T Yamamoto1, C Lacheret2, H Fukutomi1, R A Kamraoui3, L Denat1, B Zhang4, V Prevost5, L Zhang6, A Ruet7, B Triaire5, V Dousset1,2,8, P Coupé3, T Tourdias9,2,8. 1. From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France. 2. Neuroimagerie diagnostique et thérapeutique (C.L., V.D., T.T.). 3. Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre national de la recherche scientifique, Bordeaux INP, Talence, France. 4. Canon Medical Systems Europe (B.Z.), Zoetermeer, the Netherlands. 5. Canon Medical Systems Corporation (V.P., B.T.), Tochigi, Japan. 6. Canon Medical Systems China (L.Z.), Beijing, China. 7. Service de neurologie (A.R.), Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France. 8. Neurocentre Magendie (V.D., T.T.), University of Bordeaux, L'Institut national de la santé et de la recherche médicale, Bordeaux, France. 9. From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France thomas.tourdias@chu-bordeaux.fr.
Abstract
BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. CONCLUSIONS: Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. MATERIALS AND METHODS: Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. RESULTS: Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. CONCLUSIONS: Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
Authors: M Drake-Pérez; B M A Delattre; J Boto; A Fitsiori; K-O Lovblad; S Boudabbous; M I Vargas Journal: AJNR Am J Neuroradiol Date: 2018-03-01 Impact factor: 3.825