Dynamic Dual-Tracer PET Reconstruction

Gao, Fei; Liu, Huafeng; Jian, Yiqiang; Shi, Peng

doi:10.1007/978-3-642-02498-6_4

Cited by 26 publications

(22 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since its proposal by Huang et al, 177 multiple studies have investigated techniques that could allow for multiplexing PET imaging. [178][179][180][181][182][183][184][185] Simultaneous acquisition of multiple tracers is a feasible solution, would eliminate the issues of image registration and physiological changes, and is highly suited to 18 F-FMISO hypoxia imaging. However, further research is required to fully develop the concept of simultaneous multiplexing of 18 F-FMISO with other PET oncology tracers.…”

Section: Discussionmentioning

confidence: 99%

Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation

Bell

Dowson

Fay

et al. 2015

Seminars in Nuclear Medicine

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation

Bell

Dowson

Fay

et al. 2015

Seminars in Nuclear Medicine

View full text Add to dashboard Cite

“…For dynamic PET data, directly reconstruct the parametric images from the raw projection data can achieve improved accuracy and robustness [9,18,19]. The sparse image representation is directly applicable to dynamic PET data as it is a linear operation in the image domain.…”

Section: Pet Reconstruction With Sparse Image Representationmentioning

confidence: 99%

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

Jiao

Markiewicz

Burgos

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Positron emission tomography (PET) reconstruction is an illposed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in the penalised maximum likelihood (PML) method to regularise the model complexity and selectively smooth the image on a voxel basis in PET reconstruction. In this work, we propose a novel perspective of incorporating the prior information by exploring the sparse property of natural images. Instead of a regular voxel grid, the sparse image representation jointly determined by the prior image and the PET data is used in reconstruction to leverage between the image details and smoothness, and this prior is integrated into the PET forward model and has a closed-form expectation maximisation (EM) solution. Simulations show that the proposed approach achieves improved bias versus variance trade-off and higher contrast recovery than the current state-of-the-art methods, and preserves the image details better. Application to clinical PET data shows promising results.

show abstract

“…Unlike multi-isotope SPECT, where energy discrimination can be used to identify photons from each tracer, all PET tracers give rise to indistinguishable 511 keV photon pairs. It has been previously shown that, when using dynamic scanning with staggered injections, the imaging signals from 2–3 PET tracers can be reliably recovered through application of appropriate kinetic constraints for each tracer (Koeppe et al 1998, Koeppe et al 2001, Converse et al 2004, Koeppe et al 2004, Kadrmas and Rust 2005, Kudomi et al 2005, Rust and Kadrmas 2006, Black et al 2008, 2009, Gao et al 2009, Joshi et al 2009, Kadrmas et al 2010, 2013, Kadrmas and Hoffman 2013). Perhaps the most robust multi-tracer PET signal-separation algorithms rely upon parallel compartment modeling of all tracers present in order to apply the kinetic constraints and recover imaging estimates from each individual tracer.…”

Section: Introductionmentioning

confidence: 99%

Application of separable parameter space techniques to multi-tracer PET compartment modeling

2016

View full text Add to dashboard Cite

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

show abstract

Dynamic Dual-Tracer PET Reconstruction

Cited by 26 publications

References 10 publications

Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation

Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

Application of separable parameter space techniques to multi-tracer PET compartment modeling

Contact Info

Product

Resources

About