skip to main content
Idioma:

A novel alternative to classify tissues from T1 and T2 relaxation times for prostate MRI

Bojorquez, Jorge Zavala ; Bricq, Stéphanie ; Brunotte, François ; Walker, Paul M. ; Lalande, Alain

Magma (New York, N.Y.), 2016, Vol.29 (5), p.777-788 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

Texto completo disponível

Citações Citado por
  • Título:
    A novel alternative to classify tissues from T1 and T2 relaxation times for prostate MRI
  • Autor: Bojorquez, Jorge Zavala ; Bricq, Stéphanie ; Brunotte, François ; Walker, Paul M. ; Lalande, Alain
  • Assuntos: Biomedical Engineering and Bioengineering ; Computer Appl. in Life Sciences ; Health Informatics ; Imaging ; Medicine ; Medicine & Public Health ; Radiology ; Research Article ; Solid State Physics
  • É parte de: Magma (New York, N.Y.), 2016, Vol.29 (5), p.777-788
  • Descrição: Objective To segment and classify the different attenuation regions from MRI at the pelvis level using the T 1 and T 2 relaxation times and anatomical knowledge as a first step towards the creation of PET/MR attenuation maps. Materials and methods Relaxation times were calculated by fitting the pixel-wise intensities of acquired T 1 - and T 2 -weighted images from eight men with inversion-recovery and multi-echo multi-slice spin-echo sequences. A decision binary tree based on relaxation times was implemented to segment and classify fat, muscle, prostate, and air (within the body). Connected component analysis and an anatomical knowledge-based procedure were implemented to localize the background and bone. Results Relaxation times at 3 T are reported for fat ( T 1  = 385 ms, T 2  = 121 ms), muscle ( T 1  = 1295 ms, T 2  = 40 ms), and prostate ( T 1  = 1700 ms, T 2  = 80 ms). The relaxation times allowed the segmentation–classification of fat, prostate, muscle, and air, and combined with anatomical knowledge, they allowed classification of bone. The good segmentation–classification of prostate [mean Dice similarity score (mDSC) = 0.70] suggests a viable implementation in oncology and that of fat (mDSC = 0.99), muscle (mDSC = 0.99), and bone (mDSCs = 0.78) advocates for its implementation in PET/MR attenuation correction. Conclusion Our method allows the segmentation and classification of the attenuation-relevant structures required for the generation of the attenuation map of PET/MR systems in prostate imaging: air, background, bone, fat, muscle, and prostate.
  • Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.