This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-based methods in PET attenuation correction and low-count PET reconstruction by listing and comparing the proposed methods, study designs and reported performances of the current published studies with brief discussion on representative studies. The contributions and challenges among the reviewed studies were summarized and highlighted in the discussion part followed by.
Yuanpei College Peking University（北京大学元培学院）；Massachusetts General Hospital（麻省总医院）；College of Computer Science Zhejiang University（浙江大学计算机学院）
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a so…
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…
Image quality of PET reconstructions is degraded by subject motion occurring during the acquisition. MR-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of…
University of Victoria（维多利亚大学）；BC Cancer（不列颠哥伦比亚癌症中心）；University of British Columbia（不列颠哥伦比亚大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute Centre（荷兰癌症研究所中心）；University of Twente（特温特大学）
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frames from different breathing amplitude ranges. The trained model aligns different retrospectively-gated PET images, providing a final image with similar counting statistics as a non-gated image, but without the blurring effects. FlowNet-PET was applied to anthropomorphic digital phantom data, which provided the possibility to design robust metrics to quantify the corrections. When compar…
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.
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of AI models for PET image e…
Yale University（耶鲁大学）；Yale New Haven Hospital（耶鲁纽黑文医院）
In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity ($λ$-MLAA) and attenuation map ($μ$-MLAA) based on the PET raw data only. However, $μ$-MLAA suffers from high noise and $λ$-MLAA suffers from large bias as compared to the rec…
University of California, San Francisco（加州大学旧金山分校）；University of California, Berkeley（加州大学伯克利分校）；University of Colorado（科罗拉多大学）
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease. These reconstruction approaches utilize priors, either structural or statistical, together with a physics-based description of the PET system response. However, due to the nested representation of the forward problem, direct PET/MRI reconstruction is a nonlinear problem. We elucidate how a multi-faceted approach accom…
Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University（北卡罗来纳大学教堂山分校与北卡罗来纳州立大学生物医学工程联合系）；Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属精神卫生中心）
In a low-statistics PET imaging context, the positive bias in regions of low activity is a burning issue. To overcome this problem, algorithms without the built-in non-negativity constraint may be used. They allow negative voxels in the image to reduce, or even to cancel the bias. However, such algorithms increase the variance and are difficult to interpret, since negative radioactive concentrations have no physical meaning. Here, we propose a post-processing strategy to remove negative intensities while preserving the local mean activities. Our idea is to transfer the negative intensities to …
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）
Positron emission tomography (PET) suffers from severe resolution limitations which limit its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate the resolution recovery process, we incorporate high-resolution (HR) anatomical information based on magnetic resonance (MR) imaging. We introduce the spatial location information of the input image patches as additional CNN inputs to accommodate the spatially-variant nature of the blur kernels in PET. We compared the performance of shallow (3-…
Gordon Center for Medical Imaging（戈登医学影像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Martinos Center for Biomedical Imaging（马蒂诺斯生物医学影像中心）；University of California, Davis（加州大学戴维斯分校）
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels c…
Indian Institute of Technology (IIT) Bombay（印度理工学院孟买分校）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什研究学院）；Monash University（莫纳什大学）
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, s…
ARC Training Centre for Innovative BioEngineering（ARC创新生物工程培训中心）
Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue sarcomas (STSs). Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of STSs. It is difficult to determine from imaging studies which STS patients will develop metastases. 'Radiomics' refers to the extraction and analysis of quantitative features from medical images and it has been employed to help identify such tumors. The state-of-the-art in ra…
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Neuroscience Research Institute（神经科学研究所）；Gachon University of Medicine and Science（嘉泉医科大学）
Stanford University（斯坦福大学）；GE Healthcare（通用电气医疗集团）；University of California Riverside（加州大学河滨分校）
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is considered the gold-standard for the measurement of CBF in humans. PET imaging, however, is not widely available because of its prohibitive costs and use of short-lived radiopharmaceutical tracers that typically require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more readily accessible and does not involve ionizing radiation. This study presents a…
Technical University of Munich（慕尼黑工业大学）；European Institute for Molecular Imaging（欧洲分子成像研究所）；University of Münster（明斯特大学）
We propose a novel dynamic image reconstruction method from PET listmode data that could be particularly suited to tracking single or small numbers of cells. In contrast to conventional PET reconstruction our method combines the information from all detected events not only to reconstruct the dynamic evolution of the radionuclide distribution, but also to improve the reconstruction at each single time point by enforcing temporal consistency. This is achieved via optimal transport regularization where in principle, among all possible temporally evolving radionuclide distributions consistent wit…
