BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction
Harbin Institute of Technology（哈尔滨工业大学）；Chongqing University（重庆大学）；University Hospital Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-University Erlangen-Nuremberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）
Zhisheng Wang, Haijun Yu, Yixing Huang, Shunli Wang, Song Ni, Zongfeng Li, Fenglin Liu, Junning Cui
Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relatively large objects at high spatial resolutions. Recently, we devised a novel scanning mode called multiple source translation CT (mSTCT) that effectively enlarges the FOV of the micro-CT and correspondingly developed a virtual projection-based filtered backprojection (V-FBP) algorithm for reconstruction. Although V-FBP skillfully solves the truncation problem in mSTCT, it requires densely sampled projections to arrive at high-resolution reconstruction, which reduces imaging efficiency. In this paper, we developed two backprojection-filtration (BPF)-based algorithms for mSTCT, i.e., S-BPF (derivatives along source) and D-BPF (derivatives along detector). D-BPF can achieve high-resolution reconstruction with fewer projections than V-FBP and S-BPF. Through simulated and real experiments conducted in this paper, we demonstrate that D-BPF can reduce source sampling by 75% compared with V-FBP at the same spatial resolution, which makes mSTCT more feasible in practice. Meanwhile, S-BPF can yield more stable results than D-BPF, which is similar to V-FBP.
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Cardiovascular Research Foundation（心血管研究基金会）
Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, includ…
Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University（罗巴切夫斯基州立大学信息技术、数学与力学研究所）；Medical University of Vienna（维也纳医科大学）
In this paper, we address the problem of automatic three-dimensional cephalometric analysis. Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane. With the development of three-dimensional imaging techniques such as CT, several analysis methods have been proposed that extend to the 3D case. The analysis based on these methods is invariant to rotations and translations and can describe difficult skull deformation, where 2D cephalometry has no use. In this paper, we provide a wide overview of existing a…
Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；Singapore Eye Research Institute, Singapore National Eye Centre, Singapore（新加坡眼科学研究所，新加坡国家眼科中心）；School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China（武汉科技大学计算机科学与技术学院）；Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore（眼科与视觉科学学术临床计划，新加坡国立大学医学院）；Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland（分子与临床眼科学研究所，瑞士巴塞尔）；School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore（南洋理工大学化学与生物医学工程学院）；Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria（医学物理与生物医学工程中心，维也纳医科大学，维也纳，奥地利）；Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria（临床药理学系，维也纳医科大学，维也纳，奥地利）；Rothschild Foundation Hospital, Paris, France（罗特希尔德基金会医院，法国巴黎）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息与通信研究所以（I 2 R），新加坡A*STAR）
We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model's feature maps reveals that it has learned to distinguish retinal l…
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training an…
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳港协同创新研究院）；Weizhi Meditech (Foshan) Co., Ltd（佛山伟智医疗科技有限公司）；Guangdong Weiren Meditech Co., Ltd（广东伟仁医疗科技有限公司）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Shenzhen Eye Institute（深圳眼科研究院）；Shenzhen Eye Hospital（深圳眼科医院）；Jinan University（暨南大学）；Shaanxi Eye Hospital（陕西省眼科医院）；Xi’an People’s Hospital (Xi’an Fourth Hospital)（西安市人民医院（西安市第四医院））；Affiliated People’s Hospital of Northwest University（西北大学附属人民医院）
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入部门、放射物理、生物医学工程，乌梅大学）；Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统单位，工程部门，罗马生物医学学院）；Vita-Salute San Raffaele University（维塔-萨拉特·桑拉法埃莱大学）；Department of Medicine, Surgery and Dentistry, University of Salerno（医学、外科和牙科部门，萨勒诺大学）；Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel（诊断和介入神经放射学部门，放射学部门，巴塞尔大学医院）；Department of Pediatric Radiology, University Children’s Hospital Basel（儿科放射学部门，巴塞尔儿童医院）；Department of Life Science and Public Health, Università Cattolica del Sacro Cuore（生命科学与公共健康部门，圣心大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马里诺斯生物医学成像中心）；Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete（人工智能与转化成像（ATI）实验室，放射学部门，医学院，克里特大学）；Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute（放射学部门，临床科学、介入和科技（CLINTEC）部门，卡罗林斯卡研究所）；Columbia University Medical Center（哥伦比亚大学医学中心）；Department of Diagnostics and intervention, Diagnostic radiology, Umeå University（诊断与介入部门，诊断放射学，乌梅大学）
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）；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…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) automatic 3-D registration is implemented and validated for small animal image volumes so that the high-resolution anatomical MRI information can be fused with the low spatial resolution of functional PET information for the localization of lesion that is currently in high demand in the study of tumor of cancer (oncology) and its corresponding preparation of pharmaceutical drugs. Though many registration algorithms are developed and applied on human brain volumes, these methods may not be as efficient on small animal datas…
Indian Institute of Information Technology, Sri City（印度信息技术学院，斯里城）
Image to Image Translation (I2I) is a challenging computer vision problem used in numerous domains for multiple tasks. Recently, ophthalmology became one of the major fields where the application of I2I is increasing rapidly. One such application is the generation of synthetic retinal optical coherence tomographic (OCT) scans. Existing I2I methods require training of multiple models to translate images from normal scans to a specific pathology: limiting the use of these models due to their complexity. To address this issue, we propose an unsupervised multi-domain I2I network with pre-trained s…
CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
University of Bucharest（布加勒斯特大学）；University Politehnica of Bucharest（布加勒斯特理工大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carol Davila University of Medicine and Pharmacy（卡罗尔·达维拉医药大学）；Linköping University（林雪平大学）
Kyoto University（京都大学）；e-Growth Co., Ltd.（e-Growth 有限公司）；Nara Medical University（奈良县立医科大学）；Rakuwakai Otowa Hospital（洛和会音羽医院）
The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT imag…
University of Iowa（爱荷华大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Utah（犹他大学）
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registr…
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）
Guangdong Cardiovascular Institute（广东省心血管病研究所）；Guangdong Academy of Medical Sciences（广东省医学科学院）；University of Notre Dame（圣母大学）
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients. However, CT image quality is directly related to radiation dose, thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high dose like high-quality CT images (domain X) from low dose low-quality CTimages (domain Y), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a targ…
University of Oulu（奥卢大学）；University College London（伦敦大学学院）
Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is c…
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）
Institute of Biological and Medical Imaging, Helmholtz Zentrum Munich（慕尼黑亥姆霍兹中心生物与医学成像研究所）；Technical University Munich（慕尼黑工业大学）
Objective:Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of the work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging. Methods: We present a novel method for optoacoustic tomography based on an …
Nagoya University（名古屋大学）；Sapporo-Kosei General Hospital（札幌厚生综合医院）；Sapporo Minami-sanjo Hospital（札幌南三条医院）；Keiwakai Nishioka Hospital（惠和会西冈医院）；National Institute of Informatics（国立信息学研究所）
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes. Precise non-invasive diagnosis of lung cancer mainly utilizes 3D multidetector computed-tomography (CT) data. On the other hand, we can take micro CT images of resected lung specimen in 50 micro meter or higher resolution. However, micro CT scanning cannot be applied to living human imaging. For obtaining highly detailed information such as cancer invasion area from pre-operative clinical CT vol…
