Evidence map›Paper›PMID 32394162›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2021

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

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

Abstract readLetter
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.European journal of nuclear medicine and molecular imaging · 2026
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  20. Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Andrii PozarukMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.
Kamlesh PawarMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.
Shenpeng LiMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.
Alexandra CareyMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.
Jeremy ChengDepartment of Surgery, Central Clinical School, Monash University, Melbourne, Australia.
Viswanath P SudarshanMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.
Marian CholewaDepartment of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Rzeszow, Poland.
Jeremy GrummetDepartment of Surgery, Central Clinical School, Monash University, Melbourne, Australia.
Zhaolin ChenMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia. zhaolin.chen@monash.edu.
Gary EganMonash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia.

Funding

Australian Research Council LP170100494Australian Research Council of Excellence for Integrative Brain Function CE140100007Reignwood Cultural Foundation Reignwood Cultural Foundation
6 · The paper itself

Abstract

purposeEstimation 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.

methodsWe 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.

resultsFor 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-map

Indexed as

Deep LearningHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMalePositron-Emission TomographyProstateTomography, X-Ray ComputedAttenuation correctionDeep learningMR-PETPET-MRProstate cancerPseudo-CTPSMA

Identifiers

PMID32394162

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.