2024
Next-generation digital chest tomosynthesis
Gange C, Ku J, Gosangi B, Liu J, Maolinbay M. Next-generation digital chest tomosynthesis. Journal Of Clinical Imaging Science 2024, 14: 22. PMID: 38975057, PMCID: PMC11225395, DOI: 10.25259/jcis_4_2024.Peer-Reviewed Original ResearchRadiation doseLow dose chest CTLung phantomLung nodulesEffective radiation dosePlane resolutionLow radiation doseLow dose CTPhantomSimulated pulmonary nodulesChest CTChest radiologistsHealthy volunteersPulmonary nodulesDose CTAnatomical locationNodule detectionImaging characteristicsTomosynthesisNodulesLungDoseDetect lung nodulesDevicesPotential utility
2023
Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting
Cavallo J, de Oliveira Santo I, Mezrich J, Forman H. Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting. Journal Of The American College Of Radiology 2023, 20: 438-445. PMID: 36736547, DOI: 10.1016/j.jacr.2022.12.016.Peer-Reviewed Original ResearchConceptsEmergency department settingPulmonary nodulesCT examinationsDepartment settingSecondary reviewNumber of patientsQuality assurance studyMedian timeEmergent settingPatient followImaging recommendationsAppropriate followMajority of reviewsRadiological reportsAssurance studyClinical implementationLung anatomyQuality assurance programPatientsSignificant delayNodulesFollowExaminationPulmonary nodule detectionNodule detection
2018
End-to-End Lung Nodule Detection in Computed Tomography
Wu D, Kim K, Dong B, Fakhri G, Li Q. End-to-End Lung Nodule Detection in Computed Tomography. Lecture Notes In Computer Science 2018, 11046: 37-45. DOI: 10.1007/978-3-030-00919-9_5.Peer-Reviewed Original ResearchDeep reconstruction networkLung nodule detectionReconstruction networkEnd-to-end detectorMedical imagesLung Image Database Consortium image collectionNodule detectionEfficient network trainingReconstructed imagesConvolutional neural networkEnd-to-endSuperior detection performanceRaw dataComputer visionCAD systemCNN detectorNetwork trainingImage collectionNeural networkDetection performanceImage spaceDetection taskDetection systemModern medical imagingFanbeam projections
2016
Upper-Bound on Dose Reduction in CT Reconstruction for Nodule Detection
De Man R, Wang G, Kalra M, Otrakji A, Hsieh S, Pelc N. Upper-Bound on Dose Reduction in CT Reconstruction for Nodule Detection. IEEE Access 2016, 4: 4247-4253. DOI: 10.1109/access.2016.2592941.Peer-Reviewed Original ResearchDetection accuracyMajor CT vendorsRaw data domainMedical diagnosis toolCT reconstructionIterative image reconstructionAdditional prior informationSpecific location informationLocation informationData domainNodule detectionCT data setsDetection taskImage reconstructionRaw dataCT vendorsReconstruction algorithmReconstruction solutionCase performancePrior informationDiagnosis toolData setsImage qualityUpper boundsAchievable dose reduction
2003
Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection
Ko JP, Rusinek H, Naidich DP, McGuinness G, Rubinowitz AN, Leitman BS, Martino JM. Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection. Radiology 2003, 228: 70-5. PMID: 12775850, DOI: 10.1148/radiol.2281020254.Peer-Reviewed Original ResearchConceptsNodule detectionJoint Photographic Experts Group (JPEG) standardImage compression technologyWavelet image compressionLung CT dataLung nodule detectionPulmonary nodule detectionImage compressionJPEG2000 methodChest CT dataCompression technologyCT dataWavelet compressionCompression rateCase imagesCompression levelsAz valuesTest casesImages
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