Evidence map›Paper›PMID 41490961›Full record

ArticleEJNMMI physics2026

Assessing the feasibility of quantitative SPECT imaging for low

Johan Høiness, Caroline Stokke, Eivor Hernes, Lars Tore Gyland Mikalsen, Monika Kvassheim

Abstract read
In one paragraph

Article in EJNMMI 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. Normal Tissue Dosimetry of IntraperitonealJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026
    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

5 authors.

Johan HøinessDepartment of Physics, University of Oslo, Oslo, Norway. j.s.hoiness@gmail.com.ORCID http://orcid.org/0009-0007-3312-8093
Caroline StokkeDepartment of Physics, University of Oslo, Oslo, Norway.
Eivor HernesDepartment of Nuclear Medicine, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
Lars Tore Gyland MikalsenDepartment of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
Monika KvassheimDepartment of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLead-212 (

methodsFillable phantom compartment shells of the kidneys, liver and five vertebrae (T11-L3) were 3D-printed based on a patient's CT-images. The same patient's [

resultsQuantification was relatively stable across energy windows and matrix sizes, but best results were achieved using 30-120 reconstruction updates. Low activity concentration volumes representing background and vertebral bodies (0.03-0.05 kBq/ml) were not quantifiable with deviations over 400% for all investigated reconstructions. The activity concentrations in the liver and kidneys were underestimated by 10-50% compared to the gamma counter measurements. Precision between SPECT acquisitions was higher for the larger image matrix, with standard deviations of liver and kidney measurements less than 6% for the higher activity images, and less than 8% for the lower activity images.

conclusionWe found that larger volumes, such as liver and kidneys with at least 210 Bq/ml, may be quantifiable with an accuracy of approx. 30-40%. While very low activity concentrations below 54 Bq/ml were not quantifiable, this still indicate carefully used imaging results to be of value in dosimetric calculations, also when characterising latter parts of time activity curves.

Indexed as

212Pb3D printingAnthropomorphicPhantomQuantitativeSPECT

Identifiers

PMID41490961
PMCPMC13157375

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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.