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Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial.
Tanenbaum, L N; Bash, S C; Zaharchuk, G; Shankaranarayanan, A; Chamberlain, R; Wintermark, M; Beaulieu, C; Novick, M; Wang, L.
Afiliación
  • Tanenbaum LN; From RadNet (L.N.T., S.C.B.), New York, New York nuromri@gmail.com.
  • Bash SC; From RadNet (L.N.T., S.C.B.), New York, New York.
  • Zaharchuk G; Stanford University Medical Center (G.Z., C.B.), Stanford, California.
  • Shankaranarayanan A; Subtle Medical (A.S., R.C., L.W.), Menlo Park, California.
  • Chamberlain R; Subtle Medical (A.S., R.C., L.W.), Menlo Park, California.
  • Wintermark M; MD Anderson Cancer Center (M.W.), University of Texas, Houston, Texas.
  • Beaulieu C; Stanford University Medical Center (G.Z., C.B.), Stanford, California.
  • Novick M; All-American Teleradiology (M.N.), Bay Village, Ohio.
  • Wang L; Subtle Medical (A.S., R.C., L.W.), Menlo Park, California.
AJNR Am J Neuroradiol ; 44(8): 987-993, 2023 08.
Article en En | MEDLINE | ID: mdl-37414452
ABSTRACT
BACKGROUND AND

PURPOSE:

Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR. MATERIALS AND

METHODS:

From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

RESULTS:

For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR (P < .0001).

CONCLUSIONS:

Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Clinical_trials / Diagnostic_studies Límite: Humans Idioma: En Revista: AJNR Am J Neuroradiol Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Clinical_trials / Diagnostic_studies Límite: Humans Idioma: En Revista: AJNR Am J Neuroradiol Año: 2023 Tipo del documento: Article
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