Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, b...

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Veröffentlicht in:	Medical image analysis 2022-08, Vol.80, p.102524-102524, Article 102524
Hauptverfasser:	Guo, Xueqi, Zhou, Bo, Pigg, David, Spottiswoode, Bruce, Casey, Michael E, Liu, Chi, Dvornek, Nicha C
Format:	Artikel
Sprache:	eng
Schlagworte:	Humans Image Processing, Computer-Assisted - methods Memory, Short-Term Neural Networks, Computer Positron-Emission Tomography - methods Whole Body Imaging - methods
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