The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models [0.03%]
深度学习模型生成的动态[公式]|-FAPI-04全身PET参数成像的扫描时间影响研究
Jidong Han,Yu Liu,Meiyong Huang et al.
Jidong Han et al.
Purpose: To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric images from static PET. Compared with traditional methods, this approach req...
Quantitative accuracy of 177Lu SPECT/CT imaging using ring-shaped CZT versus dual-head NaI systems [0.03%]
基于环形CZT与双探头NaI系统的177Lu SPECT/CT显像的定量精度研究
Rachele Danieli,Clémentine Marin,Anna-Lena Theisen et al.
Rachele Danieli et al.
Background: Ring-shaped systems based on CZT are promising for quantitative imaging, but their accuracy may be affected by septal penetration of high-energy photons-given the fixed collimator-and by limitations in scatter...
Optimization of brain PET markerless head motion correction in a large human cohort [0.03%]
基于大型人群的脑PET无标志物头动校正优化研究
Tianyi Zeng,Jiazhen Zhang,Jean-Dominique Gallezot et al.
Tianyi Zeng et al.
Purpose: Uncorrected head motion in brain PET degrades image resolution and biases quantification. Previously, we evaluated the United Imaging Healthcare markerless motion tracking (UMT) system for brain PET (Baseline UMT...
Quantitative 161Tb SPECT/CT imaging for dosimetry using a ring-based digital CZT camera [0.03%]
基于环形数字CdZnTe探测器的SPECT/CT相机在161Tb定量化成像中的应用研究
Eline Zoetelief,Marcel Segbers,Johannes Hofland et al.
Eline Zoetelief et al.
Background: 161Tb-labeled compounds are emerging as promising alternatives to 177Lu-labeled compounds for radioligand therapy (RLT). The assessment of RLT safety and efficacy rely on accurate dosimetry, which necessitates...
Correcting fast irregular motion in PET: maximum-likelihood motion and activity (MLMA) reconstruction [0.03%]
PET中矫正快速不规则运动：最大可能的运动和活性（MLMA）重建
Rodrigo José Santo,Ethan Waterink,Cornelis A T van den Berg et al.
Rodrigo José Santo et al.
Purpose: Positron emission tomography (PET) imaging naturally suffers from motion blur due to long acquisitions. As such, motion-compensation provides a promising solution to improve image quality. Traditional methods for...
Improved patient models for simulation of clinically realistic 68Ga-SSTR PET and 177Lu-PRRT SPECT studies [0.03%]
改进患者模型以实现68镓-SSTR PET和177镥-PRRT SPECT研究的临床现实仿真
Johan Gustafsson,Philip Kalaitzidis,Selma Curkic Kapidzic et al.
Johan Gustafsson et al.
Background: The aim was to improve the realism of patient models for Monte-Carlo based evaluation of image-based quantification of tumour volume and activity in [68Ga]Ga-DOTA-TOC PET and [177Lu]Lu-DOTA-TATE SPECT images. ...
Improved attenuation correction registration for FDG PET/CT images using data-driven gating (DDG)-based motion match [0.03%]
基于数据驱动门控（DDG）运动匹配的FDG PET/CT图像衰减校正配准改进方法
Zoë Wilson,Manjeet Kuhar,Kuan-Hao Su et al.
Zoë Wilson et al.
Background: Advancements in PET technologies and reconstruction methods have improved spatial resolution and noise in PET/CT, making respiratory motion artefacts more impactful on image quality. The motion-match CT (MMCT)...
Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in 177Lu-DOTATATE dosimetry : The type of article: original research article [0.03%]
基于深度学习分割的177Lu-DOTATATE剂量学的分割精度评估及时间有效性的改进：原始研究论文类型文章
Tetsu Nakaichi,Yuhei Shimizu,Satoshi Nakamura et al.
Tetsu Nakaichi et al.
Background: The efficacy of deep learning-based artificial intelligence segmentation (AI-seg) in 177Lu-DOTATATE dosimetry remains underexplored. This study evaluates AI-seg's contouring accuracy, dosimetric reliability, a...
A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans [0.03%]
一种使用未经衰减校正的PET图像进行儿童患者全身快速PET成像的方法
Jingxi Hu,Yuqian Huang,Qiyang Zhang et al.
Jingxi Hu et al.
Background: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumulative radiation dose, particularly from the CT component. We propose Sn...
Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning [0.03%]
基于深度学习的儿科PET成像的时间/剂量减少方法研究
Chenyang Han,Andrew T Trout,Andi Li et al.
Chenyang Han et al.
Background: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging. However, conventional DL approaches typically require large and diver...