Evidence map›Paper›PMID 40614312›Full record

ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2025

Accurate image reconstruction from reduced data in pulsed electron paramagnetic resonance imaging.

Zheng Zhang, Boris Epel, Buxin Chen, Dan Xia, Emil Y Sidky, Howard Halpern, Xiaochuan Pan

Abstract read
In one paragraph

Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zheng ZhangDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Boris EpelDepartment of Radiation & Cellular Oncology, The University of Chicago, Chicago, IL, USA.
Buxin ChenDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Dan XiaDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Emil Y SidkyDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Howard HalpernDepartment of Radiation & Cellular Oncology, The University of Chicago, Chicago, IL, USA.
Xiaochuan PanDepartment of Radiology, The University of Chicago, Chicago, IL, USA; Department of Radiation & Cellular Oncology, The University of Chicago, Chicago, IL, USA. Electronic address: xpan@uchicago.edu.

Funding

Radiation Biology of EPR Oxygen ImagesR01CA098575 · NCI · UNIVERSITY OF CHICAGO · PI HALPERN, HOWARD J · 2003 to 2022
$4.5M
Intraoperative quantitative CT imaging of breast specimen for reducing re-surgery rate and tumor cataloguingR01CA287302 · NCI · UNIVERSITY OF CHICAGO · PI XIAOCHUAN PAN, Emil Y Sidky · 2024 to 2026
$3.4M
Improved Radiation Therapy of Hypoxic Tumor Regions by Integrated PET, EPR, and MR Imaging - Resubmission 01 - Revision - 1R01CA236385 · NCI · UNIVERSITY OF CHICAGO · PI CHEN, CHIN-TU, HALPERN, HOWARD J · 2020 to 2024
$3.3M
Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01R01EB026282 · NIBIB · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN, SIDKY, EMIL Y · 2018 to 2021
$2.1M
A preclinical integrated PET/EPR imaging system-Rev3R01EB029948 · NIBIB · UNIVERSITY OF CHICAGO · PI EPEL, BORIS, KAO, CHIEN-MIN · 2020 to 2023
$1.7M
Spectral CT metal artifact correctionR01EB023968 · NIBIB · MARQUETTE UNIVERSITY · PI SCHMIDT, TALY GILAT, SIDKY, EMIL Y · 2019 to 2022
$1.4M
Algorithm-Enabled Auto-Calibrating Quantitative Dual-Energy CTR21CA263660 · NCI · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN · 2022 to 2023
$414k
NCI NIH HHS R01 CA098575NCI NIH HHS R01 CA236385NCI NIH HHS R01 CA287302NCI NIH HHS R21 CA263660NIBIB NIH HHS R01 EB023968NIBIB NIH HHS R01 EB026282NIBIB NIH HHS R01 EB029948
6 · The paper itself

Abstract

objectiveWe investigate and develop algorithms for reconstructing effective probe-density images, and then for obtaining oxygen-concentration images, from data of a subject collected at sparse views (SVs) or over a limited-angular range (LAR) for possibly achieving fast pulsed electron paramagnetic resonance imaging (EPRI). We refer to the effective probe-density image simply as the EPR image in the work.

methodsThe reconstruction problem of EPR images from SV or LAR data in pulsed EPRI is formulated as an optimization program that includes a constraint either on the total variation (TV) or on the directional-TVs (DTVs) of the EPR image. Two algorithms, referred to as TV and DTV algorithms, are developed then for reconstruction of EPR images, respectively, from SV and LAR data through solving the respective optimization programs. Oxygen-concentration image is estimated subsequently from the EPR images reconstructed.

resultsUsing numerical studies with simulated data of a digital phantom and also with real data of a physical phantom and a mouse model, we demonstrate the potential of the TV and DTV algorithms that yield, respectively, from SV and LAR data, numerically accurate EPR and oxygen-concentration images.

conclusionThe TV and DTV algorithms developed can yield numerically accurate EPR and oxygen-concentration images, respectively, from SV and LAR data in pulsed EPRI. SIGNIFICANCE: The work may yield insights into the design of scans with minimized scanning time, thus potentially enabling basic and preclinical in vivo studies with fast pulsed EPRI.

Indexed as

Image Processing, Computer-AssistedAlgorithmsAnimalsComputer SimulationElectron Spin Resonance SpectroscopyMiceOxygenPhantoms, ImagingOxygenElectron paramagnetic resonance imaging (EPRI)Optimization-based reconstructionPrimal–dual algorithmPulsed EPRI

Identifiers

PMID40614312
PMCPMC13502257

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.