Diffusion models have become a popular approach for image generation and reconstruction due to their numerous advantages. However, most diffusion-based inverse problem-solving methods only deal with 2D images, and even recently published 3D methods do not fully exploit the 3D distribution prior. To address this, we propose a novel approach using two perpendicular pre-trained 2D diffusion models to solve the 3D inverse problem. By modeling the 3D data distribution as a product of 2D distributions sliced in different directions, our method effectively addresses the curse of dimensionality. Our experimental results demonstrate that our method is highly effective for 3D medical image reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing MRI, and sparse-view CT. Our method can generate high-quality voxel volumes suitable for medical applications.
3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland Piracicaba Dental School, University of Campinas, Piracicaba, Brazil State Key Laboratory of Oral \& Maxillofacial Reconstruction；Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School \& Hospital of Stomatology, Wuhan University, Wuhan, China Dental Imaging, University Center for Dental Medicine, University of Basel, Basel, Switzerland Department of Oral Health \& Medicine, University Center for Dental Medicine, University of Basel, Basel, Switzerland
3D-LLDM: Label-Guided 3D Latent Diffusion Model for Improving High-Resolution Synthetic MR Imaging in Hepatic Structure Segmentation
OUTTA, Republic of Korea（韩国OUTTA）；Stanford University（斯坦福大学）；Seoul National University（首尔国立大学）；Sangmyung University（Sangmyung大学）；Chung-Ang University（Chung-Ang大学）；Samsung Medical Center（三星医疗中心）；NVIDIA；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Samsung Changwon Hospital（三星昌原医院）
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Sungkyunkwan University（成均馆大学）；Ulsan National Institute of Science and Technology（蔚山科学技术院）；Shanghai Jiao Tong University（上海交通大学）；Seoul National University（首尔国立大学）
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa（海军、电气、电子与电信工程部门，热那亚大学）
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Medical Solutions USA（美国西门子医疗解决方案公司）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（西奈卫生系统卢嫩菲尔德-塔南鲍姆研究所）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
