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PixelPrint: A collection of three-dimensional printed CT phantoms of different respiratory diseases.
Mei, Kai; Roshkovan, Leonid; Pasyar, Pouyan; Shapira, Nadav; Gang, Grace J; Stayman, J Webster; Geagan, Michael; Noël, Peter B.
Afiliação
  • Mei K; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Roshkovan L; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Pasyar P; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Shapira N; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Gang GJ; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Stayman JW; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Geagan M; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Noël PB; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Article em En | MEDLINE | ID: mdl-37854299
ABSTRACT
Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article