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）
Tianqi Chen, Jun Hou, Yinchi Zhou, Huidong Xie, Xiongchao Chen, Qiong Liu, Xueqi Guo, Menghua Xia, James S. Duncan, Chi Liu, Bo Zhou
Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation hazards to patients and healthcare providers. Reducing the tracer injection dose and eliminating the CT acquisition for attenuation correction can reduce the overall radiation dose, but often results in PET with high noise and bias. Thus, it is desirable to develop 3D methods to translate the non-attenuation-corrected low-dose PET (NAC-LDPET) into attenuation-corrected standard-dose PET (AC-SDPET). Recently, diffusion models have emerged as a new state-of-the-art deep learning method for image-to-image translation, better than traditional CNN-based methods. However, due to the high computation cost and memory burden, it is largely limited to 2D applications. To address these challenges, we developed a novel 2.5D Multi-view Averaging Diffusion Model (MADM) for 3D image-to-image translation with application on NAC-LDPET to AC-SDPET translation. Specifically, MADM employs separate diffusion models for axial, coronal, and sagittal views, whose outputs are averaged in each sampling step to ensure the 3D generation quality from multiple views. To accelerate the 3D sampling process, we also proposed a strategy to use the CNN-based 3D generation as a prior for the diffusion model. Our experimental results on human patient studies suggested that MADM can generate high-quality 3D translation images, outperforming previous CNN-based and Diffusion-based baseline methods.
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）
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）
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）
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…
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）
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）
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…
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）
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）
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）
3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；Shanghai United Imaging Healthcare Co., Ltd.（上海联影医疗科技有限公司）；Department of Radiology and Biomedical Imaging, Yale University（耶鲁大学放射科与生物医学成像系）；Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University（石溪大学应用数学与统计系和计算机科学系）
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear deformations has been evaluated t…
CNRS（法国国家科学研究中心）；UPMC Univ Paris 06（巴黎第六大学）；INRIA Paris（法国国家信息与自动化研究所巴黎分部）；University of Porto（波尔图大学）；Samara National Research University（萨马拉国立研究大学）；Université de Toulon USTV（土伦大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；École Polytechnique（巴黎综合理工学院）
We present a new image inpainting algorithm, the Averaging and Hypoelliptic Evolution (AHE) algorithm, inspired by the one presented in [SIAM J. Imaging Sci., vol. 7, no. 2, pp. 669--695, 2014] and based upon a semi-discrete variation of the Citti-Petitot-Sarti model of the primary visual cortex V1. The AHE algorithm is based on a suitable combination of sub-Riemannian hypoelliptic diffusion and ad-hoc local averaging techniques. In particular, we focus on reconstructing highly corrupted images (i.e. where more than the 80% of the image is missing), for which we obtain reconstructions comparab…
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Yale University（耶鲁大学）；Northwestern University（西北大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital, University of Bern（伯尔尼大学医院 Inselspital，伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）；Yale School of Medicine（耶鲁医学院）
University of California, San Francisco（加州大学旧金山分校）；University of New Mexico（新墨西哥大学）；Washington University（华盛顿大学）；University of Iowa（爱荷华大学）；University of Washington（华盛顿大学）
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this paper we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly-available whole-body MRI. Specifically, we use a dataset of 56 $^{18}$F-FDG-PET/MRI exams to train a 3D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training we implemented a balanced loss function to gene…
Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
IWR, Heidelberg University（海德堡大学IWR）；Silicon Austria Labs（Silicon Austria实验室）；College of Medicine and Biological Information Engineering, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University（医学与生物信息工程学院，医学图像智能计算教育部重点实验室，东北大学）；Department of Epidemiology & Global Health, Umeå University（流行病学与全球健康系，乌梅大学）
University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Image-to-image translation plays a vital role in tackling various medical imaging tasks such as attenuation correction, motion correction, undersampled reconstruction, and denoising. Generative adversarial networks have been shown to achieve the state-of-the-art in generating high fidelity images for these tasks. However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and inter…
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…
PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Yale University（耶鲁大学生物医学工程系）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（耶鲁医学院放射学与生物医学影像系）
CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach
Hanoi University of Science and Technology（河内科技大学）；Military Central Hospital（108军事中央医院）；VinUniversity；National Institute of Advanced Industrial Science and Technology（国立先进工业技术研究院）
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…
Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. …
Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into …
Institute for Infocomm Research（信息通信研究院）；Wuhan University of Science and Technology（武汉科技大学）
Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine struct…
