POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Yale University（耶鲁大学）；University of California Irvine（加利福尼亚大学尔湾分校）；Canon Medical Research USA Inc.（佳能医疗研究美国公司）；Siemens Medical Solutions Inc.（西门子医疗解决方案公司）
Bo Zhou, Jun Hou, Tianqi Chen, Yinchi Zhou, Xiongchao Chen, Huidong Xie, Qiong Liu, Xueqi Guo, Yu-Jung Tsai, Vladimir Y. Panin, Takuya Toyonaga, James S. Duncan, Chi Liu
低剂量 PET 为在 PET 成像中最大限度减少辐射暴露提供了一种有价值的手段。然而，当前普遍采用额外 CT 扫描来生成用于 PET 衰减校正的衰减图（u-map）的做法显著增加了辐射剂量。为解决这一问题并进一步降低低剂量 PET 检查中的辐射暴露，我们提出了 POUR-Net——一种创新的人群先验辅助过-欠表示网络，旨在根据低剂量 PET 生成高质量衰减图。首先，POUR-Net 结合了过-欠表示网络（OUR-Net），以促进高效的特征提取，涵盖低分辨率抽象特征和精细细节特征，从而辅助全分辨率层面的深度生成。其次，作为 OUR-Net 的补充，人群先验生成机（PPGM）利用一个全面的、由 CT 派生的 u-map 数据集，提供额外的先验信息以辅助 OUR-Net 生成。OUR-Net 与 PPGM 在级联框架中的集成能够迭代细化 $μ$-map 生成，从而产生高质量的 $μ$-map。实验结果强调了 POUR-Net 的有效性，表明它是准确的无 CT 低计数 PET 衰减校正的一种有前景的解决方案，并且其性能超越了以往的基线方法。
英文摘要：
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of $μ$-map generation, resulting in the production of high-quality $μ$-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.
University of California, San Francisco（加州大学旧金山分校）；Microsoft（微软）
Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI. Conventionally, attenuated events are corrected in sinogram space using attenuation maps derived from CT or MR-derived pseudo CT. Separately, scattered events are iteratively estimated by a 3D model-based simulation using down-sampled attenuation and emission sinograms. However, no studies have investigated joint correction of attenuation and scatter using DCNN in image space. Therefore, we aim to develop and optimize a DCNN model for attenuatio…
2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction
Yale School of Medicine（耶鲁医学院）；University of California Irvine（加州大学欧文分校）；Yale University（耶鲁大学）
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…
Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge
University of Science, VNU-HCM（越南国立大学胡志明市理科大学）；University of New Brunswick（新不伦瑞克大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡）
University of California, San Francisco（加州大学旧金山分校）
Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure wa…
CT-Less Attenuation Correction Using Multiview Ensemble Conditional Diffusion Model on High-Resolution Uncorrected PET Images
Department of Medical Imaging and Radiation Sciences（医学影像与放射科学系）；Université de Sherbrooke（Sherbrooke大学）；Sherbrooke Molecular Imaging Center（Sherbrooke分子影像中心）；CRCHUS；Nantes Université（Nantes大学）；Laboratoire CRCI2NA, INSERM, CNRS（CRCI2NA实验室，INSERM，CNRS）；Imaging Research and Technology (IR&T) Inc.（影像研究与技术公司）；Research Center on Aging, Department of Medicine（衰老研究中心，医学系）；Department of Electrical Engineering and Computer Engineering（电气工程与计算机工程系）
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…
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）
An efficient approach with theoretical guarantees to simultaneously reconstruct activity and attenuation sinogram for TOF-PET
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China（数学科学国家重点实验室，数学与系统科学研究院，中国科学院，北京100190，中国）；University of Chinese Academy of Sciences, Beijing 100190, China（中国科学院大学，北京100190，中国）
ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain
School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Mathematics and Computer Science, Nanchang University（南昌大学数学与计算机学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；College of Optical Science and Engineering, Zhejiang University（浙江大学光电科学与工程学院）
Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation（亨利·杰克逊基金会神经科学与再生医学中心）；Clinical Center, National Institute of Health（美国国立卫生研究院临床中心）
With the increasing popularity of PET-MR scanners in clinical applications, synthesis of CT images from MR has been an important research topic. Accurate PET image reconstruction requires attenuation correction, which is based on the electron density of tissues and can be obtained from CT images. While CT measures electron density information for x-ray photons, MR images convey information about the magnetic properties of tissues. Therefore, with the advent of PET-MR systems, the attenuation coefficients need to be indirectly estimated from MR images. In this paper, we propose a fully convolut…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Normandie Univ（诺曼底大学）；UNIROUEN（鲁昂大学）；UNIHAVRE（勒阿弗尔大学）；INSA Rouen（鲁昂应用科学学院）
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiations. Therefore, recently, researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a data-driven approach to address this challenging problem. Specific…
