Time resolution of the plastic scintillator strips with matrix photomultiplier readout for J-PET tomograph

Recent tests of a single module of the Jagiellonian Positron Emission Tomography system (J-PET) consisting of 30 cm long plastic scintillator strips have proven its applicability for the detection of annihilation quanta (0.511 MeV) with a coincidence resolving time (CRT) of 0.266 ns. The achieved resolution is almost by a factor of two better with respect to the current TOF-PET detectors and it can still be improved since, as it is shown in this article, the intrinsic limit of time resolution for the determination of time of the interaction of 0.511 MeV gamma quanta in plastic scintillators is much lower. As the major point of the article, a method allowing to record timestamps of several photons, at two ends of the scintillator strip, by means of matrix of silicon photomultipliers (SiPM) is introduced. As a result of simulations, conducted with the number of SiPM varying from 4 to 42, it is shown that the improvement of timing resolution saturates with the growing number of photomultipliers, and that the 2 x 5 configuration at two ends allowing to read twenty timestamps, constitutes an optimal solution. The conducted simulations accounted for the emission time distribution, photon transport and absorption inside the scintillator, as well as quantum efficiency and transit time spread of photosensors, and were checked based on the experimental results. Application of the 2 x 5 matrix of SiPM allows for achieving the coincidence resolving time in positron emission tomography of $\approx$ 0.170 ns for 15 cm axial field-of-view (AFOV) and $\approx$ 0.365 ns for 100 cm AFOV. The results open perspectives for construction of a cost-effective TOF-PET scanner with significantly better TOF resolution and larger AFOV with respect to the current TOF-PET modalities.Comment: To be published in Phys. Med. Biol. (26 pages, 17 figures

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging

Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model

A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D TOF PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon's raytracer, we propose another more simplified geometrical projector based on the Bresenham's raytracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a nonfactored model such as the analytical model while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve optimal reconstruction performance based on a sparse factorization model with an only image domain resolution model