Joint reconstruction of activity and attenuation in dynamic PET

Joint reconstruction of attenuation and emission in positron emission tomography (PET) using the maximum likelihood activity and attenuation estimation (MLAA) algorithm was proposed in the past. However, cross-talk between the activity and attenuation estimation limits the usefulness of MLAA for PET data without time-of-flight (TOF) information. This work introduces dynamic MLAA (dMLAA), an extension of the MLAA algorithm for dynamic data, to jointly reconstruct the activity distributions and a single attenuation map. The hypothesis is that using information from multiple dynamic emission frames may improve the estimated attenuation map compared to using static PET data. Preliminary results using dMLAA algorithm showed that use of multiple dynamic emission frames slightly improves the reconstructed attenuation map (especially in bones, cavities and lesion area) compared to using a single emission frame. However, without TOF, the reconstructed map still suffers from ill-posedness of the problem despite the additional dynamic information. The reconstruction may be improved for tracers that present a higher inter- and intra-dynamic frame contrast and edge variability

Motion-corrected reconstruction of parametric images from dynamic PET data with the Synergistic Image Reconstruction Framework (SIRF)

Motion correction has been added to a PET-MR reconstruction tool, SIRF, by incorporating a registration package, NiftyReg. New functionality has been demonstrated in the context of estimating kinetic parameters in the left temporal lobe, comparing direct and indirect reconstructions and evaluating the impact of using motion correction.Principal component analysis was used to detect motion and to determine time frames, while STIR's parametric-OSEM was used to perform the motion-corrected direct parametric reconstruction.It was found that the variance in the left temporal lobe decreased when motion correction was performed, and the same was true of direct reconstructions compared to indirect.With SIRF, the entirety of the demonstrated functionality can be performed from a single Matlab or Python script

Joint Activity/Attenuation Reconstruction in SPECT Using Photopeak and Scatter Sinograms

This work presents a joint activity/attenuation reconstruction method in SPECT, based on the maximisation of the scatter and non-scatter data joint log-likelihood. The activity image is updated with standard expectation maximisation (EM) whereas the attenuation is updated with a quasi-Newton line-search. Results on simulation demonstrates that the utilisation of scatter considerably reduces the ill-posedness of the initial reconstruction problem with non-scatter counts only. Results on phantom data show that using scatter enables myocardial reconstruction similar to an EM reconstruction with CT attenuation correctio

Pulmonary 18F-FDG uptake helps refine current risk stratification in idiopathic pulmonary fibrosis (IPF).

PURPOSE: There is a lack of prognostic biomarkers in idiopathic pulmonary fibrosis (IPF) patients. The objective of this study is to investigate the potential of 18F-FDG-PET/ CT to predict mortality in IPF. METHODS: A total of 113 IPF patients (93 males, 20 females, mean age ± SD: 70 ± 9 years) were prospectively recruited for 18F-FDG-PET/CT. The overall maximum pulmonary uptake of 18F-FDG (SUVmax), the minimum pulmonary uptake or background lung activity (SUVmin), and target-to-background (SUVmax/ SUVmin) ratio (TBR) were quantified using routine region-of-interest analysis. Kaplan-Meier analysis was used to identify associations of PET measurements with mortality. We also compared PET associations with IPF mortality with the established GAP (gender age and physiology) scoring system. Cox analysis assessed the independence of the significant PET measurement(s) from GAP score. We investigated synergisms between pulmonary 18F-FDG-PET measurements and GAP score for risk stratification in IPF patients. RESULTS: During a mean follow-up of 29 months, there were 54 deaths. The mean TBR ± SD was 5.6 ± 2.7. Mortality was associated with high pulmonary TBR (p = 0.009), low forced vital capacity (FVC; p = 0.001), low transfer factor (TLCO; p  4.9 was 24 months. Combining PET data with GAP data ("PET modified GAP score") refined the ability to predict mortality. CONCLUSIONS: A high pulmonary TBR is independently associated with increased risk of mortality in IPF patients

Consensus recommendations on the use of 18F-FDG PET/CT in lung disease

Positron emission tomography (PET) with 18F-fluorodeoxyglucose (18F-FDG) has been increasingly applied, predominantly in the research setting, to study drug effects and pulmonary biology and monitor disease progression and treatment outcomes in lung diseases, disorders that interfere with gas exchange through alterations of the pulmonary parenchyma, airways and/or vasculature. To date, however, there are no widely accepted standard acquisition protocols and imaging data analysis methods for pulmonary 18F-FDG PET/CT in these diseases, resulting in disparate approaches. Hence, comparison of data across the literature is challenging. To help harmonize the acquisition and analysis and promote reproducibility, acquisition protocol and analysis method details were collated from seven PET centers. Based on this information and discussions among the authors, the consensus recommendations reported here on patient preparation, choice of dynamic versus static imaging, image reconstruction, and image analysis reporting were reached.                   </p

Simulation of Breast Lesions in X-Ray Mammography Screening.

