Small animal PET scanners require high spatial resolution and good sensitivity. To reconstruct high-resolution images in 3D-PET, iterative methods, such as OSEM, are superior to analytical reconstruction algorithms, although their high computational cost is still a serious drawback. The higher performance of modern computers could make iterative image reconstruction fast enough to be viable, provided we are able to deal with the large number of probability coefficients for the system response matrix in high-resolution PET scanners, which is a difficult task that prevents the algorithms from reaching peak computing performance. Considering all possible axial and in-plane symmetries, as well as certain quasi-symmetries, we have been able to reduce the memory requirements to store the system response matrix (SRM) well below 1 GB, which allows us to keep the whole response matrix of the system inside RAM of ordinary industry-standard computers, so that the reconstruction algorithm can achieve near peak performance. The elements of the SRM are stored as cubic spline profiles and matched to voxel size during reconstruction. In this way, the advantages of on-the-fly calculation and of fully stored SRM are combined. The on-the-fly part of the calculation (matching the profile functions to voxel size) of the SRM accounts for 10 to 30% of the reconstruction time, depending on the number of voxels chosen. We tested our approach with real data from a commercial small animal PET scanner. The results (image quality and reconstruction time) show that the proposed technique is a feasible solution.
Modern TOF-PET scanner systems require high-speed computing resources for efficient data processing, monitoring and image reconstruction. In this article we present the data flow and software architecture for the novel TOF-PET scanner developed by the J-PET collaboration. We discuss the data acquisition system, reconstruction framework and image reconstruction software. Also, the concept of computing outside hospitals in the remote centers such as Świerk Computing Centre in Poland is presented.
A novel Positron Emission Tomography system, based on plastic scintillators, is being developed by the J-PET collaboration. In this article we present the simulation results of the scatter fraction, representing one of the parameters crucial for background studies defined in the NEMA-NU-2-2012 norm. We elaborate an event selection methods allowing to suppress events in which gamma quanta were scattered in the phantom or underwent the multiple scattering in the detector. The estimated scatter fraction for the single-layer J-PET scanner varies from 37% to 53% depending on the applied energy thre…
In the present ongoing study, we are proposing a prototype model for positron emission tomography detection technology by introduction of a new discriminatory window parameter. It can be a new generation PET detection technique. We introduced Polarization Measurement of the annihilation photons(generated from the annihilation of positron and electron) as an additional parameter in proposed prototype, to correlate annihilation photons of a particular annihilation event. The motivation behind this introduction is Quantum Entanglement relation between the two annihilation photons. These two oppos…
J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data…
Instituto de Física Corpuscular (IFIC)（粒子物理研究所）；Universitat de València（瓦伦西亚大学）；Donostia International Physics Center（圣塞巴斯蒂安国际物理中心）；Ikerbasque (Basque Foundation for Science)（伊克尔巴斯基（巴斯克科学基金会））
J-PET analysis framework is a flexible, lightweight, ROOT-based software package which provides the tools to develop reconstruction and calibration procedures for PET tomography. In this article we present the implementation of the full data-processing chain in the J-PET framework which is used for the data analysis of the J-PET tomography scanner. The Framework incorporates automated handling of PET setup parameters' database as well as high level tools for building data reconstruction procedures. Each of these components is briefly discussed.
