Chen, XiongchaoPretorius, P. HendrikZhou, BoLiu, HuiJohnson, Karen L.Liu, Yi-HwaKing, Michael ALiu, Chi2022-08-232022-08-232022-04-262022-07-15<p>Chen X, Hendrik Pretorius P, Zhou B, Liu H, Johnson K, Liu YH, King MA, Liu C. Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT. J Nucl Cardiol. 2022 Apr 26. doi: 10.1007/s12350-022-02978-7. Epub ahead of print. PMID: 35474443. <a href="https://doi.org/10.1007/s12350-022-02978-7">Link to article on publisher's site</a></p>1071-3581 (Linking)10.1007/s12350-022-02978-735474443https://hdl.handle.net/20.500.14038/48646It has been proved feasible to generate attenuation maps (mu-maps) from cardiac SPECT using deep learning. However, this assumed that the training and testing datasets were acquired using the same scanner, tracer, and protocol. We investigated a robust generation of CT-derived mu-maps from cardiac SPECT acquired by different scanners, tracers, and protocols from the training data. We first pre-trained a network using 120 studies injected with (99m)Tc-tetrofosmin acquired from a GE 850 SPECT/CT with 360-degree gantry rotation, which was then fine-tuned and tested using 80 studies injected with (99m)Tc-sestamibi acquired from a Philips BrightView SPECT/CT with 180-degree gantry rotation. The error between ground-truth and predicted mu-maps by transfer learning was 5.13 +/- 7.02%, as compared to 8.24 +/- 5.01% by direct transition without fine-tuning and 6.45 +/- 5.75% by limited-sample training. The error between ground-truth and reconstructed images with predicted mu-maps by transfer learning was 1.11 +/- 1.57%, as compared to 1.72 +/- 1.63% by direct transition and 1.68 +/- 1.21% by limited-sample training. It is feasible to apply a network pre-trained by a large amount of data from one scanner to data acquired by another scanner using different tracers and protocols, with proper transfer learning.en-USAttenuation map generationSPECT/CTmyocardial perfusion imagingtransfer learningBiological and Chemical PhysicsCardiologyMedical BiophysicsRadiologyJournal Articlehttps://escholarship.umassmed.edu/radiology_pubs/70930234297radiology_pubs/709