2024
Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry
Williams M, Shanbhag A, Zhou J, Michalowska A, Lemley M, Miller R, Killekar A, Waechter P, Gransar H, Van Kriekinge S, Builoff V, Feher A, Miller E, Bateman T, Dey D, Berman D, Slomka P. Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry. European Heart Journal - Cardiovascular Imaging 2024, 25: 976-985. PMID: 38376471, PMCID: PMC11210989, DOI: 10.1093/ehjci/jeae045.Peer-Reviewed Original ResearchCoronary artery calcificationRight coronary arteryAttenuation correction CTGated CTAgatston unitsLeft circumflexHazard ratioCoronary artery calcification quantificationMulti-center registryAdverse cardiovascular eventsYear follow-upConfidence intervalsLinear weighted Cohen's kappaPrognostic implicationsMulticenter registryPrognostic assessmentCardiovascular eventsArtery calcificationCoronary arteryAttenuation correctionCohen's kappaLM/LADRegistry
2021
Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study
Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, Gullberg GT, Seo Y. Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 755-765. PMID: 36059429, PMCID: PMC9438341, DOI: 10.1109/trpms.2021.3138372.Peer-Reviewed Original ResearchData management strategiesTraining dataAdvanced networksDeep learning techniquesConventional U-NetRepresentation of dataSimilarity of dataDeep learningLearning techniquesGAN networkData managementDL modelsU-NetPerformance variabilityNetworkDimensional spaceAttenuation correctionEffective trainingCardiac SPECTGlobal performanceImagesTaskLearningTrainingSpace