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Korean J Radiol ; 21(2): 218-227, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-31997597

RESUMEN

OBJECTIVE: This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients. MATERIALS AND METHODS: Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data. RESULTS: In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm² for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm², but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability. CONCLUSION: Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.


Asunto(s)
Adenocarcinoma/patología , Carcinoma de Células Escamosas/patología , Procesamiento de Imagen Asistido por Computador/métodos , Neoplasias del Cuello Uterino/patología , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estadificación de Neoplasias , Estudios Retrospectivos , Relación Señal-Ruido , Adulto Joven
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