Cone-beam computed tomography (CBCT) images are problematic in clinical medicine because of their low contrast and high artifact content compared with conventional CT images. Although there are some studies to improve image quality, in regions subject to organ deformation, the anatomical structure may change after such image quality improvement. In this study, we propose an overcorrection-free CBCT image quality enhancement method based on a conditional latent diffusion model using pseudo-CBCT images. Pseudo-CBCT images are created from CT images using a simple method that simulates CBCT artifacts and are spatially consistent with the CT images. By performing self-supervised learning with these spatially consistent paired images, we can improve image quality while maintaining anatomical structures. Furthermore, extending the framework of the conditional diffusion model to latent space improves the efficiency of image processing. Our model was trained on pelvic CT-pseudo-CBCT paired data and was applied to both pseudo-CBCT and real CBCT data. The experimental results using data of 75 cases show that with our proposed method, the structural changes were less than 1/1000th (in terms of the number of pixels) of those of a conventional method involving learning with real images, and the correlation coefficient between the CT value distributions of the generated and reference images was 0.916, approaching the same level as conventional methods. We also confirmed that the proposed framework achieves faster processing and superior improvement performance compared with the framework of a conditional diffusion model, even under constrained training settings.
Human-Guided Shade Artifact Suppression in CBCT-to-MDCT Translation via Schrödinger Bridge with Conditional Diffusion
National Institute for Mathematical Sciences（国家信息科学研究院）；Department of Computer Engineering, Korea National University of Transportation（计算机工程系，韩国交通国立大学）
Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss
The Hong Kong Polytechnic University（香港理工大学）；Sun Yat-Sen University（中山大学）；Fourth Military Medical University（中国人民解放军空军军医大学）；Xijing Hospital（西京医院）；Queen Mary Hospital（玛丽医院）；Guangzhou University of Chinese Medicine（广州中医药大学）；Soochow University（苏州大学）
Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT
School of Cyberspace, Hangzhou Dianzi University（电子科技大学信息学院）；Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University（电子科技大学电子设计自动化创新中心）；Hangzhou Geriatric Stomatology Hospital, Hangzhou Dental Hospital Group（杭州老年口腔医院，杭州口腔医院集团）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
Friedrich-Alexander-University Erlangen-Nuremberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Erlangen Graduate School in Advanced Optical Technologies (SAOT)（埃尔朗根先进光学技术研究生院）
Due to the lack of standardized 3D cephalometric analytic methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution. In addition, the radiation dose during the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of sy…
Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
State Key Laboratory of Advanced Medical Materials and Devices（先进医用材料与器件国家重点实验室）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
Yonsei University（延世大学）；National Institute for Mathematical Sciences（国家数学科学研究所）
Low-dose dental cone beam computed tomography (CBCT) has been increasingly used for maxillofacial modeling. However, the presence of metallic inserts, such as implants, crowns, and dental filling, causes severe streaking and shading artifacts in a CBCT image and loss of the morphological structures of the teeth, which consequently prevents accurate segmentation of bones. A two-stage metal artifact reduction method is proposed for accurate 3D low-dose maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth shape prior information from intra-oral scan data whose acquisition do…
Z 2 Sky Technologies Inc.（Z 2 天工科技公司）；University of Rochester（罗切斯特大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所 中国科学院智能信息处理重点实验室）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advance Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention. However, CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects, causing vertebra segmentation, even manually, a demanding task. In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations. To overco…
RSTAR4D: Rotational Streak Artifact Reduction in 4D CBCT using a Separable 4D CNN
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属胸科医院）；Jiangsu First-Imaging Tech.（江苏首影科技有限公司）；Research Center of Augmented Intelligence, Zhejiang Lab（之江实验室增强智能研究中心）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
University of Rochester（罗切斯特大学）；University of Maryland, College Park（马里兰大学帕克分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Carestream Health, Inc.（锐珂医疗公司）
A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the metal trace, and thus, when the metallic implant is large, significant secondary artifacts are often introduced. In this work, we propose to replace metal artifact affected regions with anatomically consistent content through joint projection-sinogram correction as well as adversaria…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Peking University Cancer Hospital（北京大学肿瘤医院）
Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We developed a semantic physics-based data augmentation method for segmenting esophagus in both planning CT (pCT) and cone-beam CT (CBCT) using 3D convolutional neural networks. 191 cases with their pCT and CBCTs from four independent datasets were used to train a modified 3D-Unet architecture with a multi-objective loss function specifically designed for soft-tissue organs such as esophagus. Scatter artifacts and noise were ex…
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. Methods: The key idea of our approach called cross modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segm…
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-…
During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image format…
National Institute for Mathematical Sciences（国家数学科学研究所）；Yonsei University（延世大学）
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations o…
During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems. Due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality greatly. To reduce the artifacts, many metal artifact reduction algorithms have been developed and metal inpainting in projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections and the inpainted image is obtained by removing the noise in conditional resampling process. The result implies…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemente…
Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification
Yonsei University（延世大学）；College of Dentistry, Yonsei University（延世大学牙科学院）；Oral Science Research Center（口腔科学研究中心）
Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡大学先进光学技术研究生院）；Siemens Healthcare GmbH（西门子医疗有限公司）
The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Rochester（罗切斯特大学）；University of Science and Technology of China（中国科学技术大学）
It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion. This work takes a step further to address a challenging setting in reconstructing a multi-phase}4D lung image from just a single}3D CBCT acquisition. To this end, we introduce REpiratory-GAted Synthesis of views, or REGAS. REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. This method allows a much better estimation of between-phase Deformation Vector Fields (DVFs), which are used…
ShanghaiTech University（上海科技大学）；The University of Edinburgh（爱丁堡大学）；The University of Hong Kong（香港大学）；Texas A&M University（得克萨斯农工大学）
Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging. Several research works have been proposed to reconstruct high-quality CBCT images from sparse-view 2D projections, but the current state-of-the-arts suffer from artifacts and the lack of fine details. In this paper, we propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields, where we have invented a novel view augmentation strategy to overcome the challenges int…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Halmstad University（哈尔姆斯塔德大学）；Elekta Instrument AB（医科达仪器公司）
CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images…
The recent application of Fourier Based Iterative Reconstruction Method (FIRM) has made it possible to achieve high-quality 2D images from a fan beam Computed Tomography (CT) scan with a limited number of projections in a fast manner. The proposed methodology in this article is designed to provide 3D Radon space in linogram fashion to facilitate the use of FIRM with cone beam projections (CBP) for the reconstruction of 3D images in a low dose Cone Beam CT (CBCT).
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Helmholtz-Zentrum hereon（亥姆霍兹赫里昂中心）；Hannover Medical School（汉诺威医学院）；Human Technopole（人类技术园）
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks seri…
Unsupervised image translation using adversarial learning has been attracting attention to improve the image quality of medical images. However, adversarial training based on the global evaluation values of discriminators does not provide sufficient translation performance for locally different image features. We propose adversarial learning with a feedback mechanism from a discriminator to improve the quality of CBCT images. This framework employs U-net as the discriminator and outputs a probability map representing the local discrimination results. The probability map is fed back to the gene…
ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
Zhejiang University（浙江大学）；Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学浙江大学-伊利诺伊大学厄巴纳-香槟分校学院）；Angelalign Inc.（时代天使公司）；Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine（浙江大学医学院口腔医学院口腔医院）
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）
The cone-beam computed tomography (CBCT) provides 3D volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBC…
Sichuan University（四川大学）；Zhejiang University School of Medicine（浙江大学医学院）；Harvard University（哈佛大学）；Zhejiang University（浙江大学）；AngelAlign Tech Inc.（时代天使科技有限公司）；Shanghai Jiao Tong University（上海交通大学）
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous studies have established several methods for CBCT segmentation using deep learning. However, the inherent resolution discrepancy of CBCT and the loss of occlusal and dentition information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental m…
Leibniz University Hanover（汉诺威莱布尼茨大学）；Hannover Medical School（汉诺威医学院）
Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming. We propose a Markov random field (MRF) mo…
From Data Completeness to Data Sufficiency: A Task-Driven Imaging Framework for Intraoperative CBCT under Quality-Time-Dose Trade-offs
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Shanghai First-Imaging Information Technology Co., LTD.（上海第一影像信息技术有限公司）
DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Cyber Science and Engineering, Sichuan University（网络科学与工程学院，四川大学）
Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction
Department of Electrical Engineering & Computer Science, University of Michigan（电气工程与计算机科学系，密歇根大学）；Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）
High quality reconstruction with interventional C-arm cone-beam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced in the backprojection operation. With prolonged acquisition times of interventional C-arm CBCT the likelihood of rigid patient motion increases. To adapt the backprojection operation accordingly, a motion estimation strategy is necessary. Recently, a novel learning-based approach was proposed, capable of compensating motions within the acquisitio…
Georgia Institute of Technology（佐治亚理工学院）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Peking University Cancer Hospital（北京大学肿瘤医院）
In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response. Using a novel physics-based da…
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e.g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step tow…
DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom（伦敦大学学院霍克斯研究所，医学物理与生物医学工程系，伦敦大学学院，英国）；Department of Computer Science, University College London, United Kingdom（伦敦大学学院计算机科学系，伦敦大学学院，英国）；Elekta, AB, Stockholm, Sweden（Elekta公司，斯德哥尔摩，瑞典）；Institute of Nuclear Medicine, University College London, United Kingdom（伦敦大学学院核医学研究所，伦敦大学学院，英国）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Generalizability is a concern when applying a deep learning (DL) model trained on one dataset to other datasets. Training a universal model that works anywhere, anytime, for anybody is unrealistic. In this work, we demonstrate the generalizability problem, then explore potential solutions based on transfer learning (TL) by using the cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion task as the testbed. Previous works have converted CBCT to CT-like images. However, all of those works studied only one or two anatomical sites and used images from the same vendor's …
