Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners

19Citations
Citations of this article
44Readers
Mendeley users who have this article in their library.

Abstract

Purpose: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. Methods: Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. Results: No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. Conclusion: We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners.

References Powered by Scopus

U-net: Convolutional networks for biomedical image segmentation

65053Citations
N/AReaders
Get full text

U-Net: deep learning for cell counting, detection, and morphometry

1335Citations
N/AReaders
Get full text

Attenuation correction for a combined 3D PET/CT scanner

782Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients

42Citations
N/AReaders
Get full text

Combined [68 Ga]Ga-PSMA-11 and low-dose 2-[18F]FDG PET/CT using a long-axial field of view scanner for patients referred for [177Lu]-PSMA-radioligand therapy

26Citations
N/AReaders
Get full text

Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies

24Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Sari, H., Teimoorisichani, M., Mingels, C., Alberts, I., Panin, V., Bharkhada, D., … Rominger, A. (2022). Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners. European Journal of Nuclear Medicine and Molecular Imaging, 49(13), 4490–4502. https://doi.org/10.1007/s00259-022-05909-3

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 14

64%

Researcher 7

32%

Professor / Associate Prof. 1

5%

Readers' Discipline

Tooltip

Medicine and Dentistry 8

47%

Engineering 5

29%

Computer Science 2

12%

Neuroscience 2

12%

Save time finding and organizing research with Mendeley

Sign up for free