Investigations into a positron emission imaging algorithm

Includes abstract.Includes bibliographical references.A positron emission imaging algorithm which makes use of the entire set of lines-of-response in list-mode form is presented. The algorithm parameterises the lines-of-response by a Cartesian mesh over the field-of-view of a Positron Emission Tomography (PET) scanner to find their density distribution throughout the mesh. The algorithm is applied to PET image reconstruction and Positron Emission Particle Tracking (PEPT). For the PET image reconstruction, a redistribution of the lines-of-response is employed to remove the discrete nature of the data caused by the finite size of the detector cells, and once the density distribution has been determined, it is filtered and corrected for attenuation. The algorithm is applied to static and dynamic systems of hard phantoms, biological specimens and fluid flow through a column. In the dynamic systems, timesteps as low as 1 second are achieved. The results from the algorithm are compared to the standard Radon transform reconstruction algorithm, and the presented algorithm is observed to produce images with superior edge contrast, smoothness and representation of the physical system

Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D denoising kernel

Purpose: Reconstructed PET images are typically noisy, especially in dynamic imaging where the acquired data are divided into several short temporal frames. High noise in the reconstructed images translates to poor precision/reproducibility of image features. One important role of “denoising” is therefore to improve the precision of image features. However, typical denoising methods achieve noise reduction at the expense of accuracy. In this work, we present a novel four-dimensional (4D) denoised image reconstruction framework, which we validate using 4D simulations, experimental phantom, and clinical patient data, to achieve 4D noise reduction while preserving spatiotemporal patterns/minimizing error introduced by denoising. Methods: Our proposed 4D denoising operator/kernel is based on HighlY constrained backPRojection (HYPR), which is applied either after each update of OSEM reconstruction of dynamic 4D PET data or within the recently proposed kernelized reconstruction framework inspired by kernel methods in machine learning. Our HYPR4D kernel makes use of the spatiotemporal high frequency features extracted from a 4D composite, generated within the reconstruction, to preserve the spatiotemporal patterns and constrain the 4D noise increment of the image estimate. Results: Results from simulations, experimental phantom, and patient data showed that the HYPR4D kernel with our proposed 4D composite outperformed other denoising methods, such as the standard OSEM with spatial filter, OSEM with 4D filter, and HYPR kernel method with the conventional 3D composite in conjunction with recently proposed High Temporal Resolution kernel (HYPRC3D-HTR), in terms of 4D noise reduction while preserving the spatiotemporal patterns or 4D resolution within the 4D image estimate. Consequently, the error in outcome measures obtained from the HYPR4D method was less dependent on the region size, contrast, and uniformity/functional patterns within the target structures compared to the other methods. For outcome measures that depend on spatiotemporal tracer uptake patterns such as the nondisplaceable Binding Potential (BPND), the root mean squared error in regional mean of voxel BPND values was reduced from ~8% (OSEM with spatial or 4D filter) to ~3% using HYPRC3D-HTR and was further reduced to ~2% using our proposed HYPR4D method for relatively small target structures (~10 mm in diameter). At the voxel level, HYPR4D produced two to four times lower mean absolute error in BPND relative to HYPRC3D-HTR. Conclusion: As compared to conventional methods, our proposed HYPR4D method can produce more robust and accurate image features without requiring any prior information

Segmentation of Dynamic PET Images with Kinetic Spectral Clustering

International audienceSegmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks

Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques

<p>Abstract</p> <p>Background</p> <p>Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization.</p> <p>Methods</p> <p>We have applied principal component analysis to provide high-contrast parametric image sets of lower dimensions than the original data set separating structures based on their kinetic characteristics. Our method has the potential to constitute an alternative quantification method, independent of any kinetic model, and is particularly useful when the retrieval of the arterial input function is complicated. In independent component analysis images, structures that have different kinetic characteristics are assigned opposite values, and are readily discriminated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporal properties of the entire image sequence according to a reference region.</p> <p>Results</p> <p>Using our new cubed sum coefficient similarity measure, we have shown that structures with similar time activity curves can be identified, thus facilitating the detection of lesions that are not easily discriminated using the conventional method employing standardized uptake values.</p

Factor analysis of dynamic PET images

Thanks to its ability to evaluate metabolic functions in tissues from the temporal evolution of a previously injected radiotracer, dynamic positron emission tomography (PET) has become an ubiquitous analysis tool to quantify biological processes. Several quantification techniques from the PET imaging literature require a previous estimation of global time-activity curves (TACs) (herein called \textit{factors}) representing the concentration of tracer in a reference tissue or blood over time. To this end, factor analysis has often appeared as an unsupervised learning solution for the extraction of factors and their respective fractions in each voxel. Inspired by the hyperspectral unmixing literature, this manuscript addresses two main drawbacks of general factor analysis techniques applied to dynamic PET. The first one is the assumption that the elementary response of each tissue to tracer distribution is spatially homogeneous. Even though this homogeneity assumption has proven its effectiveness in several factor analysis studies, it may not always provide a sufficient description of the underlying data, in particular when abnormalities are present. To tackle this limitation, the models herein proposed introduce an additional degree of freedom to the factors related to specific binding. To this end, a spatially-variant perturbation affects a nominal and common TAC representative of the high-uptake tissue. This variation is spatially indexed and constrained with a dictionary that is either previously learned or explicitly modelled with convolutional nonlinearities affecting non-specific binding tissues. The second drawback is related to the noise distribution in PET images. Even though the positron decay process can be described by a Poisson distribution, the actual noise in reconstructed PET images is not expected to be simply described by Poisson or Gaussian distributions. Therefore, we propose to consider a popular and quite general loss function, called the $\beta$-divergence, that is able to generalize conventional loss functions such as the least-square distance, Kullback-Leibler and Itakura-Saito divergences, respectively corresponding to Gaussian, Poisson and Gamma distributions. This loss function is applied to three factor analysis models in order to evaluate its impact on dynamic PET images with different reconstruction characteristics

Quantification of tumor heterogeneity using PET/MRI and machine learning

Despite a broad understanding that solid tumors exhibit significant tissue heterogeneity, clinical trials have not seen a remarkable development in techniques that aid in characterizing cancer. Needle biopsies often represent only a partial view of the tumor profile, lacking the ability to comprehensively reflect spatiotemporal phenotypic changes. Recent multimodal multiparametric imaging techniques could provide further valuable insights if the complementary imaging information is sufficiently analyzed. Therefore, in this work I developed and applied machine learning methods on multiparametric positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, acquired using mice bearing subcutaneous tumors, to obtain a precise spatio-temporal characterization of intratumor heterogeneity

Patch-based image reconstruction for PET using prior-image derived dictionaries

This collection contains figures and reconstructed images in .mat format associated with the manuscript tiled "Patch-based image reconstruction for PET using prior-image derived dictionaries" . The file, Data_Fig9-10.zip contains the reconstructed images associated with Fig 9 and 10 as a function of iteration for different methods. Data_Fig10-12.zip contains reconstructed images of the real data for different methods