Yanis Chemli, PhD
Postdoctoral AssociateAbout
Research
Publications
2026
Scan-wise generalized PET denoising with contrastive adversarial learning
Liu X, Marin T, Eslahi S, Tiss A, Chemli Y, Najmaoui Y, Jang S, Fakhri G, Ouyang J. Scan-wise generalized PET denoising with contrastive adversarial learning. Physics In Medicine And Biology 2026, 71: 105030. PMID: 42173146, PMCID: PMC13312193, DOI: 10.1088/1361-6560/ae7231.Peer-Reviewed Original ResearchConceptsAdversarial frameworkContrastive lossDomain generalizationCross-entropyPeak signal-to-noise ratioSuperior denoising performanceContrastive learning schemeStructural similarity indexNoise realizationsAdversarial learningAdversarial trainingDenoising performanceSignal-to-noise ratioLearning schemeNegative pairsDeep learningAdversarial methodsPositive pairsStandard baselinesMutual informationDenoisingPerformance degradationSimilarity indexDistribution shiftsDomain distributionUnsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift
Liu X, Xia M, Chemli Y, Fakhri G, Liu C, Ouyang J. Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift. 2026, 00: 1-5. DOI: 10.1109/isbi61048.2026.11515918.Peer-Reviewed Original ResearchUnsupervised domain adaptationLabel shiftPseudo-labelsSupervised learningSelf-trainingPseudo-label selectionBox regressionDomain adaptationUnsupervised adaptationCovariate shiftConfidence thresholdLesion detectionLearningLabelingDetectionAnchor shapeUnsupervisedFROCHistogramAdaptationSize compositionPseudoAI‑driven multi-lesion detection in whole‑body FDG PET/CT
Liu X, Xia M, Chemli Y, Fakhri G, Liu C, Ouyang J. AI‑driven multi-lesion detection in whole‑body FDG PET/CT. Progress In Biomedical Optics And Imaging 2026, 13928: 7. DOI: 10.1117/12.3087729.Peer-Reviewed Original ResearchWhole-body FDG PET/CTFDG-PET/CTLesion detectionFDG-PET/CT studiesIntersection-over-unionOncologic PET/CTLesion detection networkDetection of lesionsDeep learning modelsCT informationDiagnostic accuracyPET-onlyTreatment planningLesion sizePET/CTObject detectorsEfficiency of radiologistsIoU thresholdLesionsDetection modelPublic datasetsDetectorDetection networkNumerous lesionsLocalization performanceExploring the limits of deep-learning‑based PET image denoising for lesion detectability
Bayerlein R, Xia M, Ouyang J, Chemli Y, Melnichuk D, Fakhri G, Nardo L, Liu C, Badawi R. Exploring the limits of deep-learning‑based PET image denoising for lesion detectability. Progress In Biomedical Optics And Imaging 2026, 13928: 5. DOI: 10.1117/12.3085222.Peer-Reviewed Original ResearchDenoised imageDL-based denoisersLesion contrastDetectability of low-contrast lesionsDL-basedInformation diffusion modelDeep learning denoisingPET image qualityImage qualityVisual image qualityArea under the ROC curveActivity concentration ratioLow-contrast lesionsOverall image appearanceNoisy imagesImage representationLearning denoisingDenoisingHigh-contrast featuresNoise levelLesion uptakeLesion-to-background ratioInput noise levelTOF-OSEMDetection task
2025
Physics-Informed List-Mode Deep Image Prior Reconstruction with Motion Correction in 3D Brain PET
Chemli Y, Najmaoui Y, Normandin M, Fakhri G, Marin T, Ouyang J. Physics-Informed List-Mode Deep Image Prior Reconstruction with Motion Correction in 3D Brain PET. 2025 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) And Room Temperature Semiconductor Detector Conference (RTSD) 2025, 1-2. DOI: 10.1109/nss/mic/rtsd57106.2025.11287851.Peer-Reviewed Original ResearchDeep Image PriorList-modeContrast recoveryMotion correctionList-mode eventsReconstructed activity distributionHoffman brain phantomNoise-resolution trade-offML-EM reconstructionLow-count dataResolution phantomBrain phantomBrain positron emission tomographySuper-resolution effectActivity distributionModel attenuationML-EMBack-projection operationsFine structureDown-sampled dataNegative log-likelihoodImage registrationUnsupervised regularizerBack-projectionImage priorsLR-PET: A Subspace-Based Dynamic PET Imaging via Explicit Non-Negative Low-Rank Factorization
Djebra Y, Najmaoui Y, Chemli Y, Normandin M, Fakhri G, Ma C, Marin T. LR-PET: A Subspace-Based Dynamic PET Imaging via Explicit Non-Negative Low-Rank Factorization. 2025, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57106.2025.11286709.Peer-Reviewed Original ResearchOn Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report)
Xia M, Bayerlein R, Chemli Y, Liu X, Ouyang J, Lin M, Fakhri G, Badawi R, Li Q, Liu C. On Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report). Journal Of Nuclear Medicine 2025, 67: 166-174. PMID: 41198241, PMCID: PMC12866389, DOI: 10.2967/jnumed.125.270653.Peer-Reviewed Original ResearchYRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography
Najmaoui Y, Chemli Y, Toussaint M, Petibon Y, Marty B, Fontaine K, Gallezot J, Razdevsek G, Orehar M, Dhaynaut M, Guehl N, Dolenec R, Pestotnik R, Johnson K, Ouyang J, Normandin M, Tetrault M, Lecomte R, Fakhri G, Marin T. YRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography. IEEE Transactions On Radiation And Plasma Medical Sciences 2025, 10: 535-546. PMID: 41424471, PMCID: PMC12714321, DOI: 10.1109/trpms.2025.3619872.Peer-Reviewed Original ResearchThis study introduces YRT-PET, an open-source GPU-accelerated software for PET image reconstruction, demonstrating high flexibility, speed, and compatibility with existing tools for dynamic imaging and motion correction.In vivo 3D myocardial membrane potential mapping in humans using PET/MRI
Bijari F, Han P, Marin T, Lee W, Chemli Y, Gertsenshteyn I, Mounime I, Djebra Y, Chi D, Normandin M, Ma C, Fakhri G. In vivo 3D myocardial membrane potential mapping in humans using PET/MRI. EJNMMI Research 2025, 15: 93. PMID: 40715686, PMCID: PMC12297085, DOI: 10.1186/s13550-025-01287-7.Peer-Reviewed Original ResearchMembrane potentialExtracellular volume fraction measurementsExtracellular volume fraction mappingBolus-plus-infusion protocolT1 mapping sequencesVolume of distributionWritten Informed ConsentCardiac PET/MR imagingRigid image registrationHumans in vivoContrast agent injectionPET motion correctionFree breathingTracer volume of distributionImage registrationBolus injectionCardiac MRMitochondrial membrane potentialCardiac diseaseHealthy subjectsPET/MR imagingImaging studiesTreatment monitoringAgent injectionPET tracersQuantitative Measurement of Tau Burden in a Dual-Time-Window Dynamic PET Imaging Protocol with [18F]MK6240
Xia Y, Dhaynaut M, Chemli Y, Lois C, Hanseeuw B, Thibault E, Groot C, Ossenkoppele R, Johnson K, El Fakhri G, Normandin M, Guehl N. Quantitative Measurement of Tau Burden in a Dual-Time-Window Dynamic PET Imaging Protocol with [18F]MK6240. Journal Of Nuclear Medicine 2025, 66: 1299-1306. PMID: 40533354, PMCID: PMC12320581, DOI: 10.2967/jnumed.125.270165.Peer-Reviewed Original Research