Methods Inf Med - ML segmentation strategies for object interference compensation in FDG-PET lesion quantification.


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CKGROUND: Quantification of lesion activity by FDG uptake in oncological PET is severely limited by partial volume effects. A maximum likelihood (ML) expectation maximization (EM) algorithm considering regional basis functions (AWOSEM-region) had been previously developed. Regional basis functions are iteratively segmented and quantified, thus identifying the volume and the activity of the lesion.OBJECTIVES: Improvement of AWOSEM-region when analyzing proximal interfering hot objects is addressed by proper segmentation initialization steps and models of spill-out and partial volume effects. Conditions relevant to lung PET-CT studies are considered: 1) lesion close to hot organ (e.g. chest wall, heart and mediastinum), 2) two close lesions.METHODS: CT image was considered for pre-segmenting hot anatomical structures, never for lesion identification, solely defined by iterations on PET data. Further resolution recovery beyond the smooth standard clinical image was necessary to start lesion segmentation. A watershed algorithm was used to separate two close lesions. A subtraction of the spill-out from a nearby hot organ was introduced to enhance a lesion for the initial segmentation and start the further quantification steps. Biograph scanner blurring was modeled from phantom data in order to implement the procedure for 3D clinical lung studies.RESULTS: In simulations, the procedure was able to separate structures as close as one pixel-size (2.25 mm). Robustness against the input segmentation errors defining the addressed objects was tested showing that convergence was not sensitive to initial volume overestimates up to 130%. Poor robustness was found against underestimates. A clinical study of a small lung lesion close to chest wall displayed a good recovery of both lesion activity and volume.CONCLUSIONS: With proper initialization and models of spill-out from hot organs, AWOSEM-region can be successfully applied to lung oncological studies.

Resumo Limpo

ckground quantif lesion activ fdg uptak oncolog pet sever limit partial volum effect maximum likelihood ml expect maxim em algorithm consid region basi function awosemregion previous develop region basi function iter segment quantifi thus identifi volum activ lesionobject improv awosemregion analyz proxim interf hot object address proper segment initi step model spillout partial volum effect condit relev lung petct studi consid lesion close hot organ eg chest wall heart mediastinum two close lesionsmethod ct imag consid preseg hot anatom structur never lesion identif sole defin iter pet data resolut recoveri beyond smooth standard clinic imag necessari start lesion segment watersh algorithm use separ two close lesion subtract spillout nearbi hot organ introduc enhanc lesion initi segment start quantif step biograph scanner blur model phantom data order implement procedur d clinic lung studiesresult simul procedur abl separ structur close one pixels mm robust input segment error defin address object test show converg sensit initi volum overestim poor robust found underestim clinic studi small lung lesion close chest wall display good recoveri lesion activ volumeconclus proper initi model spillout hot organ awosemregion can success appli lung oncolog studi

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