Evidence map›Paper›PMID 40778900›Full record

Trial reportJACC. Cardiovascular imaging2025

General Purpose Deep Learning Attenuation Correction Improves Diagnostic Accuracy of SPECT MPI: A Multicenter Study.

Aakash D Shanbhag, Robert J H Miller, Mark Lemley, Paul Kavanagh, Joanna X Liang, Anna M Marcinkiewicz, Valerie Builoff, Serge Van Kriekinge, Terrence D Ruddy, Mathews B Fish and 9 more

Registry-linked trialAbstract readMulticenter StudyClinical Trial, Phase III
PubMed Full text at PMC Publisher
In one paragraph

Trial report in JACC. Cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01347710 (A Phase 3, Open Label, Multicenter Study for the Assessment of Myocardial Perfusion Using Positron Emission Tomography), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT01347710 phase3completednot on this map

A Phase 3, Open Label, Multicenter Study for the Assessment of Myocardial Perfusion Using Positron Emission Tomography (PET) Imaging of Flurpiridaz F18 Injection in Patients With Suspected or Known Coronary Artery Disease (CAD)

TypeinterventionalSponsorLantheus Medical ImagingRan2011 to 2013Enrolled795ConditionsCoronary Artery DiseaseArmsFlurpiridaz F18, 99mTechnicium (sestamibi or tetrofosmin)
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. AlEuropean journal of nuclear medicine and molecular imaging · 2026
    Article
  2. Review
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

19 authors.

Aakash D ShanbhagDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Robert J H MillerDepartment of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada.
Mark LemleyDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Paul KavanaghDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Joanna X LiangDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Anna M MarcinkiewiczDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Center of Radiological Diagnostics, National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland.
Valerie BuiloffDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Serge Van KriekingeDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Terrence D RuddyDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Mathews B FishOregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon, USA.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA; Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA.
Monica MartinsSwansea University Medical School, Swansea University, Swansea, United Kingdom.
Julian P HalcoxSwansea University Medical School, Swansea University, Swansea, United Kingdom.
Philipp A KaufmannDivision of Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Christopher BuckleyGE HealthCare, Pollards Wood, United Kingdom.
Timothy M BatemanCardiovascular Imaging Technologies, LLC, Kansas City, Missouri, USA; Saint Luke's Health System, Kansas City, Missouri, USA.
Daniel S BermanDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Damini DeyDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Piotr J SlomkaDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: piotr.slomka@cshs.org.

Funding

Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
NHLBI NIH HHS R01 HL089765NHLBI NIH HHS R35 HL161195
6 · The paper itself

Abstract

backgroundSingle-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) uses computed tomography (CT)-based attenuation correction (AC) to improve diagnostic accuracy. Deep learning (DL) has the potential to generate synthetic AC images, as an alternative to CT-based AC.

objectivesThis study evaluated whether DL-generated synthetic SPECT images could enhance accuracy of conventional SPECT MPI.

methodsStudy investigators developed a DL model in a multicenter cohort of 4,894 patients from 4 sites to generate simulated SPECT AC images (DeepAC). The model was externally validated in 746 patients from 72 sites in a clinical trial (A Phase 3 Multicenter Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD; NCT01347710) and in 320 patients from another external site. In the first external cohort, the study assessed the diagnostic accuracy for obstructive coronary artery disease (CAD)-defined as left main coronary artery stenosis ≥50% or ≥70% in other vessels-for total perfusion deficit (TPD). In the latter, the study completed change analysis and compared quantitative scores for AC, DeepAC, and nonattenuation correction (NC) with clinical scores.

resultsIn the first external cohort (mean age, 63 ± 9.5 years; 69.0% male), 206 patients (27.6%) had obstructive CAD. The area under the receiver-operating characteristic curve (AUC) of DeepAC TPD (0.77; 95% CI: 0.73-0.81) was higher than the NC TPD (AUC: 0.73; 95% CI: 0.69-0.77; P < 0.001). In the second external cohort, DeepAC quantitative scores had closer agreement with actual AC scores compared with NC.

conclusionsIn a multicenter external cohort, DeepAC improved prediction performance for obstructive CAD. This approach could enhance diagnostic accuracy in facilities using conventional SPECT systems without requiring additional equipment, imaging time, or radiation exposure.

Indexed as

Coronary Artery DiseaseCoronary CirculationCoronary StenosisDeep LearningImage Interpretation, Computer-AssistedMyocardial Perfusion ImagingTomography, Emission-Computed, Single-PhotonAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of Resultsartificial intelligenceattenuation correctiondeep learningmyocardial perfusion imagingSPECT

Identifiers

PMID40778900
PMCPMC12626045

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.