A plug-and-play method for guided multi-contrast MRI reconstruction based on content/style modeling [0.03%]
一种基于内容/风格建模的引导多对比度MRI重建方法
Chinmay Rao,Matthias van Osch,Nicola Pezzotti et al.
Chinmay Rao et al.
Since the various contrast-weighted MR images of a given anatomy contain redundant information, one contrast can be used to guide the reconstruction of another undersampled contrast acquired subsequently in the same session. To solve this r...
LMGDM: A Lesion-aware Mutual Guidance Diffusion Model with attenuation prior constraint for self-attenuation correction of whole-body PET [0.03%]
具有衰减先验约束的病变意识互导扩散模型（LMGDM）用于全身PET自我衰减校正
Shengjun Li,Kaicong Sun,Caiwen Jiang et al.
Shengjun Li et al.
Attenuation correction is critical for PET imaging to correctly reflect physiological activity. Recent studies perform PET self-attenuation correction, enabling attenuation correction from PET data itself instead of using additional CT or M...
Reconstructing shared visual experiences from human brain activity across individuals [0.03%]
跨个体重构共享视觉体验的人脑活动影像研究
Jinke Li,Yuxiao Yang,Yanyan Huang et al.
Jinke Li et al.
Reconstructing visual experiences from brain activity promises to strengthen brain-computer interfaces and our fundamental understanding of perception. However, current deep learning approaches for functional magnetic resonance imaging (fMR...
FlowLet: Conditional 3D brain MRI synthesis using wavelet flow matching [0.03%]
基于小波流匹配的条件合成三维脑弥散加权磁共振图像模型_flowlet: conditional 3d brain mri synthesis using wavelet flow matching
Danilo Danese,Angela Lombardi,Matteo Attimonelli et al.
Danilo Danese et al.
Generative modeling for 3D brain MRI is challenged by a trade-off between anatomical fidelity, sample diversity, and computational efficiency. Diffusion-based approaches achieve strong visual quality but typically require hundreds to thousa...
Zhongyi Han,Bin Wang,Shenjing Wu et al.
Zhongyi Han et al.
Deep learning has become integral to medical imaging, but its tendency to memorize training data poses serious risks for patient privacy. Machine unlearning offers a potential remedy by revoking sensitive information, yet existing approache...
PIVOTS: Aligning unseen structures using preoperative to intraoperative volume-to-surface registration for liver navigation [0.03%]
基于术前到术中体积对齐表面的配准用于肝脏导航以校正未见结构偏移的轴标点方法
Peng Liu,Bianca Güttner,Yutong Su et al.
Peng Liu et al.
Non-rigid registration is essential for augmented reality-guided laparoscopic liver surgery, as it enables the fusion of preoperative information such as tumor location and vascular structures into the limited intraoperative view, thereby e...
Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid registration [0.03%]
基于血管感知的刚体和金字塔分层非刚体配准的两阶段鲁棒3D CTA-2D DSA对齐方法
Xiaosong Xiong,Caiwen Jiang,Han Wu et al.
Xiaosong Xiong et al.
Accurate vascular structural alignment between 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) can significantly enhance visualization during percutaneous coronary intervention (PCI), thereby imp...
A false discovery rate control method using a fully connected hidden Markov random field for neuroimaging data [0.03%]
一种用于神经影像数据的全连接隐马尔可夫随机场假阳性率控制方法
Taehyo Kim,Qiran Jia,Mony J de Leon et al.
Taehyo Kim et al.
False discovery rate (FDR) control methods are essential for voxel-wise multiple testing in neuroimaging data analysis, where hundreds of thousands or even millions of tests are conducted to detect brain regions associated with disease-rela...
A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy [0.03%]
一种时空依赖感知轻量级CNN-VIT网络的3D MRF平衡加速策略
Jintao Wei,Huihui Ye,Bingchen Shao et al.
Jintao Wei et al.
The push for rapid MRI acquisition aims to enhance clinical efficiency and diagnostic consistency by shortening scan times. 3D Magnetic Resonance Fingerprinting (MRF) has emerged as a promising technique for fast, multi-parametric quantitat...
Onur Çakı,Sinan Unver,Ayse Humeyra Dur Karasayar et al.
Onur Çakı et al.
Automatic cell detection is a key task in digital pathology, where manual counting remains impractical due to its time-consuming nature and susceptibility to variability and error. Current deep learning approaches still have difficulty achi...