TY - CHAP
T1 - Fast Automated PET Image Quality Assessment by Deep Learning
AU - Hopson, Jessica B.
AU - Neji, Radhouene
AU - Reader, Andrew J.
AU - Hammers, Alexander
N1 - Funding Information:
Manuscript received December 19, 2020. This work was supported in part by Siemens Healthcare Limited.
Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - The use of simultaneous positron emission tomography - magnetic resonance imaging (PET-MR) earlier in the diagnostic pathway for memory clinic patients is inhibited by associated radiation dose and tracer costs. Low-dose PET imaging and reconstruction may overcome these limitations, but the higher noise levels compromise image quality. We have developed a convolutional neural network (CNN) capable of automating low-dose PET image quality assessments, typically executed by experienced clinicians. Automating image quality assessments may aid large-scale reconstruction hyperparameter investigations, working faster towards clinically feasible reconstructions at ultra-reduced injected doses. Preliminary work used 1800 reconstructed phantoms to predict, initially, the coefficient of variation (CV) as a noise level measure. For non-normalised and normalised test images, the mean errors in CV prediction were 3.4 ± 2.6% and 16.1 ± 11.9%, respectively. In a real patient study, data from one patient was resampled at different dose levels and reconstructed using maximum-likelihood expectation maximisation. A CNN was trained on 3200 randomly extracted patches, with simultaneous predictions of the dose and three clinician-scored metrics: global quality rating, pattern recognition, and diagnostic confidence. The median prediction for 1000 test-time patches for each dose level was taken, obtaining mean-absolute-errors of 10.20 ± 22.30 MBq, 0.27 ± 0.52, 0.11 ± 0.25 and 0.04 ± 0.04, for each metric, respectively. This study shows that it is possible to automatically predict multiple metrics, both clinician-scored and conventional quantitative image quality measures, simultaneously. Future work will include obtaining more clinically scored reconstructions, to improve training and performance of the model.
AB - The use of simultaneous positron emission tomography - magnetic resonance imaging (PET-MR) earlier in the diagnostic pathway for memory clinic patients is inhibited by associated radiation dose and tracer costs. Low-dose PET imaging and reconstruction may overcome these limitations, but the higher noise levels compromise image quality. We have developed a convolutional neural network (CNN) capable of automating low-dose PET image quality assessments, typically executed by experienced clinicians. Automating image quality assessments may aid large-scale reconstruction hyperparameter investigations, working faster towards clinically feasible reconstructions at ultra-reduced injected doses. Preliminary work used 1800 reconstructed phantoms to predict, initially, the coefficient of variation (CV) as a noise level measure. For non-normalised and normalised test images, the mean errors in CV prediction were 3.4 ± 2.6% and 16.1 ± 11.9%, respectively. In a real patient study, data from one patient was resampled at different dose levels and reconstructed using maximum-likelihood expectation maximisation. A CNN was trained on 3200 randomly extracted patches, with simultaneous predictions of the dose and three clinician-scored metrics: global quality rating, pattern recognition, and diagnostic confidence. The median prediction for 1000 test-time patches for each dose level was taken, obtaining mean-absolute-errors of 10.20 ± 22.30 MBq, 0.27 ± 0.52, 0.11 ± 0.25 and 0.04 ± 0.04, for each metric, respectively. This study shows that it is possible to automatically predict multiple metrics, both clinician-scored and conventional quantitative image quality measures, simultaneously. Future work will include obtaining more clinically scored reconstructions, to improve training and performance of the model.
UR - http://www.scopus.com/inward/record.url?scp=85124696662&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC42677.2020.9508023
DO - 10.1109/NSS/MIC42677.2020.9508023
M3 - Conference paper
AN - SCOPUS:85124696662
T3 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
BT - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
Y2 - 31 October 2020 through 7 November 2020
ER -