The possibility to separate signals caused by 511 keV photons created in annihilation of electron-positron pairs and the so-called prompt photons from nuclei de- excitation is investigated. It could potentially be used to improve the quality of reconstructed images in the J-PET scanner in 2+1 photon tomography. Firstly, a research is conducted for several radioisotopes that decay via \b{eta}+ decay followed by de-excitation of an excited nucleus. Efficiency, purity and false positive rate are calculated for each isotope as a function of energy deposited threshold, with a hypothesis that signal…
We are investigating a prototype virtual pinhole positron emission tomography (VP-PET) system for small animal imaging applications. The PET detector modules were made up of 1.3 mm lutetium-yttrium oxyorthosilicate (LYSO) arrays, and the insert detectors consists of 0.6 mm pixelated Cadmium Zinc Telluride (CdZnTe). To validate the imaging experiment, we conducted a Monte Carlo simulation for the VP-PET system in Geant4 Application for Emission Tomography (GATE). For a point source of Na-22 with 0.5 mm diameter, the filtered back-projection (FBP) algorithm reconstructed PET image shows a resolu…
We developed a positron emission tomography (PET) system for multiple-isotope imaging. Our PET system, named multiple-isotope PET (MI-PET), can distinguish between different tracer nuclides using coincidence measurement of prompt gamma-rays, which are emitted after positron emission. In MI-PET imaging with a pure positron emitter and prompt-gamma emitter, because of the imperfectness of prompt gamma-ray detection, an image for a pure positron emitter taken by MI-PET is superposed by a positron-γ emitter. Therefore, in order to make isolated images of the pure positron emitter, we developed ima…
The foreseen CaLIPSO Positron Emission Tomography (PET) scanner is expected to yield simultaneously a fine image resolution, about 1 mm$^3$, and a high contrast. In this paper we present results of simulations for the full CaLIPSO PET scanner with a "cube" geometry. We quantify by simulations the expected image resolution and Noise Equivalent Count Rates and compare them to the performance of the most efficient clinically used PET scanner, the High-Resolution Research Tomograph by Siemens. We bring up the issues of the image reconstruction for a scanner with high spatial resolution. We also pr…
According to current prognosis studies of pancreatic cancer, survival rate nowadays is still as low as 6% mainly due to late detections. Taking into account the location of the disease within the body and making use of the level of miniaturization in radiation detectors that can be achieved at the present time, EndoTOFPET-US collaboration aims at the development of a multimodal imaging technique for endoscopic pancreas exams that combines the benefits of high resolution metabolic information from Time-Of- Flight (TOF) Positron Emission Tomography (PET) and anatomical information from ultrasoun…
Attenuation and scatter correction (AC) is crucial for quantitative Positron Emission Tomography (PET) imaging. Recently, direct application of AC in the image domain using deep learning approaches has been proposed for the hybrid PET/MR and dedicated PET systems that lack accompanying transmission or anatomical imaging. This study set out to investigate deep learning-based AC in the image domain using different input settings.
Roadmap Towards Quantum Entanglement Positron Emission Tomography (QE-PET)
Institute of Physics, Jagiellonian University（雅盖隆大学物理研究所）；Center for Theranostics, Jagiellonian University（雅盖隆大学诊疗中心）；University of California Santa Cruz（加州大学圣克鲁兹分校）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；The University of Tokyo（东京大学）；Institute for Medical Research and Occupational Health, Division of Radiation Protection（医学研究与职业健康研究所辐射防护部）；University of Sydney（悉尼大学）；University of Zagreb Faculty of Science, Department of Physics（萨格勒布大学理学院物理系）；ICTP South American Institute for Fundamental Research（ICTP南美基础研究所）；Universidade Federal de São Paulo（圣保罗联邦大学）；Instituto de Fisica Corpuscular (IFIC), CSIC-UV（粒子物理研究所（IFIC），西班牙国家研究委员会-瓦伦西亚大学）；University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Institutes for Quantum Science and Technology（国立量子科学技术研究院）；Institute of Science Tokyo（东京科学研究所）
Standard dual-energy computed tomography (CT) uses two different X-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and many other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrade or increasing radiation dose. This paper proposes a novel dual-energy CT imaging method that is enabled by the already-available P…
Integration of Spectral CT with PET and SPECT: Bringing Tissue Composition Information to Molecular Imaging
University of California Davis Health（加州大学戴维斯分校健康中心）；University College London（伦敦大学学院）；University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）
A major strength of iterative algorithms used in positron emission tomography (PET) lies in their abilities to introduce precise models of the physics at play, which includes the statistical nature of the detection processes, and a detailed description of the radiation-matter interactions. The process of data acquisition by the imaging system is described in a system response model, or system response matrix (SRM). In PET, elements of this matrix correspond to a probability of a coincident pair of gamma emitted from a certain element of the imaged volume (voxel) to be detected by the apparatus…
University Medical Center Utrecht（乌得勒支大学医学中心）；DKFZ German Cancer Research Center（德国癌症研究中心）；Heidelberg University（海德堡大学）
