Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning

• Published in: European journal of nuclear medicine and molecular imaging Vol. 47; no. 13; pp. 2998 - 3007
• Main Authors: Chen, Kevin T., Schürer, Matti, Ouyang, Jiahong, Koran, Mary Ellen I., Davidzon, Guido, Mormino, Elizabeth, Tiepolt, Solveig, Hoffmann, Karl-Titus, Sabri, Osama, Zaharchuk, Greg, Barthel, Henryk
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Advanced Image Analyses (Radiomics and Artificial Intelligence); Amyloid; Artificial neural networks; Cardiology; Cerebellum; Convolution; Deep Learning; Diagnostic systems; Female; Females; Humans; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Imaging; Magnetic Resonance Imaging; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Nuclear Medicine; Oncology; Original Article; Orthopedics; Physicians; Positron emission; Positron-Emission Tomography; Protocol (computers); Radiology; Signal quality; Signal to noise ratio; Synthesis; Tomography; Tomography, X-Ray Computed; Transfer learning; Low-count imaging; PET/MRI; Transfer learning; Amyloid PET
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-020-04897-6

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 [ 18 F]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.