文献类型： 会议论文

第一作者： Natacha Lambert

作者： Natacha Lambert ¹ ; Daniel Roquet ² ; Matéo Mahaut ³ ; Nicolas Bitouzé ⁴ ; Ga?l Chételat ² ; Abderrahim Elmoataz ⁵ ;

作者机构： 1.Neuropresage Team & GREYC Laboratory, University of Caen Normandy, Caen, France

2.Neuropresage Team, INSERM, U1237, Cyceron, University of Caen Normandy, Caen, France

3.Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Catalunya

4.Forlabs, Saint-Contest, France

5.Image Team, GREYC Laboratory, CNRS 6072, CEDEX, University of Caen Normandy, Caen, France

关键词： Neuroimaging;Image coding;Costs;PSNR;Decoding;Convolutional neural networks;Alzheimer's disease

会议名称： International Conference on Computer Graphics and Image Processing

主办单位：

页码： 139-146

摘要： Alzheimer's Disease (AD) is supported by several complementary neuroimaging scans including 18F - fluorodeoxyglucose PET (FDG-PET), 18F-AV45 florbetapir PET (AV45-PET) and tau PET. Limiting PET scans would allow reducing cost and radiation exposure. Interestingly, the early PET acquisition of the AV45 scan would allow providing information similar to a FDG-PET scan. This study aims to perform an unidirectional translation from eAV45-PET to FDG-PET to optimize the similarities between both scan so that both complementary informations could be obtained from a single visit and radiotracer injection. The eAV45-PET to FDG-PET image translation is made by a ResN et neural network as well as a specific encoding and decoding algorithms. Results show good performances (0.00160 for Mean Square Error, 0.10301 for Normalized Root Mean Square Error, 33.80288 for Peak Signal to Noise Ratio and 33.80288 for Structural Similarity), however the technique still needs to be optimized to reduce the intensity discrepancy in some brain regions especially the frontal cortex.

分类号： tp3-53