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) To replace CT in magnetic resonance (MR)-based treatment planning. II) Facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy. III) Derive attenuation maps for the correction of positron emission tomography (PET). Appropriate database searching …
Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The MBIR approach is often modeled as a convex optimization problem including a data fitting function and a penalty function. The tuning parameter value that regulates the strength of the penalty function is critical for achieving good reconstruction results but difficult to choose. In this work, we describe two path seeking algorithms that are capable of efficiently generating a series of MBIR images with different strengths of the penalty function. The root-mean-squared-…
Omni-tomography/Multi-tomography -- Integrating Multiple Modalities for Simultaneous Imaging
Virginia Tech（弗吉尼亚理工大学）；Wake Forest University（维克森林大学）；University of Minnesota（明尼苏达大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Baylor University Medical Center（贝勒大学医学中心）；Texas Instruments Inc.（德州仪器公司）；University of Chicago（芝加哥大学）
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院与哈佛医学院戈登医学影像中心）；Department of Biomedical Engineering, University of California, Davis（加利福尼亚大学戴维斯分校生物医学工程系）；Department of Statistics, University of California, Davis（加利福尼亚大学戴维斯分校统计系）
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post…
Purpose: The scattering phenomenon creates degrading effects in positron emission tomography (PET) and the corresponding events are rejected conventionally. We have proposed a mathematical model to retrace the original line of response of the single-scattered coincident events with the aim to incorporate such events in PET. Methods: We have devised a new Virtual extrapolation technique based on the concept of the probability density functions. Through which we transformed the original two-parameter list mode data for the coincident photon pairs to a one-parameter data set. The procedure of ran…
Super-resolution positron emission tomography by intensity modulation: Proof of concept
Huazhong University of Science and Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉国家实验室（光电））；University of Science and Technology of China（中国科学技术大学）；The University of Chicago（芝加哥大学）
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
Univ. Brest, LaTIM, INSERM, UMR 1101（布雷斯特大学、LaTIM研究所、法国国家健康与医学研究院、UMR 1101联合研究单位）；Nuclear Medicine Department, Poitiers University Hospital（普瓦捷大学医院核医学科）
One of the major challenges in design and developing of PET, scanners are the presence of inactive areas between the detector blocks which degrade the image spatial resolution and leads to streaking artifacts especially when we employ analytical image reconstruction. The aim of this study is to assess the feasibility of generating the gap-free PET image using the deep convolutional encoder-decoder in sinogram space. The gap-corrupted sinograms of simulated HRRT scanner, sinograms without gaps as ideal/ground truth and predicted sinograms owing to the implemented our deep-fill method were quant…
University of Michigan（密歇根大学）；University of Hawai’i–Mānoa（夏威夷大学马诺阿分校）
Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR). In model-based image reconstruction (MBIR), using more iterations of an unregularized method may increase the noise, so incorporating regularization into the image reconstruction is desirable to control the noise. New regularization methods based on learned convolutional operators are emerging in MBIR. We modify the architecture of an iterative neural network, BCD-Net, for PET MBIR, a…
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map ($μ$-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as $μ$-map, reconstructs a pseudo PET (pPET) which is as close as possibl…
A standard dose of radioactive tracer must be delivered into the patients body to obtain high-quality Positron Emission Tomography (PET) images for diagnostic purposes, which raises the risk of radiation harm. A reduced tracer dose, on the other hand, results in poor image quality and a noise-induced quantitative bias in PET imaging. The partial volume effect (PVE), which is the result of PET intrinsic limited spatial resolution, is another source of quality and quantity degradation in PET imaging. The utilization of anatomical information for PVE correction (PVC) is not straightforward due to…
For single source helical Computed Tomography (CT), both Filtered-Back Projection (FBP) and statistical iterative reconstruction have been investigated. However for dual source CT with flying focal spot (DS-FFS CT), statistical iterative reconstruction that accurately models the scanner geometry and physics remains unknown to researchers. Therefore, this paper presents a novel physics-based iterative reconstruction method for DS-FFS CT and assess its image quality. Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual source helical tr…
Quantitative accuracy and thus diagnostic precision in Emission Tomography is impaired by the inherent random characteristics of the data acquisition leading to statistical image noise. Edge preserving spatial variation regularized iterative image reconstruction approaches require case-specific control parameter adaptation for optimized contrast-vs-noise tradeoff. For MLEM reconstruction, we propose and evaluate iRDF which automatically adapts RDP edge preservation parameters according to local image noise and PET data characteristics. In order to distinguish between clustered noise spots and …
