Your browser doesn't support javascript.
loading
Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.
Brain, Matthew E; Amukotuwa, Shalini; Bammer, Roland.
Afiliación
  • Brain ME; Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia.
  • Amukotuwa S; Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia.
  • Bammer R; Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia.
J Med Imaging Radiat Oncol ; 68(4): 377-384, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38577926
ABSTRACT

INTRODUCTION:

Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique is yet to be fully established. This study aims to assess whether a commercially available DLR technique applied to 2D T2-weighted FLAIR brain images allows a reduction in scan time, without compromising image quality and thus diagnostic accuracy.

METHODS:

47 participants (24 male, mean age 55.9 ± 18.7 SD years, range 20-89 years) underwent routine, clinically indicated brain MRI studies in March 2022, that included a standard-of-care (SOC) T2-weighted FLAIR sequence, and an accelerated acquisition that was reconstructed using the DLR denoising product. Overall image quality, lesion conspicuity, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artefacts for each sequence, and preferred sequence on direct comparison, were subjectively assessed by two readers.

RESULTS:

There was a strong preference for SOC FLAIR sequence for overall image quality (P = 0.01) and head-to-head comparison (P < 0.001). No difference was observed for lesion conspicuity (P = 0.49), perceived SNR (P = 1.0), and perceived CNR (P = 0.84). There was no difference in motion (P = 0.57) nor Gibbs ringing (P = 0.86) artefacts. Phase ghosting (P = 0.038) and pseudolesions were significantly more frequent (P < 0.001) on DLR images.

CONCLUSION:

DLR algorithm allowed faster FLAIR acquisition times with comparable image quality and lesion conspicuity. However, an increased incidence and severity of phase ghosting artefact and presence of pseudolesions using this technique may result in a reduction in reading speed, efficiency, and diagnostic confidence.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Relación Señal-Ruido / Aprendizaje Profundo Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Med Imaging Radiat Oncol Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS / RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Australia

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Relación Señal-Ruido / Aprendizaje Profundo Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Med Imaging Radiat Oncol Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS / RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Australia