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 fro…
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）
Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging
School of Biomedical Engineering, University of British Columbia（英属哥伦比亚大学生物医学工程学院）；BC Cancer Research Institute（不列颠哥伦比亚省癌症研究所）；Department of Radiology, University of British Columbia（英属哥伦比亚大学放射学系）；Department of Physics, University of British Columbia（英属哥伦比亚大学物理系）；Department of Earth, Ocean and Atmospheric Sciences（地球、海洋与大气科学系）
Data Science Institute, University of British Columbia（不列颠哥伦比亚大学数据科学研究所）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）
Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult in large part due to privacy concerns. For this reason, generative image models are highly sought after to facilitate data sharing. However, 3-D generative models are understudied, and investigation of their privacy leakage is needed. We introduce our 3-D generative model, Transversal GAN (TrGAN), using head & neck PET images which are conditioned on tumour masks as a case study. We define quantitative measures of image fidelity, utility and privacy for our model. These metric…
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches to automate longtudinal analysis present many challenges including lack of available longitudinal datasets, managing complex large multimodal imaging examinations, and need for detailed annotations for traditional supervised machine learning. In this work we develop OncoNet, novel machine learning algorithm that assesses treatment response from a 1,954 pairs of sequential FDG PET/CT exams through weak supervision usi…
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
Finden Ltd（芬登有限公司）；Rutherford Appleton Laboratory（卢瑟福·阿普尔顿实验室）；Science & Technology Facilities Council（科学与技术设施委员会）；National Physical Laboratory（国家物理实验室）；Électricité de France（法国电力公司）；CSIRO（联邦科学与工业研究组织）；Technical University of Denmark（丹麦技术大学）；University College London（伦敦大学学院）；KU Leuven（鲁汶大学）；Institute of Nuclear Medicine, University College London（伦敦大学学院核医学研究所）
Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans
Technical University of Munich（慕尼黑工业大学）；TUM School of Natural Sciences（慕尼黑工业大学自然科学学院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Munich Institute of Biomedical Engineering（慕尼黑生物医学工程研究所）
Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（斯蒂文斯神经影像与信息学研究所，凯克医学院，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California（明希部门电气与计算机工程系，维特比工程学院，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，维特比工程学院，南加州大学）
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
Department of Biomedical Engineering, Wake Forest University School of Medicine（生物医学工程系，威克森林大学医学院）；Department of Radiology, Wake Forest School of Medicine（放射学系，威克森林医学院）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射学与生物医学成像系，耶鲁医学院）
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Shandong University（山东大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible …
Leiden University Medical Center（莱顿大学医学中心）；Philips（飞利浦）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Delft University of Technology（代尔夫特理工大学）
Positron Emission Tomography (PET) is an imaging method that can assess physiological function rather than structural disturbances by measuring cerebral perfusion or glucose consumption. However, this imaging technique relies on injection of radioactive tracers and is expensive. On the contrary, Arterial Spin Labeling (ASL) MRI is a non-invasive, non-radioactive, and relatively cheap imaging technique for brain hemodynamic measurements, which allows quantification to some extent. In this paper we propose a convolutional neural network (CNN) based model for translating ASL to PET images, which …
Stanford University（斯坦福大学）；GE Healthcare（通用电气医疗集团）；University of California Riverside（加州大学河滨分校）；National Taiwan University（台湾大学）
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomography (PET) is currently regarded as the gold standard for the measurement of CBF in the human brain. PET imaging, however, is not widely available because of its prohibitive costs, use of ionizing radiation, and logistical challenges, which require a co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope. Magnetic resonance imaging (MRI), in contrast, is more readil…
PAII Inc.（PAII公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chang Gung Memorial Hospital（长庚纪念医院）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；National Tsing Hua University（清华大学（台湾））；Ping An Technology（平安科技）
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN). Determining and delineating the spread of GTVLN is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose an effective distance-based gating approach to simulate and simplify the high-level reasoning protocols conducted by radiation o…
While Positron emission tomography (PET) imaging has been widely used in diagnosis of number of diseases, it has costly acquisition process which involves radiation exposure to patients. However, magnetic resonance imaging (MRI) is a safer imaging modality that does not involve patient's exposure to radiation. Therefore, a need exists for an efficient and automated PET image generation from MRI data. In this paper, we propose a new frequency-aware attention U-net for generating synthetic PET images. Specifically, we incorporate attention mechanism into different U-net layers responsible for es…
Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机学院）；Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University（首都医科大学北京友谊医院核医学科）；Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health（首都医科大学北京儿童医院放射科）
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
Department of Computing Science, Umeå University（计算科学系，乌梅大学）；Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Umeå University（诊断与介入系，生物医学工程与放射物理，乌梅大学）
