Evidence map›Paper›PMID 39831942›Full record

ArticleEJNMMI physics2025

AI-based automatic patient positioning in a digital-BGO PET/CT scanner: efficacy and impact.

John A Kennedy, Tala Palchan-Hazan, Zohar Keidar

Abstract read
In one paragraph

Article in EJNMMI physics, 2025. 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

3 authors.

John A KennedyDepartment of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel. j_kennedy@rambam.health.gov.il.ORCID http://orcid.org/0000-0002-2043-4397
Tala Palchan-HazanDepartment of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel.
Zohar KeidarDepartment of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel.ORCID http://orcid.org/0000-0002-6057-778X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated.

methodThe digital-BGO PET/CT with AI-based auto-positioning was compared (χ

resultsThe phantom validation study gave < 1.6 mm error in 15 off-centering measurements. Patient off-centering was biased 1.92 ± 1.79 cm (mean ± standard deviation) in the posterior direction which was significantly different from the 0.22 ± 1.21 cm bias in the left lateral direction (p < 0.0001, Wilcoxon). After recalibration, 27% (38/140) of the studies had off-centering results > 2.5cm for the digital-BGO, which was significantly better than the 49% (143/292, p < 0.001) before recalibration and better than for the digital-LYSO: 54% (119/222, p < 0.001) before and 55% (66/121, p < 0.001) after. On average, CT image quality was superior for non-obese patients who were most closely aligned with the isocenter: noise increased by 3.2 ± 0.1% for every 1 cm increase in off-centering. DLP increased by 144 ± 22 Gy cm for every 1 cm increase in anterior off-centering.

conclusionAI-based automatic patient positioning in a digital-BGO PET/CT scanner significantly reduces patient off-centering, thereby improving image quality and ensuring proper radiation exposure.

Indexed as

Image qualityPatient off-centeringPET/CTRadiation exposure

Identifiers

PMID39831942
PMCPMC11746997

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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.