Evidence map›Paper›PMID 34365117›Full record

ArticlePhysica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)2021

Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization.

Yuanyuan Gao, Yansong Zhu, Murat Bilgel, Saeed Ashrafinia, Lijun Lu, Arman Rahmim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Auxiliary Value of [Cancers · 2025
    Article
  4. On the partial volume effect in magnetic particle imaging.Physics in medicine and biology · 2025
    Article
  5. Article
  6. Article
  7. Article
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

6 authors.

Yuanyuan GaoSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA. Electronic address: cindygao@smu.edu.cn.
Yansong ZhuDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1M9, Canada.
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 20892, USA.
Saeed AshrafiniaDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Lijun LuSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
Arman RahmimDepartment of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1M9, Canada. Electronic address: arman.rahmim@ubc.ca.

Funding

Partial Volume Correction Methods to Improve Quantitation and Interpretation of Amyloid & Tau PET Imaging in Aging and DementiaR21AG056142 · NIA · JOHNS HOPKINS UNIVERSITY · PI RAHMIM, ARMAN, ROUSSET, OLIVIER · 2017 to 2018
$450k
Intramural NIH HHS Z99 AG999999NIA NIH HHS R21 AG056142
6 · The paper itself

Abstract

purposePositron emission tomography (PET) images tend to be significantly degraded by the partial volume effect (PVE) resulting from the limited spatial resolution of the reconstructed images. Our purpose is to propose a partial volume correction (PVC) method to tackle this issue.

methodsIn the present work, we explore a voxel-based PVC method under the least squares framework (LS) employing anatomical non-local means (NLMA) regularization. The well-known non-local means (NLM) filter utilizes the high degree of information redundancy that typically exists in images, and is typically used to directly reduce image noise by replacing each voxel intensity with a weighted average of its non-local neighbors. Here we explore NLM as a regularization term within iterative-deconvolution model to perform PVC. Further, an anatomical-guided version of NLM was proposed that incorporates MRI information into NLM to improve resolution and suppress image noise. The proposed approach makes subtle usage of the accompanying MRI information to define a more appropriate search space within the prior model. To optimize the regularized LS objective function, we used the Gauss-Seidel (GS) algorithm with the one-step-late (OSL) technique.

resultsAfter the import of NLMA, the visual and quality results are all improved. With a visual check, we notice that NLMA reduce the noise compared to other PVC methods. This is also validated in bias-noise curve compared to non-MRI-guided PVC framework. We can see NLMA gives better bias-noise trade-off compared to other PVC methods.

conclusionsOur efforts were evaluated in the base of amyloid brain PET imaging using the BrainWeb phantom and in vivo human data. We also compared our method with other PVC methods. Overall, we demonstrated the value of introducing subtle MRI-guidance in the regularization process, the proposed NLMA method resulting in promising visual as well as quantitative performance improvements.

Indexed as

Magnetic Resonance ImagingPositron-Emission TomographyAlgorithmsBrainHumansImage Processing, Computer-AssistedPhantoms, ImagingAnatomical guidanceNLMANon-Local MeansPartial Volume CorrectionPET

Identifiers

PMID34365117
PMCPMC9614701

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.