X-ray mammography is the imaging modality of choice in screening to detect breast cancer in its early stages. In recent years, film-screen systems have been replaced by various digital mammography technologies as these can deliver better performance than conventional film-screen technology. However, it remains unclear how the physical performance of such systems and the choice of their operating parameters is correlated with the ability to detect early breast cancer. While clinical trials are used to address this issue, they have many associated limitations such as unethical extra exposure, time consuming data collection and completion of trials. Alternatively, a simulation framework whereby suitably realistic synthetic breast cancer pathology is inserted into normal clinical mammograms to form a large database can enable a more efficient comparison of multiple systems and study of technical parameters which influence the detection task. This thesis presents a novel computational model of breast mass appearance using fractal growth which can exhibit a range of lesion appearances. Masses generated using Random Walk (RW) and Diffusion Limited Aggregation (DLA) models were inserted into raw digital 2D mammograms using a physical model of the imaging process, thus avoiding ad hoc post-processing of the final image. The simulation framework accounted for local glandularity, polychromatic X-ray spectra, image degradation caused by the imaging system acquisition process, scatter and finally processing with manufacturer’s image processing software to produce realistic lesion attenuation and contrast. An ROC study of realism gave an average AUC and corresponding 95% CIs of 0.55 (0.51, 0.59) for DLA masses. This suggests that the DLA approach appears to produce a more realistic range of mass appearances compared to the RW approach, which achieved an AUC of 0.60 (0.56, 0.63). Both results demonstrate improvement compared to previously published ROC studies of realism of the simulated masses. The mass simulation models may be used subsequently as part of a tool to evaluate different breast imaging technologies (2D and 3D) and their performance in the detection task. Digital breast tomosynthesis (DBT) may have superior performance compared to 2D mammography in terms of cancer visibility, especially in dense breasts. Lesions grown using the DLA method, previously validated in 2D mammograms, were used to simulate breast masses into clinical DBT projection images. A pilot study was performed where radiologists feedback suggests that DLA masses can be successfully embedded in DBT projections and can produce visually authentic DBT images containing synthetic pathology. However, mass appearance whilst entirely satisfactory in 2D, does not always reliably infer satisfactory appearance in DBT

Simulation of spiculated breast lesions

Virtual clinical trials are a promising new approach increasingly used for the evaluation and comparison of breast imaging modalities. A key component in such an assessment paradigm is the use of simulated pathology, in particular, simulation of lesions. Breast mass lesions can be generally classified into two categories based on their appearance; nonspiculated masses and spiculated masses. In our previous work, we have successfully simulated non-spiculated masses using a fractal growth process known as diffusion limited aggregation. In this new work, we have extended the DLA model to simulate spiculated lesions by using features extracted from patient DBT images containing spiculated lesions. The features extracted included spicule length, width, curvature and distribution. This information was used to simulate realistic looking spicules which were attached to the surface of a DLA mass to produce a spiculated mass. A batch of simulated spiculated masses was inserted into normal patient images and presented to an experienced radiologist for review. The study yielded promising results with the radiologist rating 60% of simulated lesions in 2D and 50% of simulated lesions in DBT as realistic

Simulation and assessment of realistic breast lesions using fractal growth models.

A new method of generating realistic three dimensional simulated breast lesions known as diffusion limited aggregation (DLA) is presented, and compared with the random walk (RW) method. Both methods of lesion simulation utilize a physics-based method for inserting these simulated lesions into 2D clinical mammogram images that takes into account the polychromatic x-ray spectrum, local glandularity and scatter. DLA and RW masses were assessed for realism via a receiver operating characteristic (ROC) study with nine observers. The study comprised 150 images of which 50 were real pathology proven mammograms, 50 were normal mammograms with RW inserted masses and 50 were normal mammograms with DLA inserted masses. The average area under the ROC curve for the DLA method was 0.55 (95% confidence interval 0.51-0.59) compared to 0.60 (95% confidence interval 0.56-0.63) for the RW method. The observer study results suggest that the DLA method produced more realistic masses with more variability in shape compared to the RW method. DLA generated lesions can overcome the lack of complexity in structure and shape in many current methods of mass simulation