Your browser doesn't support javascript.
loading
Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.
Szczykutowicz, Timothy P; Nett, Brian; Cherkezyan, Lusik; Pozniak, Myron; Tang, Jie; Lubner, Meghan G; Hsieh, Jiang.
Afiliación
  • Szczykutowicz TP; Department of Radiology, University of Wisconsin Madison, 1111 Highland Ave, 1005 WIMR, Madison, WI 53705.
  • Nett B; Department of Medical Physics, University of Wisconsin Madison, Madison, Madison, WI.
  • Cherkezyan L; Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI.
  • Pozniak M; GE Healthcare, Waukesha, WI.
  • Tang J; GE Healthcare, Waukesha, WI.
  • Lubner MG; Department of Radiology, University of Wisconsin Madison, 1111 Highland Ave, 1005 WIMR, Madison, WI 53705.
  • Hsieh J; GE Healthcare, Waukesha, WI.
AJR Am J Roentgenol ; 216(6): 1668-1677, 2021 06.
Article en En | MEDLINE | ID: mdl-33852337
OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X / Fantasmas de Imagen / Aprendizaje Profundo Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: AJR Am J Roentgenol Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X / Fantasmas de Imagen / Aprendizaje Profundo Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: AJR Am J Roentgenol Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos