Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT
Pretorius, P. Hendrik
Johnson, Karen L.
King, Michael A.
UMass Chan AffiliationsDepartment of Radiology
Document TypeJournal Article
KeywordsAttenuation map generation
myocardial perfusion imaging
Biological and Chemical Physics
MetadataShow full item record
AbstractIt 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.
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. Link to article on publisher's site