MATGAN: A generative adversarial network with multi-scale attention and Huber loss for brain MRI registration [0.03%]
基于多尺度注意力和Huber损失的生成对抗网络脑部MRI配准方法研究（ MATGAN）
Fuchun Zhang,Meng Li
Fuchun Zhang
Brain magnetic resonance image registration is a fundamental task in medical image analysis. However, achieving fine-grained alignment remains challenging, particularly in brain regions with small volumes or complex anatomical structures. G...
Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study [0.03%]
基于fMRI的精神分裂症生物标志物的EEG预测：一项同步EEG-fMRI研究
Ryuta Tamano,Takeshi Ogawa,Arisa Katagiri et al.
Ryuta Tamano et al.
Objective: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using machine-learning classifiers that serve as potential fMRI-derived biom...
A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images [0.03%]
一种新颖的深度学习网络用于双气囊小肠镜图像中小肠溃疡病变的检测与鉴别诊断
Xudong Guo,Shimin Zhou,Youhan Zhang et al.
Xudong Guo et al.
Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligence (AI) model using DBE images for accurate SBUD identification and classification.
Me...
RaGAN-Seg: A relativistic adversarial enhancement and segmentation framework for nucleus segmentation in IHC images [0.03%]
RaGAN-Seg：一种针对IHC图像中细胞核分割的相对论对抗增强和分割框架
Xiuling Hu,Qingyao Xiong,Jiangang Chen et al.
Xiuling Hu et al.


Objective: Accurate nucleus segmentation in immunohistochemistry (IHC) images is essential for quantitative analysis in digital pathology. However, segmentation remains challenging due to heterogeneous tissue types, staining diffe...
Denoising of low-dose chest computed tomography images using a U-net based convolutional Autoencoder and transfer learning [0.03%]
基于U形网络的卷积自编码器和迁移学习的低剂量胸部CT图像降噪方法
Simone Damiani,Patrizio Barca,Marco Giannelli et al.
Simone Damiani et al.
Low-Dose Computed Tomography (LDCT) is a widely used imaging modality to perform CT examinations with a reduced radiation exposure to patients, but it is affected by increased image noise and artifacts with respect to standard dose imaging,...
Computational imaging of cardio-magnetic sources: reconstruction of time-varying dipoles and epicardial potentials from magnetocardiography [0.03%]
心脏磁成像的计算成像：从心电图仪重建时变偶极子和心外膜电位
Vikas R Bhat,Anitha H
Vikas R Bhat
The electrical activities in living tissues are caused by ionic movements along cell membranes, particularly in excitable tissues like neurons, cardiac cells, and skeletal muscles. The heart muscles produce currents that flow through the ti...
Parametric regression model approach for CT-based prediction of stopping power ratio for a Hounsfield look-up table [0.03%]
基于参数回归模型的CT值查表法预测停能比的CT数值方法
Masashi Yagi,Calvin Wei Yang Koh,Kah Seng Lew et al.
Masashi Yagi et al.
This study proposes a CT number (CTN)-to-stopping power ratio (SPR) calibration method that can be directly integrated into the photon therapy workflows and evaluates its robustness against tissue variations, as well as dosimetric deviation...
Effect of action potential-induced intracellular streaming on neural diffusion weighted imaging: A computational analysis [0.03%]
基于行动电位诱导细胞内流动的神经弥散张量成像效应的计算研究
AmirAli Saboorian,Bahman Vahidi
AmirAli Saboorian
Diffusion MRI relies greatly on the apparent diffusion coefficient as a key indicator of tissue microstructure, yet the way and the extent to which intracellular dynamics influence it remains elusive. In this work the role of cytoplasmic st...
A reproducible data-driven parameter optimization framework for classical skull stripping methods across heterogeneous brain MRI datasets [0.03%]
一种可重复的数据驱动的经典头骨剥离方法的参数优化框架用于异质性脑MRI数据集
Nila Prasetya Aryani,Freddy Haryanto,Siti Nurul Khotimah et al.
Nila Prasetya Aryani et al.
Accurate skull stripping is a critical preprocessing step for reliable brain MRI analysis, yet the performance of classical algorithms remains highly sensitive to parameter selection. This study proposes a statistically reproducible, datase...
Phantom-based comparison of image quality in two digital PET/CT systems with different axial fields of view [0.03%]
基于幽灵研究的两种不同轴向视场的数字PET/CT系统图像质量比较
Khomotso Marlene Legodi,Milani Qebetu,Kaluzi Banda et al.
Khomotso Marlene Legodi et al.
Objective: SiPM-based PET/CT scanners offer enhanced sensitivity and timing resolution compared with conventional detector designs. This study compared two SiPM PET/CT systems sharing the same technology platform but diff...