Charalampos Tsoumpas

STIR: software for tomographic image reconstruction release 2

We present an update to STIR, an Open Source object-oriented library in C++ for 3D PET reconstruction. This library has been designed so that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design, but also adds extra capabilities such as list mode reconstruction, more data formats etc.

Connectivity-Based Functional Analysis of Dopamine Release in the Striatum Using Diffusion-Weighted MRI and Positron Emission Tomography

The striatum acts in conjunction with the cortex to control and execute functions that are impaired by abnormal dopamine neurotransmission in disorders such as Parkinsons and schizophrenia. To date, in vivo quantification of striatal dopamine has been restricted to structure-based striatal subdivisions. Here, we present a multimodal imaging approach that quantifies the endogenous dopamine release following the administration of d-amphetamine in the functional subdivisions of the striatum of healthy humans with [(11)C]PHNO and [(11)C]Raclopride positron emission tomography ligands. Using connectivity-based (CB) parcellation, we subdivided the striatum into functional subregions based on striato-cortical anatomical connectivity information derived from diffusion magnetic resonance imaging (MRI) and probabilistic tractography. Our parcellation showed that the functional organization of the striatum was spatially coherent across individuals, congruent with primate data and previous diffusion MRI studies, with distinctive and overlapping networks. d-amphetamine induced the highest dopamine release in the limbic followed by the sensory, motor, and executive areas. The data suggest that the relative regional proportions of D2-like receptors are unlikely to be responsible for this regional dopamine release pattern. Notably, the homogeneity of dopamine release was significantly higher within the CB functional subdivisions in comparison with the structural subdivisions. These results support an association between local levels of dopamine release and cortical connectivity fingerprints.

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.

Functional and structural synergy for resolution recovery and partial volume correction in brain PET

PURPOSE Positron Emission Tomography (PET) has the unique capability of measuring brain function but its clinical potential is affected by low resolution and lack of morphological detail. Here we propose and evaluate a wavelet synergistic approach that combines functional and structural information from a number of sources (CT, MRI and anatomical probabilistic atlases) for the accurate quantitative recovery of radioactivity concentration in PET images. When the method is combined with anatomical probabilistic atlases, the outcome is a functional volume corrected for partial volume effects. METHODS The proposed method is based on the multiresolution property of the wavelet transform. First, the target PET image and the corresponding anatomical image (CT/MRI/atlas-based segmented MRI) are decomposed into several resolution elements. Secondly, high-resolution components of the PET image are replaced, in part, with those of the anatomical image after appropriate scaling. The amount of structural input is weighted by the relative high frequency signal content of the two modalities. The method was validated on a digital Zubal phantom and clinical data to evaluate its quantitative potential. RESULTS Simulation studies showed the expected relationship between functional recovery and the amount of correct structural detail provided, with perfect recovery achieved when true images were used as anatomical reference. The use of T1-MRI images brought significant improvements in PET image resolution. However improvements were maximized when atlas-based segmented images as anatomical references were used; these results were replicated in clinical data sets. CONCLUSION The synergistic use of functional and structural data, and the incorporation of anatomical probabilistic information in particular, generates morphologically corrected PET images of exquisite quality.

Study of direct and indirect parametric estimation methods of linear models in dynamic positron emission tomography

In dynamic positron emission tomography (PET) studies, the time changing activity of the radiotracer is measured through multiple consecutive frames. Subsequent pixel-by-pixel application of the appropriate kinetic model provides quantitative information in terms of images of the distribution of the physiological parameter of interest. In this context, iterative reconstruction methods may be used to reconstruct for each time frame a static image of appreciable higher quality than the analytical algorithms, especially in low-count cases. Furthermore, if the reconstruction algorithm also models the kinetics of the measured counts, the parametric image is expected to be of even higher quality. In this work, we investigate the methodology to directly reconstruct parametric images in three-dimensional PET when the kinetic model is linear in its parameters (Patlak plot) and compare with indirectly estimated parametric maps, where the radioactivity distribution was estimated by the filtered back projection and ordered subsets expectation maximization algorithms. Both real and simulated data for tracers with irreversible kinetics in brain studies are included. The results demonstrate appreciable smaller standard deviation and mean squared error characteristics for the direct reconstruction. However, some regions may converge slowly. The FBP and ordered subsets expectation maximization (OSEM) indirect estimations have a similar level of bias after matching their resolutions, but OSEM has smaller standard deviation.

