Evidence map›Paper›PMID 42253226›Full record

ArticleMedical physics2026

Comparison of deep learning and particle smoother EM methods for estimation of Rb-82 myocardial perfusion PET kinetic parameters.

Myungheon Chin, Sarah J Zou, Garry Chinn, Craig S Levin

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2026. 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

4 authors.

Myungheon ChinDepartment of Electrical Engineering, Stanford University, Stanford, California, USA.
Sarah J ZouDepartment of Electrical Engineering, Stanford University, Stanford, California, USA.
Garry ChinnDepartment of Radiology and Molecular Imaging Program at Stanford (MIPS), Stanford University, Stanford, California, USA.
Craig S LevinDepartment of Electrical Engineering, Stanford University, Stanford, California, USA.

Funding

Sun-Pan Family Fellowship FundWu Tsai Neurosciences Institute, Stanford University
6 · The paper itself

Abstract

backgroundPositron emission tomography (PET) enables quantification of dynamic physiological processes through time-resolved imaging. In PURPOSE: This work develops and evaluates two alternative kinetic analysis approaches for

methodsBoth methods were evaluated using simulated

resultsAcross 2-10 s frames, the CNN achieved the lowest relative errors for all parameters (

conclusionsOverall, the CNN provided the most accurate and robust in-distribution kinetic parameter estimates across frame durations. In contrast, PSEM exhibited parameter-dependent behavior, improving

Indexed as

Deep LearningImage Processing, Computer-AssistedMyocardial Perfusion ImagingPositron-Emission TomographyRubidium RadioisotopesAlgorithmsConvolutional Neural NetworksKineticsRubidium-82Rubidium Radioisotopesdynamic PETkinetic analysismyocardial perfusion

Identifiers

PMID42253226
PMCPMC13366064

What OpenQuestion holds

Textmetadata
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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.