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1.
Clin Nucl Med ; 49(6): 540-542, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38530235

RESUMO

ABSTRACT: Radiolabeled fibroblast activation protein inhibitor (FAPI) is considered as a potential alternative agent to 18 F-FDG for tumor-specific imaging. We report 18 F-FDG and 68 Ga-FAPI-04 PET/MR findings in a 67-year-old woman with gallbladder adenocarcinoma. The lesions showed intense 18 F-FDG uptake but limited 68 Ga-FAPI-04 uptake in PET/MR. This case emphasizes the necessity for nuclear clinicians to exercise caution when assessing gallbladder lesions with limited 68 Ga-FAPI-04 uptake, underscoring the continued relevance of 18 F-FDG in this diagnostic domain.


Assuntos
Adenocarcinoma , Fluordesoxiglucose F18 , Neoplasias da Vesícula Biliar , Quinolinas , Humanos , Neoplasias da Vesícula Biliar/diagnóstico por imagem , Feminino , Idoso , Adenocarcinoma/diagnóstico por imagem , Tomografia por Emissão de Pósitrons , Imageamento por Ressonância Magnética , Imagem Multimodal
2.
Heliyon ; 9(6): e17459, 2023 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-37416642

RESUMO

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

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