Monochromatic breast computed tomography with synchrotron radiation: phase-contrast and phase-retrieved image comparison and full-volume reconstruction [0.03%]
单色乳腺CT成像：基于同步辐射的相位对比和相位恢复图像比较及容积重建
Luca Brombal,Bruno Golosio,Fulvia Arfelli et al.
Luca Brombal et al.
A program devoted to performing the first in vivo synchrotron radiation (SR) breast computed tomography (BCT) is ongoing at the Elettra facility. Using the high spatial coherence of SR, phase-contrast (PhC) imaging techniques can be used. T...
Moving table magnetic particle imaging: a stepwise approach preserving high spatio-temporal resolution [0.03%]
移动式磁粒子成像：一种保持高时空分辨率的分步方法
Patryk Szwargulski,Nadine Gdaniec,Matthias Graeser et al.
Patryk Szwargulski et al.
Magnetic particle imaging (MPI) is a highly sensitive imaging method that enables the visualization of magnetic tracer materials with a temporal resolution of more than 46 volumes per second. In MPI, the size of the field of view (FoV) scal...
Multireader sample size program for diagnostic studies: demonstration and methodology [0.03%]
多阅片者诊断研究的样本量计算程序：演示与方法学
Stephen L Hillis,Kevin M Schartz
Stephen L Hillis
The software "Multireader sample size program for diagnostic studies," written by Kevin Schartz and Stephen Hillis, performs sample size computations for diagnostic reader-performance studies. The program computes the sample size needed to ...
Haichong K Zhang,Alexis Cheng,Younsu Kim et al.
Haichong K Zhang et al.
Accurate tracking and localization of ultrasound (US) images are used in various computer-assisted interventions. US calibration is a preoperative procedure to recover the transformation bridging the tracking sensor and the US image coordin...
Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images [0.03%]
基于冠状动脉光学相干断层扫描图像中A线上 plaques 的深度神经网络分类方法研究
Chaitanya Kolluru,David Prabhu,Yazan Gharaibeh et al.
Chaitanya Kolluru et al.
We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of > 500 microscopic-resolution images, cr...
Fully connected neural network for virtual monochromatic imaging in spectral computed tomography [0.03%]
光谱CT虚拟单色成像的全连接神经网络法
Chuqing Feng,Kejun Kang,Yuxiang Xing
Chuqing Feng
Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including...
Dynamic fluence field modulation for miscentered patients in computed tomography [0.03%]
针对CT扫描偏心患者的动态剂量分布调制方法研究
Andrew Mao,Grace J Gang,William Shyr et al.
Andrew Mao et al.
Traditional CT image acquisition uses bowtie filters to reduce dose, x-ray scatter, and detector dynamic range requirements. However, accurate patient centering within the bore of the CT scanner takes time and is often difficult to achieve ...
Automated segmentation of cellular images using an effective region force [0.03%]
一种有效区域力的细胞图像自动化分割方法
Khadeejah Mohiuddin,Justin W L Wan
Khadeejah Mohiuddin
Understanding the behavior of cells is an important problem for biologists. Significant research has been done to facilitate this by automating the segmentation of microscopic cellular images. Bright-field images of cells prove to be partic...
Imaging biomarkers in thyroid eye disease and their clinical associations [0.03%]
甲状腺眼病的影像生物标志物及其临床相关性
Shikha Chaganti,Katrina Nelson,Kevin Mundy et al.
Shikha Chaganti et al.
The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multiatlas segmentation of computed tomography (CT) imaging. Images of 170 orbits of 85 retrospectively selected TED patients...
Breast ultrasound lesions recognition: end-to-end deep learning approaches [0.03%]
乳腺超声病变识别的端到端深度学习方法
Moi Hoon Yap,Manu Goyal,Fatima M Osman et al.
Moi Hoon Yap et al.
Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolu...