Evidence map›Paper›PMID 31413426›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2019

Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

Grant T Gullberg, Alexander I Veress, Uttam M Shrestha, Jing Liu, Karen Ordovas, W Paul Segars, Youngho Seo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Grant T GullbergDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Alexander I VeressDepartment of Mechanical Engineering, University of Washington, Seattle WA, USA.
Uttam M ShresthaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Jing LiuDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Karen OrdovasDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
W Paul SegarsDepartment of Radiology, Duke University, Raleigh-Durham, North Carolina, USA.
Youngho SeoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Funding

Precision Cardiac CT: Development of a Computational Platform for Optimizing ImagingR01HL131753 · NHLBI · DUKE UNIVERSITY · PI SAMEI, EHSAN, SEGARS, WILLIAM P · 2017 to 2020
$2.7M
Energy-independent single photon molecular imaging technologyR01EB026331 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CUI, YONGGANG, SEO, YOUNGHO · 2018 to 2022
$2.6M
Dynamic cardiac SPECTR01HL135490 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GULLBERG, GRANT T, SEO, YOUNGHO · 2017 to 2020
$2.4M
NHLBI NIH HHS R01 HL131753NHLBI NIH HHS R01 HL135490NIBIB NIH HHS R01 EB026331
6 · The paper itself

Abstract

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images.

Indexed as

Bayesian reconstructioncardiacfinite element cardiac mechanical modelhybrid PET/MRIjoint entropypositron emission tomography

Identifiers

PMID31413426
PMCPMC6693867

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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.