MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET
Med Image Comput Comput Assist Interv. 2022 Sep;13434:163-172. doi: 10.1007/978-3-031-16440-8_16. Epub 2022 Sep 16.ABSTRACTInter-frame patient motion introduces spatial misalignment and degrades parametric imaging in whole-body dynamic positron emission tomography (PET). Most current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model performance. The MCP-Net contains three modules: a motion es...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 11, 2024 Category: Radiology Authors: Xueqi Guo Bo Zhou Xiongchao Chen Chi Liu Nicha C Dvornek Source Type: research

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline
Med Image Comput Comput Assist Interv. 2023 Oct;14394:265-275. doi: 10.1007/978-3-031-47425-5_24. Epub 2024 Feb 3.ABSTRACTMagnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fu...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 4, 2024 Category: Radiology Authors: Minhui Yu Yunbi Liu Jinjian Wu Andrea Bozoki Shijun Qiu Ling Yue Mingxia Liu Source Type: research

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI
Med Image Comput Comput Assist Interv. 2023 Oct;14227:109-119. doi: 10.1007/978-3-031-43993-3_11. Epub 2023 Oct 1.ABSTRACTBrain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of su...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 23, 2024 Category: Radiology Authors: Lintao Zhang Jinjian Wu Lihong Wang Li Wang David C Steffens Shijun Qiu Guy G Potter Mingxia Liu Source Type: research

Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI
Med Image Comput Comput Assist Interv. 2023 Oct;14220:46-56. doi: 10.1007/978-3-031-43907-0_5. Epub 2023 Oct 1.ABSTRACTResting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 23, 2024 Category: Radiology Authors: Qianqian Wang Mengqi Wu Yuqi Fang Wei Wang Lishan Qiao Mingxia Liu Source Type: research

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI
Med Image Comput Comput Assist Interv. 2023 Oct;14227:109-119. doi: 10.1007/978-3-031-43993-3_11. Epub 2023 Oct 1.ABSTRACTBrain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of su...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 23, 2024 Category: Radiology Authors: Lintao Zhang Jinjian Wu Lihong Wang Li Wang David C Steffens Shijun Qiu Guy G Potter Mingxia Liu Source Type: research

Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI
Med Image Comput Comput Assist Interv. 2023 Oct;14220:46-56. doi: 10.1007/978-3-031-43907-0_5. Epub 2023 Oct 1.ABSTRACTResting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 23, 2024 Category: Radiology Authors: Qianqian Wang Mengqi Wu Yuqi Fang Wei Wang Lishan Qiao Mingxia Liu Source Type: research