Evidence map›Paper›PMID 37293349›Full record

ArticleRadiology. Artificial intelligence2023

AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.

Yan-Ran Joyce Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow and 2 more

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In one paragraph

Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
  2. Article
  3. Article
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  5. Article
  6. Review
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  8. Review
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

12 authors.

Yan-Ran Joyce Wang *From the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0002-1369-3892
Liangqiong Qu *From the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).
Natasha Diba SheybaniFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0002-1137-058X
Xiaolong LuoFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0001-6192-7342
Jiangshan WangFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0009-0005-8912-3676
Kristina Elizabeth HawkFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).
Ashok Joseph TheruvathFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0003-0764-6800
Sergios GatidisFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0002-6928-4967
Xuerong XiaoFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0002-3926-8325
Allison PribnowFrom the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0001-7069-3402
Daniel Rubin *From the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).ORCID 0000-0001-5057-4369
Heike E Daldrup-Link *From the Departments of Biomedical Data Science (Y.R.J.W., L.Q., N.D.S., K.E.H., X.X., D.R., H.E.D.L.), Radiology (Y.R.J.W., A.J.T.), and Nuclear Medicine (K.E.H.), Stanford University, Stanford, CA 94304; Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany (S.G.); School of Engineering, University of Science and Technology of China, Hefei, China (X.L., J.W.); and Department of Pediatrics, Division of Pediatric Oncology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, Calif (A.P., D.R., H.E.D.L.).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank Results: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET ( Conclusion: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.

Indexed as

Convolutional Neural Network (CNN)Dose ReductionPediatricsPET

Identifiers

PMID37293349
PMCPMC10245181

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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.