Reducing motion artifacts in craniocervical background subtraction angiography with deformable registration and unsupervised deep learning [0.03%]
基于可变形配准和无监督深度学习的头颈部背景消除血管造影运动伪影减少方法研究
Chaochao Zhou,Ramez N Abdalla,Dayeong An et al.
Chaochao Zhou et al.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to regis...
Yifan Peng,Qingyu Chen,George Shih
Yifan Peng
Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients [0.03%]
基于深度学习的T1-MPRAGE磁共振成像技术在记忆丧失患者脑体积定量及分级中的应用研究
Nelson Gil,Azadeh Tabari,Dominik Nickel et al.
Nelson Gil et al.
Purpose: To evaluate a physics-based deep-learning-accelerated super-resolution T1-weighted MPRAGE sequence (DL-MPRAGE) against standard 3-dimensional T1-weighted MPRAGE (STD-MPRAGE) for quantitative and qualitative regio...
Quantification of myocardial oxygen extraction fraction on noncontrast MRI enabled by deep learning [0.03%]
基于深度学习的非对比度磁共振心肌氧摄取分数定量研究
Ran Li,Cihat Eldeniz,Keyan Wang et al.
Ran Li et al.
Purpose: To develop a new deep learning enabled cardiovascular magnetic resonance (CMR) approach for noncontrast quantification of myocardial oxygen extraction fraction (mOEF) and myocardial blood volume (MBV) in vivo. ...
Comparing quantitative imaging biomarker alliance volumetric CT classifications with RECIST response categories [0.03%]
定量影像生物标志物联盟体积CT分类与RECIST反应类别的比较研究
Binsheng Zhao,Nancy Obuchowski,Hao Yang et al.
Binsheng Zhao et al.
Purpose: To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Sol...
Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease [0.03%]
冠状动脉疾病患者基于动态CT灌注成像的血流储备分数测量研究
Aaron So,Ki Seok Choo,Ji Won Lee et al.
Aaron So et al.
Purposes: The objective was to evaluate the accuracy of a novel CT dynamic angiographic imaging (CT-DAI) algorithm for rapid fractional flow reserve (FFR) measurement in patients with coronary artery disease (CAD). ...
Estimating time-to-total knee replacement on radiographs and MRI: a multimodal approach using self-supervised deep learning [0.03%]
基于自我监督深度学习的多模态估计膝关节置换时间：基于X光片和MRI影像的方法
Ozkan Cigdem,Shengjia Chen,Chaojie Zhang et al.
Ozkan Cigdem et al.
Purpose: Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within ...
Magnetic particle imaging enables nonradioactive quantitative sentinel lymph node identification: feasibility proof in murine models [0.03%]
磁性粒子成像能够实现非放射性的定量前哨淋巴结识别：小鼠模型中的可行性研究证明
Olivia C Sehl,Kelvin Guo,Abdul Rahman Mohtasebzadeh et al.
Olivia C Sehl et al.
Background: Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic staging procedure. Conventional SLNB procedures with 99mTc radiotracers and scintigraphy are constrained by tracer half-life and, in some cas...
Different sodium concentrations of noncancerous and cancerous prostate tissue seen on MRI using an external coil [0.03%]
不同钠浓度的良性及恶性前列腺组织的MRI表现(应用体外线圈)
Josephine L Tan,Vibhuti Kalia,Stephen E Pautler et al.
Josephine L Tan et al.
Background: Sodium (23Na) MRI of prostate cancer (PCa) is a novel but underdocumented technique conventionally acquired using an endorectal coil. These endorectal coils are associated with challenges (e.g., a nonuniform s...
Deep generative model of the distal tibial classic metaphyseal lesion in infants: assessment of synthetic images [0.03%]
婴儿远端经典干骺病变的深度生成模型：合成图像评估
Shaoju Wu,Sila Kurugol,Paul K Kleinman et al.
Shaoju Wu et al.
Background: The classic metaphyseal lesion (CML) is a distinctive fracture highly specific to infant abuse. To increase the size and diversity of the training CML database for automated deep-learning detection of this fra...