Positron Emission Tomography (PET) scanners are usually designed with the goal to obtain the best compromise between sensitivity, resolution, field-of-view size, and cost. Therefore, it is difficult to improve the resolution of a PET scanner with hardware modifications, without affecting some of the other important parameters. Iterative image reconstruction methods such as the ordered subsets expectation maximization (OSEM) algorithm are able to obtain some resolution recovery by using a realistic system response matrix that includes all the relevant physical effects. Nevertheless, this resolution recovery is often limited by reduced sampling in the projection space, determined by the geometry of the detector. The goal of this work is to improve the resolution beyond the detector size limit by increasing the sampling with data-driven interpolated data. A maximum-likelihood estimation of the counts in each virtual sub-line-of-response (subLOR) is obtained after a complete image reconstruction, conserving the statistics of the initial data set. The new estimation is used for the next complete reconstruction. The method typically requires two or three of these full reconstructions (superiterations). We have evaluated it with simulations and real acquisitions for the Argus and Super Argus preclinical PET scanners manufactured by SMI, considering different types of increased sampling. Quantitative measurements of recovery and resolution evolution against noise per iteration for the standard OSEM and successive superiterations show promising results. The procedure is able to reduce significantly the impact of depth-of-interaction in large crystals, and to improve the spatial resolution. The proposed method is quite general and it can be applied to other scanners and configurations.
With the newly gained interest in the time of flight method for positron emission tomography (TOF-PET), many options for pushing the time resolution to its borders have been investigated. As one of these options the exploitation of the Cherenkov effect has been proposed, since it allows to bypass the scintillation process and therefore provides almost instantaneous response to incident 511keV annihilation photons. Our simulation studies on the yield of Cherenkov photons, their arrival rate at the photon detector and their angular distribution reveal a significant influence by Cherenkov photons…
Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space
LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale（洛里昂医学成像与组织实验室，法国国家健康与医学研究院 UMR 1101，布列塔尼西部大学）；CentraleSupélec, IETR, CNRS UMR 6164（中央苏佩莱克高等学院，IETR，法国国家科学研究中心 UMR 6164）；Inria Monc, Université de Bordeaux, Bordeaux INP, CNRS（法国国家信息与自动化研究所蒙彼利埃中心，波尔多大学，波尔多国立理工学院，法国国家科学研究中心）；Nuclear Medicine Department, Poitiers University Hospital（核医学科，普瓦捷大学医院）
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…
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…
Generative-Model-Based Fully 3D PET Image Reconstruction by Conditional Diffusion Sampling
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Institute of Psychiatry, Psychology and Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；King’s College London & Guy’s and St Thomas’ PET Centre（伦敦国王学院与盖伊和圣托马斯 PET 中心）
Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Institute of Psychiatry, Psychology and Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；King’s College London（伦敦国王学院）；Guy’s & St Thomas’ PET Centre（盖伊和圣托马斯PET中心）
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Department of Physics and Technology, UiT The Arctic University of Norway（物理与技术系，UiT 北极大学）
Positron Emission Tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using novel dilated convolutional neural network to recover full-count images from low-count images. We adopted similar hierarchal structure from the conventional uNet and incorporated dilated kernels in each convolution to allow the network to observe larger, and perhaps, more robust, features within the image. Our dNet were trained alongside a uNet for comparison. Our 2.5D model used a training se…
Arizona State University（亚利桑那州立大学）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；University of Southern California（南加州大学）
Background: Beta-amyloid (A$β$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATN framework'' of AD. Current methods to detect A$β$/tau pathology include cerebrospinal fluid (CSF; invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (BBBM; promising but mainly still in development). Objective: To develop a non-invasiv…
Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging
Beijing Normal University（北京师范大学）；Beijing Friendship Hospital, Capital Medical University（首都医科大学北京友谊医院）；The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部附属第一医院）；Jiangnan University（江南大学）；University of Melbourne（墨尔本大学）
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）
