Comment on 'A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging'.

We read with great interest the paper by Lim(2018035042) on bias reduction in Y-90 PET imaging. In particular, they proposed a new formulation of the tomographic reconstruction problem that enforces non-negativity in projection space as opposed to image space. We comment on the algorithm they derived from this formulation and bring some clarifications on the constraint that this algorithm respects.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine.

Using different tracers in positron emission tomography (PET) imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition.

Use of dynamic reconstruction for parametric Patlak imaging in dynamic whole body PET.

Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of Patlakcan be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps.

Reconstruction, analysis and interpretation of posterior probability distributions of PET images, using the posterior bootstrap.

The uncertainty of reconstructed PET images remains difficult to assess and to interpret for the use in diagnostic and quantification tasks. Here we provide (1) an easy-to-use methodology for uncertainty assessment for almost any Bayesian model in PET reconstruction from single datasets and (2) a detailed analysis and interpretation of produced posterior image distributions. We apply a recent posterior bootstrap framework to the PET image reconstruction inverse problem and obtain simple parallelizable algorithms based on random weights and on existing maximum(MAP) (posterior maximum) optimization-based algorithms.

Time-of-flight (TOF) implementation for PET reconstruction in practice.

The time-of-flight (TOF) feature of PET scanners has been used for a long time in PET reconstruction, but many implementational aspects are still incomplete or ambiguous in the literature. Here we formalize and present theoretical and practical implementation details for the reconstruction of clinical TOF histogram and list-mode data using ML-EM. Relevant aspects include the computation of the TOF component of the system matrix, the processing of TOF bins, the use of estimations of random and scattered coincidences, and differences between histogram and list-mode ML-EM TOF reconstruction.

Factor Analysis of Dynamic PET Images: Beyond Gaussian Noise.

Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if it is considered with respect to suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Poissonian nature of the count rates.

PET Reconstruction of the Posterior Image Probability, Including Multimodal Images.

In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs Markov chain Monte Carlo sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image.

Unmixing dynamic PET images with variable specific binding kinetics.

To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal component analysis (PCA), independent component analysis (ICA), factor analysis and nonnegative matrix factorization (NMF). Nevertheless, these conventional approaches neglect any possible nonlinear variations in the time activity curves describing the kinetic behavior of tissues with specific binding, which limits their ability to recover a reliable, understandable and interpretable description of the data. This paper proposes an alternative analysis paradigm that accounts for spatial fluctuations in the exchange rate of the tracer between a free compartment and a specifically bound ligand compartment.

CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction.

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods.

Imaging the Impact of the P-Glycoprotein (ABCB1) Function on the Brain Kinetics of Metoclopramide.

Unlabelled: The effects of metoclopramide on the central nervous system (CNS) in patients suggest substantial brain distribution. Previous data suggest that metoclopramide brain kinetics may nonetheless be controlled by ATP-binding cassette (ABC) transporters expressed at the blood-brain barrier. We used (11)C-metoclopramide PET imaging to elucidate the kinetic impact of transporter function on metoclopramide exposure to the brain.

Variational segmentation of vector-valued images with gradient vector flow.

In this paper, we extend the gradient vector flow field for robust variational segmentation of vector-valued images. Rather than using scalar edge information, we define a vectorial edge map derived from a weighted local structure tensor of the image that enables the diffusion of the gradient vectors in accurate directions through the 4D gradient vector flow equation. To reduce the contribution of noise in the structure tensor, image channels are weighted according to a blind estimator of contrast.

Bias reduction for low-statistics PET: maximum likelihood reconstruction with a modified Poisson distribution.

Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling.

PET-based dose delivery verification in proton therapy: a GATE based simulation study of five PET system designs in clinical conditions.

PET is a promising technique for in vivo treatment verification in hadrontherapy. Three main PET geometries dedicated to in-beam treatment monitoring have been proposed in the literature: the dual-head PET geometry, the OpenPET geometry and the slanted-closed ring geometry. The aim of this work is to characterize the performance of two of these dedicated PET detectors in realistic clinical conditions.

Practical considerations for image-based PSF and blobs reconstruction in PET.

Iterative reconstructions in positron emission tomography (PET) need a model relating the recorded data to the object/patient being imaged, called the system matrix (SM). The more realistic this model, the better the spatial resolution in the reconstructed images. However, a serious concern when using a SM that accurately models the resolution properties of the PET system is the undesirable edge artefact, visible through oscillations near sharp discontinuities in the reconstructed images.

A new method for volume segmentation of PET images, based on possibility theory.

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach.

Multidimensional B-spline parameterization of the detection probability of PET systems to improve the efficiency of Monte Carlo simulations.

Accurate modeling of system response and scatter distribution is crucial for image reconstruction in emission tomography. Monte Carlo simulations are very well suited to calculate these quantities. However, Monte Carlo simulations are also slow and many simulated counts are needed to provide a sufficiently exact estimate of the detection probabilities.

Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F-FDG PET.

Unlabelled: In (18)F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from (18)F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations.

Introducing improved voxel navigation and fictitious interaction tracking in GATE for enhanced efficiency.

Geant4 Application for Emission Tomography (GATE) is a widely used, well-validated and very versatile application for Monte Carlo simulations in emission tomography. However, its computational performance is poor, especially for voxelized phantoms, partly due to the use of a very general particle tracking algorithm. In this work, two methods are proposed to reduce the time spent on particle tracking in the phantom: a newly introduced 'regular navigation algorithm' of Geant4 and fictitious interaction tracking (also known as Woodcock tracking) for photons.