Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Journal of Nuclear Medicine and Molecular Imaging
Published in: European Journal of Nuclear Medicine and Molecular Imaging 1/2021

01-01-2021 | Prostate Cancer | Original Article

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging

Authors: Andrii Pozaruk, Kamlesh Pawar, Shenpeng Li, Alexandra Carey, Jeremy Cheng, Viswanath P. Sudarshan, Marian Cholewa, Jeremy Grummet, Zhaolin Chen, Gary Egan

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 1/2021

Login to get access

Abstract

Purpose

Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images.

Methods

We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancer patients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC μ-maps and the reconstructed PET images.

Results

For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC μ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC μ-map in the pelvic region was 1.9% for μ-mapGAN + aug compared with 6.4% for μ-mapdixon, 5.9% for μ-mapdixon + bone, 2.1% for μ-mapU-Net and 2.0% for μ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug..

Conclusion

The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated μ-map and consequently the PET quantification compared to the state of the art.
Literature
1.
Hofmann M, Pichler B, Schölkopf B, Beyer T. Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques. Eur J Nucl Med Mol I. 2009;36:93–104.CrossRef
2.
Vandenberghe S, Marsden PK. PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging. Phys Med Biol. 2015;60:R115.CrossRef
3.
Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, et al. From simultaneous to synergistic MR-PET brain imaging: a review of hybrid MR-PET imaging methodologies. Hum Brain Mapp. 2018;39:5126–44.CrossRef
4.
Izquierdo-Garcia D, Catana C. MR imaging–guided attenuation correction of PET data in PET/MR imaging. PET Clinics. 2016;11:129–49.CrossRef
5.
Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal. 2016;31:1–15.CrossRef
6.
Baran J, Chen Z, Sforazzini F, Ferris N, Jamadar S, Schmitt B, et al. Accurate hybrid template–based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications. BMC Med Imaging. 2018;18:41.CrossRef
7.
Izquierdo-Garcia D, Hansen AE, Förster S, Benoit D, Schachoff S, Fürst S, et al. An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging. J Nucl Med. 2014;55:1825–30.CrossRef
8.
Sekine T, Buck A, Delso G, Ter Voert EE, Huellner M, Veit-Haibach P, et al. Evaluation of atlas-based attenuation correction for integrated PET/MR in human brain: application of a head atlas and comparison to true CT-based attenuation correction. J Nucl Med. 2016;57:215–20.CrossRef
9.
Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation-and atlas-based methods. J Nucl Med. 2011;52:1392–9.CrossRef
10.
Salomon A, Goedicke A, Schweizer B, Aach T, Schulz V. Simultaneous reconstruction of activity and attenuation for PET/MR. IEEE Trans Med Imaging. 2011;30:804–13.CrossRef
11.
Schick F. Whole-body MRI at high field: technical limits and clinical potential. Eur Radiol. 2005;15:946–59.CrossRef
12.
Gaertner FC, Fürst S, Schwaiger M. PET/MR: a paradigm shift. Cancer Imaging. 2013;13:36.CrossRef
13.
Lenzo N, Meyrick D, Turner J. Review of gallium-68 PSMA PET/CT imaging in the management of prostate cancer. Diagnostics. 2018;8:16.CrossRef
14.
Rhee H, Blazak J, Tham CM, Ng KL, Shepherd B, Lawson M, et al. Pilot study: use of gallium-68 PSMA PET for detection of metastatic lesions in patients with renal tumour. EJNMMI Res. 2016;6:76.CrossRef
15.
Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016;35:1153–9.CrossRef
16.
Li H. Deep learning for image denoising. Int J Signal Process Image Process Pattern Recognit. 2014;7:171–80.
17.
Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555:487.CrossRef
18.
Pawar K, Chen Z, Shah NJ, Egan GF. A deep learning framework for transforming image reconstruction into pixel classification. IEEE Access. 2019;7:177690–702.CrossRef
19.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention: Springer; 2015. p. 234–41.
20.
Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 1125–34.
21.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Advances in neural information processing systems; 2014. p. 2672–80.
22.
Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA. Context encoders: feature learning by inpainting. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2536–44.
23.
Zhang R, Isola P, Efros AA. Colorful image colorization. European conference on computer vision: Springer; 2016. p. 649–66.
24.
Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging–based attenuation correction for PET/MR imaging. Radiology. 2017;286:676–84.CrossRef
25.
Leynes AP, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD, Seo Y, et al. Zero-echo-time and Dixon deep pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med. 2018;59:852–8.CrossRef
26.
Torrado-Carvajal A, Vera-Olmos J, Izquierdo-Garcia D, Catalano OA, Morales MA, Margolin J, et al. Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction. J Nucl Med. 2019;60:429–35.CrossRef
27.
Dong X, Wang T, Lei Y, Higgins K, Liu T, Curran WJ, et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol. 2019;64:215016.CrossRef
28.
Varadhan R, Karangelis G, Krishnan K, Hui S. A framework for deformable image registration validation in radiotherapy clinical applications. J Appl Clin Med Phys. 2013;14:192–213.CrossRef
29.
30.
Avants BB, Tustison N, Song G. Advanced normalization tools (ANTS). Insight J. 2009;2:1–35.
31.
32.
33.
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167. 2015.
34.
Goscinski WJ, McIntosh P, Felzmann UC, Maksimenko A, Hall CJ, Gureyev T, et al. The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research. Front Neuroinform. 2014;8:30.CrossRef
35.
Kinehan P, Fletcher J. PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy. Semin Ultrasound CT MR. 2010;31:496–505.CrossRef
36.
Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.CrossRef
37.
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102–27.CrossRef
38.
Lei Y, Fu Y, Wang T, Qiu RL, Curran WJ, Liu T, et al. Deep learning in multi-organ segmentation. arXiv preprint arXiv:200110619. 2020.
39.
Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019;32:582–96.CrossRef
40.
Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. Deep learning in medical image registration: A Review. arXiv preprint arXiv:191212318. 2019.
41.
Pawar K, Chen Z, Shah NJ, Egan GF. Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation. NMR in Biomedicine. 2019:e4225.
42.
Lv J, Yang M, Zhang J, Wang X. Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study. Br J Radiol. 2018;91:20170788.CrossRef
43.
Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng. 2019;3:173.CrossRef
Metadata
Title
Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging
Authors
Andrii Pozaruk
Kamlesh Pawar
Shenpeng Li
Alexandra Carey
Jeremy Cheng
Viswanath P. Sudarshan
Marian Cholewa
Jeremy Grummet
Zhaolin Chen
Gary Egan
Publication date
01-01-2021
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 1/2021
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-020-04816-9

Other articles of this Issue 1/2021

European Journal of Nuclear Medicine and Molecular Imaging 1/2021 Go to the issue

Image of the Month

18F-FDG PET/CT of multiple cavitating pulmonary squamous cell carcinoma

Original Article

COVID-19 in the act: incidental 18F-FDG PET/CT findings in asymptomatic patients and those with symptoms not primarily correlated with COVID-19 during the United Kingdom coronavirus lockdown

Original Article

COVID-19 pneumonia: relationship between inflammation assessed by whole-body FDG PET/CT and short-term clinical outcome

Original Article

Clinical response assessment on DW-MRI compared with FDG-PET/CT after neoadjuvant chemoradiotherapy in patients with oesophageal cancer

Original Article

Usefulness of [68Ga]Ga-DOTA-FAPI-04 PET/CT in patients presenting with inconclusive [18F]FDG PET/CT findings

Letter to the Editor

Radiological protection and biological COVID-19 protection in the nuclear medicine department