Fast generation of 4D PET-MR data from real dynamic MR acquisitions

We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150× slower computation time compared to the analytic method for ten respiratory positions and is 7000× slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1% for tissue, 17% for the lungs and 18% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.

A survey of approaches for direct parametric image reconstruction in emission tomography

The quantitative data obtained by emission tomography are decoded using a number of techniques and methods in sequence to provide physiological information. Conventionally, the data are reconstructed to produce a series of static images. Then, pharmacokinetic modeling techniques are applied, and kinetic parameters that have physiological or functional significance are derived. Although it is possible to optimize each estimation step in this process, many simplifying assumptions have to be introduced to make the methods that are used practicable. Published research has shown that if the kinetic parameters are estimated directly from the measured data, the parametric images will have higher quality and lower mean-squared error than if this was done indirectly. This review highlights some aspects of the methods that have been proposed for such direct estimation of pharmacokinetic information from raw emission data.

STIR: Software for Tomographic Image Reconstruction Release 2

We present an update to STIR, an Open Source object-oriented library in C++ for 3D PET reconstruction. This library has been designed so that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design, but also adds extra capabilities such as list mode reconstruction, more data formats etc.

Simultaneous PET–MR acquisition and MR-derived motion fields for correction of non-rigid motion in PET

ObjectivePositron emission tomography (PET) provides an accurate measurement of radiotracer concentration in vivo, but performance can be limited by subject motion which degrades spatial resolution and quantitative accuracy. This effect may become a limiting factor for PET studies in the body as PET scanner technology improves. In this work, we propose a new approach to address this problem by employing motion information from images measured simultaneously using a magnetic resonance (MR) scanner.MethodsThe approach is demonstrated using an MR-compatible PET scanner and PET–MR acquisition with a purpose-designed phantom capable of non-rigid deformations. Measured, simultaneously acquired MR data were used to correct for motion in PET, and results were compared with those obtained using motion information from PET images alone.ResultsMotion artefacts were significantly reduced and the PET image quality and quantification was significantly improved by the use of MR motion fields, whilst the use of PET-only motion information was less successful.ConclusionsCombined PET–MR acquisitions potentially allow PET motion compensation in whole-body acquisitions without prolonging PET acquisition time or increasing radiation dose. This, to the best of our knowledge, is the first study to demonstrate that simultaneously acquired MR data can be used to estimate and correct for the effects of non-rigid motion in PET.

Analysis and comparison of two methods for motion correction in PET imaging

PURPOSE Although there have been various proposed methods for positron emission tomography (PET) motion correction, there is not sufficient evidence to answer which method is better in practice. This investigation aims to characterize the behavior of the two main motion-correction approaches in terms of convergence and image properties. METHODS For the first method, reconstruct-transform-average (RTA), reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), motion information is incorporated within the reconstruction. Both techniques studied were based on the ordered subsets expectation maximization algorithm. Motion information was obtained from a dynamic MR acquisition performed on a human volunteer and concurrent PET data were simulated from the dynamic MR data. The two approaches were assessed statistically using multiple realizations to accurately define the noise properties of the reconstructed images. RESULTS MCIR successfully recovers the true values of all regions, whereas RTA has high bias due to the limited count-statistics and interpolation errors during the transformation step. In addition, RTA noise is very small and stabilized, whereas in MCIR noise becomes progressively greater with the number of iterations and therefore MCIR outperforms RTA in terms of MSE only if noise is treated. For example, MCIR with postfiltering results in MSE up to 42% lower than RTA. CONCLUSIONS This study indicates that MCIR may provide superior performance overall to RTA if noise is minimized. However, in applications where quantification is not the main objective RTA can be a practical and simple method to correct for motion.