Initialise raw line of response data -- Calculate the LoR distance matrix -- Cluster LoRs closer than the tracer radius -- Find centroids of clustered LoRs' cutpoints -- Complete triangulation method code -- 6.2.14 PEPT using machine learning (PEPT-ML) -- 6.2.15 Interactive example: PEPT-ML -- Interactive PEPT analysis example using the PEPT-ML algorithm [38] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints -- Cluster cutpoints with HDBSCAN -- Compute cluster centres -- Complete PEPT-ML algorithm code -- Second pass of clustering -- Multiple particle tracking -- High-performance PEPT-ML algorithm implementation -- 6.2.16 PEPT using expectation-maximisation (PEPT-EM) -- 6.2.17 Interactive example: PEPT-EM -- Interactive PEPT analysis examples using PEPT-EM [44] -- This Jupyter Notebook -- Initialise Raw Line of Response Data -- Calculate MDP and assign a weight to each LoR -- Recalculate MDP with previous weights -- Complete PEPT-EM algorithm code -- 6.2.18 The K-medoids method -- 6.2.19 Interactive example: K-medoids method -- Interactive PEPT analysis example using the K-medoids method [45] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints. ; Filter cutpoints with far nearest neighbors -- Cluster filtered cutpoints using K-medoids -- Compute clusters' centroids -- Complete K-medoids method mode -- Multiple particle pracking -- 6.2.20 The multiple location-allocation algorithm (MLAA) -- 6.2.21 Interactive example: the multiple location-allocation algorithm (MLAA) -- Interactive PEPT analysis example using the multiple location-allocation algorithm (MLAA) [49] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Voxel global thresholding -- The location-allocation algorithm -- Complete multiple location-allocation algorithm code -- Multiple particle tracking -- 6.3 From finding tracers to tracking trajectories -- 6.3.1 Interactive example: the effects of sample size and overlap -- Effect of sample size and overlap -- Prelude -- Initialise raw line of response data -- Effect of sample size -- Effect of overlap -- 6.3.2 Trajectory extraction -- 6.3.3 Interactive example: filtering trajectories -- Filtering trajectories -- Initialising lines of response -- Filtering based on spatial error -- Filtering based on nearest neighbours -- Filtering using the PEPT Library -- 6.3.4 Interactive example: separating trajectories -- Separating trajectories -- Initialising lines of response -- PTV-based trajectory separation -- Clustering-based trajectory separation -- 6.4 Horses for courses: comparing algorithm capability for differing tasks -- References -- Chapter 7 Post-processing: extracting physical information from PEPT data -- 7.1 Particle trajectories -- 7.1.1 Single-particle trajectories and their interpretation -- 7.1.2 Interactive example: plotting single particle trajectories -- Tutorial: Using PEPT data to plot single particle trajectories -- Setting up -- Importing data -- Plotting data -- Using the pept library. ; 7.1.3 Interactive example: multiple-particle data.
• Sommario/riassunto: This book provides both an accessible introduction to, and a comprehensive overview of, the PEPT technique, replete with interactive examples, usable algorithms and real PEPT data, allowing the reader to gain a deep understanding and practical, working knowledge of the methodology.
• Titolo autorizzato: Positron Emission Particle Tracking
• ISBN: 9780750344135; 075034413X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911009379403321
• Lo trovi qui: Univ. Federico II
• Serie: IOP Ebooks Series