Evidence map›Paper›PMID 27452653›Full record

ArticlePhysics in medicine and biology2016

Investigation of optimization-based reconstruction with an image-total-variation constraint in PET.

Zheng Zhang, Jinghan Ye, Buxin Chen, Amy E Perkins, Sean Rose, Emil Y Sidky, Chien-Min Kao, Dan Xia, Chi-Hua Tung, Xiaochuan Pan

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Monte Carlo methods for medical imaging research.Biomedical engineering letters · 2024
    Review
  3. Article
  4. Article
  5. Article
  6. A balanced total-variation-Chambolle-Pock algorithm for EPR imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2021
    Article
  7. Article
  8. Non-convex primal-dual algorithm for image reconstruction in spectral CT.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. [A novel method of optic disk segmentation based on visual saliency and rotary scanning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2018
    Article
  13. 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

10 authors.

Zheng ZhangDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Jinghan YePhilips Healthcare, Cleveland, Ohio, USA.
Buxin ChenDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Amy E PerkinsPhilips Healthcare, Cleveland, Ohio, USA.
Sean RoseDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Emil Y SidkyDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Chien-Min KaoDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Dan XiaDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Chi-Hua TungPhilips Healthcare, Cleveland, Ohio, USA.
Xiaochuan PanDepartment of Radiology, The University of Chicago, Chicago, IL, USA.

Funding

Digital Specimen Tomosynthesis for Volumetric Imaging of Lumpectomy SpecimensR01EB018102 · NIBIB · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN · 2014 to 2017
$2.0M
Development of Advanced C-arm Cone-Beam CT for the Treatment of Liver CancerR01CA182264 · NCI · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN · 2014 to 2017
$1.8M
Limited Angle Image ReconstructionR01CA158446 · NCI · UNIVERSITY OF CHICAGO · PI SIDKY, EMIL Y · 2012 to 2015
$1.2M
NCI NIH HHS R01 CA158446NCI NIH HHS R01 CA182264NIBIB NIH HHS R01 EB018102
6 · The paper itself

Abstract

Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications.

Indexed as

AlgorithmsPhantoms, ImagingHumansImage Processing, Computer-AssistedPositron-Emission TomographyRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedWhole Body Imaging

Identifiers

PMID27452653
PMCPMC5062374

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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.