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Register a new userParameterizing the Angular Distribution of Emission: A model for TOF-PET low counts reconstruction
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Low counts reconstruction remains a challenge for Positron Emission Tomography (PET) even with the recent progresses in time-of-flight (TOF) resolution. In that setting, the bias between the acquired histogram, composed of low values or zeros, and the expected histogram, obtained from the forward projector, is propagated to the image, resulting in a biased reconstruction. This could be exacerbated with finer resolution of the TOF information, which further sparsify the acquired histogram. We propose a new approach to circumvent this limitation of the classical reconstruction model. It consists of extending the parametrization of the reconstruction scheme to also explicitly include the projection domain. This parametrization has greater degrees of freedom than the log-likelihood model, which can not be harnessed in classical circumstances. We hypothesize that with ultra-fast TOF this new approach would not only be viable for low counts reconstruction but also more adequate than the classical reconstruction model. An implementation of this approach is compared to the log-likelihood model by using two-dimensional simulations of a hot spots phantom. The proposed model achieves similar contrast recovery coefficients as MLEM except for the smallest structures where the low counts nature of the simulations makes it difficult to draw conclusions. Also, this new model seems to converge toward a less noisy solution than the MLEM. These results suggest that this new approach has potential for low counts reconstruction with ultra-fast TOF.
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Prostate cancer is the most common disease in men and the second leading cause of death from cancer. Generic large imaging instruments used in cancer diagnosis have sensitivity, spatial resolution, and contrast inadequate for the task of imaging details of a small organ such as the prostate. In addition, multimodality imaging can play a significant role merging anatomical and functional details coming from simultaneous PET and MRI. Indeed, multi-parametric PET/MRI was demonstrated to improve diagnosis, but it suffers from too many false positives. In order to address the above limits of the current techniques, we have proposed, built and tested, thanks to the TOPEM project funded by Italian National Institute of Nuclear Phisics a prototype of an endorectal PET-TOF/MRI probe. In the applied magnification PET geometry, performance is dominated by a high-resolution detector placed closer to the source. The expected spatial resolution in the selected geometry is about 1.5 mm FWHM and efficiency a factor of 2 with respect to what obtained with the conventional PET scanner. In our experimental studies, we have obtained timing resolution of ~ 320 ps FWHM and at the same time Depth of Interaction (DOI) resolution of under 1 mm. Tests also showed that mutual adverse PET-MR effects are minimal. In addition, the matching endorectal RF coil was designed, built and tested. In the next planned studies, we expect that benefiting from the further progress in scintillator crystal surface treatment, in SiPM technology and associated electronics would allow us to significantly improve TOF resolution
Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction. The vast majority of metrics employed for evaluating non-linear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to over-regularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. Methods: The metric is demonstrated in the context of a breast CT simulation, where different equal-dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a non-linear algorithm based on total variation constrained least-squares (TV-LSQ). The images are evaluated visually, with image RMSE, and with the proposed signal-detection based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Results: Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to over-regularization with TV-LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over-regularized images. Conclusions: The proposed signal detection based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when non-linear image reconstruction is used.
We perform a parametric study of the newly developed time-of-flight (TOF) image reconstruction algorithm, proposed for the real-time imaging in total-body Jagiellonian PET (J-PET) scanners. The asymmetric 3D filtering kernel is applied at each most likely position of electron-positron annihilation, estimated from the emissions of back-to-back $gamma$-photons. The optimisation of its parameters is studied using Monte Carlo simulations of a 1-mm spherical source, NEMA IEC and XCAT phantoms inside the ideal J-PET scanner. The combination of high-pass filters which included the TOF filtered back-projection (FBP), resulted in spatial resolution, 1.5 $times$ higher in the axial direction than for the conventional 3D FBP. For realistic $10$-minute scans of NEMA IEC and XCAT, which require a trade-off between the noise and spatial resolution, the need for Gaussian TOF kernel components, coupled with median post-filtering, is demonstrated. The best sets of 3D filter parameters were obtained by the Nelder-Mead minimisation of the mean squared error between the resulting and reference images. The approach allows training the reconstruction algorithm for custom scans, using the IEC phantom, when the temporal resolution is below 50 ps. The image quality parameters, estimated for the best outcomes, were systematically better than for the non-TOF FBP.
We present results of simulations on the influence of photon propagation and the Cherenkov effect on the time resolution of LSO:Ce scintillators. The influence of the scintillator length on the coincidence time resolution is shown. Furthermore, the impact of the depth of interaction on the time resolution, the light output and the arrival time distribution at the photon detector is simulated and it is shown how these information can be used for time walk correction.
Objective evaluation of new and improved methods for PET imaging requires access to images with ground truth, as can be obtained through simulation studies. However, for these studies to be clinically relevant, it is important that the simulated images are clinically realistic. In this study, we develop a stochastic and physics-based method to generate realistic oncological two-dimensional (2-D) PET images, where the ground-truth tumor properties are known. The developed method extends upon a previously proposed approach. The approach captures the observed variabilities in tumor properties from actual patient population. Further, we extend that approach to model intra-tumor heterogeneity using a lumpy object model. To quantitatively evaluate the clinical realism of the simulated images, we conducted a human-observer study. This was a two-alternative forced-choice (2AFC) study with trained readers (five PET physicians and one PET physicist). Our results showed that the readers had an average of ~ 50% accuracy in the 2AFC study. Further, the developed simulation method was able to generate wide varieties of clinically observed tumor types. These results provide evidence for the application of this method to 2-D PET imaging applications, and motivate development of this method to generate 3-D PET images.
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