This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.

In this work we demonstrate the proof of principle of CT-based attenuation correction of 3D positron emission tomography (PET) data by using scans of bone and soft tissue equivalent phantoms and scans of humans. This method of attenuation correction is intended for use in a single scanner that combines volume-imaging (3D) PET with x-ray computed tomography (CT) for the purpose of providing accurately registered anatomical localization of structures seen in the PET image. The goal of this work is to determine if we can perform attenuation correction of the PET emission data using accurately aligned CT attenuation information. We discuss possible methods of calculating the PET attenuation map at 511 keV based on CT transmission information acquired from 40 keV through 140 keV. Data were acquired on separate CT and PET scanners and were aligned using standard image registration procedures. Results are presented on three of the attenuation calculation methods: segmentation, scaling, and our proposed hybrid segmentation/scaling method. The results are compared with those using the standard 3D PET attenuation correction method as a gold standard. We demonstrate the efficacy of our proposed hybrid method for converting the CT attenuation map from an effective CT photon energy of 70 keV to the PET photon energy of 511 keV. We conclude that using CT information is a feasible way to obtain attenuation correction factors for 3D PET.

This paper presents two new rebinning algorithms for the reconstruction of three-dimensional (3-D) positron emission tomography (PET) data. A rebinning algorithm is one that first sorts the 3-D data into an ordinary two-dimensional (2-D) data set containing one sinogram for each transaxial slice to be reconstructed; the 3-D image is then recovered by applying to each slice a 2-D reconstruction method such as filtered-backprojection. This approach allows a significant speedup of 3-D reconstruction, which is particularly useful for applications involving dynamic acquisitions or whole-body imaging. The first new algorithm is obtained by discretizing an exact analytical inversion formula. The second algorithm, called the Fourier rebinning algorithm (FORE), is approximate but allows an efficient implementation based on taking 2-D Fourier transforms of the data. This second algorithm was implemented and applied to data acquired with the new generation of PET systems and also to simulated data for a scanner with an 18 degrees axial aperture. The reconstructed images were compared to those obtained with the 3-D reprojection algorithm (3DRP) which is the standard "exact" 3-D filtered-backprojection method. Results demonstrate that FORE provides a reliable alternative to 3DRP, while at the same time achieving an order of magnitude reduction in processing time.

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After seminal papers over the period 2009 – 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.