2024
Reproducibility of Semi-automated Quantitative Analyses of Tc-99m Pyrophosphate Uptake from Myocardial Perfusion Planar and SPECT Images
Goyal D, Sandoval V, Weyman C, Miller E, Liu Y. Reproducibility of Semi-automated Quantitative Analyses of Tc-99m Pyrophosphate Uptake from Myocardial Perfusion Planar and SPECT Images. Journal Of Medical And Biological Engineering 2024, 45: 84-91. DOI: 10.1007/s40846-024-00924-1.Peer-Reviewed Original ResearchStandardized uptake valueTc-99SPECT imagesStandard uptake value quantificationTransthyretin cardiac amyloidosisH/CL ratioTc-99m PYP uptakeMethodsOur study cohortInter-observer reproducibilityPlanar imagingTechnetium-99mCardiac amyloidosisClinical suspicionSPECT/CT imagingTc-99m PYP imagingTc-99m PYPConsecutive subjectsUptake valueObserver reproducibilityStudy cohortInter-observerPurposeThe aimResultsIntra-observerMyocardial uptakeSPECTReproducibility for quantification of myocardial Tc-99m PYP amyloid uptake in grade 1 patients
Sandoval V, Goyal D, Miller E, Liu Y. Reproducibility for quantification of myocardial Tc-99m PYP amyloid uptake in grade 1 patients. European Heart Journal - Cardiovascular Imaging 2024, 25: jeae142.047. DOI: 10.1093/ehjci/jeae142.047.Peer-Reviewed Original ResearchStandardized uptake valueTc-99m PYP uptakeTc-99Cardiac amyloidosisHeart failureStandard uptake value quantificationStandardized uptake value calculationSPECT imagesIntractable heart failureSystolic heart failureIntroduction of therapyPlanar imagingTc-99m pyrophosphateAmyloid patientsSPECT/CT imagingInter-reproducibilityUptake valueContralateral ratioCoefficient of variationDisease progressionH/M ratioGrade 1Blood poolPatientsH/CL ratio
2022
Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images
Miller RJH, Singh A, Otaki Y, Tamarappoo BK, Kavanagh P, Parekh T, Hu LH, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli MF, Liang JX, Dey D, Berman DS, Slomka PJ. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 387-397. PMID: 36194270, PMCID: PMC10042590, DOI: 10.1007/s00259-022-05972-w.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingArtery diseaseInvasive angiographyObstructive coronary artery diseaseDisease probabilityLow-risk patientsLow-risk populationHigh-risk populationTotal perfusion deficitHigh diagnostic accuracyS-TPDPerfusion deficitsPatient managementPatientsPerfusion imagingDiagnostic accuracyPerfusion SPECT imagesLower likelihoodGood calibrationCharacteristic curveAngiographySPECT imagesSelection biasDiseaseDuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT
Chen X, Zhou B, Xie H, Miao T, Liu H, Holler W, Lin M, Miller EJ, Carson RE, Sinusas AJ, Liu C. DuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Medical Physics 2022, 50: 89-103. PMID: 36048541, PMCID: PMC9868054, DOI: 10.1002/mp.15958.Peer-Reviewed Original ResearchConceptsLow reconstruction accuracySynthetic projectionsAbsolute percent errorImage predictionSPECT image reconstructionImage domainSinogram synthesisGround truthReconstruction accuracyImage reconstructionSinogram domainProjection angleData acquisitionMean square errorFast data acquisitionImagesReconstruction artifactsSPECT imagesSquare error
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