2023
Injectable ice slurry for reducing pericardial adipose tissue
Shuhaiber J, Tuchayi S, Bijari F, Guehl N, Wang Y, Farinelli W, Arkun K, Fakhri G, Anderson R, Garibyan L. Injectable ice slurry for reducing pericardial adipose tissue. Lasers In Surgery And Medicine 2023, 55: 674-679. PMID: 37464943, DOI: 10.1002/lsm.23709.Peer-Reviewed Original ResearchConceptsPericardial adipose tissuePericardial adipose tissue volumeChest computed tomographyPAT volumeBaseline chest computed tomographyAnimal modelsSubcutaneous adipose tissue volumesCardiovascular diseaseHigh risk of cardiovascular diseaseAdipose tissueLow patient complianceRisk of cardiovascular diseaseWeight lossPreclinical large animal modelAdipose tissue volumeLarge animal modelBariatric surgeryComputed tomographyPatient complianceHigh riskNovel treatmentTreated groupCT imagesTissue volumeCryolipolysis
2021
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Marin T, Zhuo Y, Lahoud R, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Lim R, Ma C, Chen Y, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiotherapy And Oncology 2021, 167: 269-276. PMID: 34808228, PMCID: PMC8934266, DOI: 10.1016/j.radonc.2021.09.034.Peer-Reviewed Original ResearchConceptsGross tumor volumeRadiation therapy treatment planningGross tumor volume contoursGross tumor volume delineationTherapy treatment planningIntra-observer variabilityConsensus contoursGTV contoursPre-operative CT imagesSoft tissue sarcomasRadiation oncologistsTumor volumeBone sarcomasTreatment planningAccurate contoursCT imagesDelineation procedureSarcomaSoft tissueConfidence levelRadiationPatientsHausdorff distanceMultiple contoursX-ray
2018
Deep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked autoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassification
2016
Penalized MLAA with Spatially-Encoded Anatomic Prior in TOF PET/MR
Kim K, Yang J, Fakhri G, Seo Y, Li Q. Penalized MLAA with Spatially-Encoded Anatomic Prior in TOF PET/MR. 2016, 1-4. DOI: 10.1109/nssmic.2016.8069514.Peer-Reviewed Original ResearchSynthetic CT imagesAlternating direction methodAttenuation correctionImage qualityMR-based attenuation correctionTOF-PET dataPET emission dataPET image qualityTime-of-flight (TOFDirection methodCost functionCT imagesPET/MR scannersNoise componentsConsistency conditionsAnatomical MRIterative processComputer simulationsPET dataMLAAPET/MR scansImagesBone signalEmission dataTOF
2014
Numerical Observer for Objective Assessment on Carotid Plaque Using Spectral CT
Lorsakul A, Fakhri G, Ouyang J, Worstell W, Rakvongthai Y, Laine A, Li Q. Numerical Observer for Objective Assessment on Carotid Plaque Using Spectral CT. 2014, 1-4. DOI: 10.1109/nssmic.2014.7430906.Peer-Reviewed Original ResearchMulti-energy CTNumerical observationsCarotid plaquesMatched filterCT systemDigital anthropomorphic phantomHotelling observerDual-energyPlaque featuresImage binConventional CT imagesConventional CT systemsChannelized Hotelling observerAnthropomorphic phantomClassification performanceCT imagesSpectral CTCalcified plaqueClinical classification taskProcessing stepsSimulated imagesPlaqueSuperior performance