https://scholars.lib.ntu.edu.tw/handle/123456789/611661
標題: | Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning | 作者: | Chen K.T. Sch?rer M. Ouyang J. Koran M.E.I. Davidzon G. Mormino E. Tiepolt S. Hoffmann K.-T. Sabri O. Zaharchuk G. Barthel H. TZE-HSIANG CHEN |
關鍵字: | amyloid;florbetaben;aged;Article;cerebellum cortex;convolutional neural network;deep learning;effect size;female;human;image quality;major clinical study;male;nuclear magnetic resonance imaging;positron emission tomography;quantitative analysis;signal noise ratio;standardized uptake value ratio;transfer of learning;image processing;x-ray computed tomography;Amyloid;Deep Learning;Female;Humans;Image Processing, Computer-Assisted;Magnetic Resonance Imaging;Positron-Emission Tomography;Tomography, X-Ray Computed | 公開日期: | 2020 | 卷: | 47 | 期: | 13 | 起(迄)頁: | 2998-3007 | 來源出版物: | European Journal of Nuclear Medicine and Molecular Imaging | 摘要: | Purpose: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols. Methods: Eighty simultaneous [18F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8?years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11?years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90–110?min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with site 1 data and either (A) directly applied or (B) trained further on site 2 data using transfer learning. Networks were also trained from scratch based on (C) site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/?) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful site 2 FreeSurfer segmentations were analyzed. Results: All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B–D scored similarly or better than the ground-truth images. Conclusions: Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization. ? 2020, Springer-Verlag GmbH Germany, part of Springer Nature. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086446543&doi=10.1007%2fs00259-020-04897-6&partnerID=40&md5=859fad2d524db2331dad3e7e32695305 https://scholars.lib.ntu.edu.tw/handle/123456789/611661 |
ISSN: | 16197070 | DOI: | 10.1007/s00259-020-04897-6 |
顯示於: | 醫學工程學研究所 |
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