Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis
dc.contributor.author
dc.date.accessioned
2022-09-30T09:13:18Z
dc.date.available
2022-09-30T09:13:18Z
dc.date.issued
2022-09-29
dc.identifier.issn
1662-4548
dc.identifier.uri
dc.description.abstract
The assessment of disease activity using serial brain MRI scans is one of the most valuable strategies for monitoring treatment response in patients with multiple sclerosis (MS) receiving disease-modifying treatments. Recently, several deep learning approaches have been proposed to improve this analysis, obtaining a good trade-off between sensitivity and specificity, especially when using T1-w and T2-FLAIR images as inputs. However, the need to acquire two different types of images is time-consuming, costly and not always available in clinical practice. In this paper, we investigate an approach to generate synthetic T1-w images from T2-FLAIR images and subsequently analyse the impact of using original and synthetic T1-w images on the performance of a state-of-the-art approach for longitudinal MS lesion detection. We evaluate our approach on a dataset containing 136 images from MS patients, and 73 images with lesion activity (the appearance of new T2 lesions in follow-up scans). To evaluate the synthesis of the images, we analyse the structural similarity index metric and the median absolute error and obtain consistent results. To study the impact of synthetic T1-w images, we evaluate the performance of the new lesion detection approach when using (1) both T2-FLAIR and T1-w original images, (2) only T2-FLAIR images, and (3) both T2-FLAIR and synthetic T1-w images. Sensitivities of 0.75, 0.63, and 0.81, respectively, were obtained at the same false-positive rate (0.14) for all experiments. In addition, we also present the results obtained when using the data from the international MSSEG-2 challenge, showing also an improvement when including synthetic T1-w images. In conclusion, we show that the use of synthetic images can support the lack of data or even be used instead of the original image to homogenize the contrast of the different acquisitions in new T2 lesions detection algorithms
dc.description.sponsorship
AC holds an FPI grant from the Ministerio de Ciencia, Innovación y Universidades with reference number PRE2018-083507. This work has been supported by DPI2020-114769RB-I00 from the Ministerio de Ciencia, Innovación y Universidades. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this research. This work has been also supported by ICREA Academia Program
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media
dc.relation
PID2020-114769RB-I00
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Reproducció digital del document publicat a: https://doi.org/10.3389/fnins.2022.954662
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Frontiers in Neuroscience, 2022, vol. 16, art.núm. 954662
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Articles publicats (D-ATC)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114769RB-I00/ES/MODELOS PARA LA ESCLEROSIS MULTIPLE USANDO DEEP LEARNING EN DATOS RADIOLOGICOS, CLINICOS Y DE LABORATORIO/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
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dc.relation.ProjectAcronym
dc.identifier.eissn
1662-453X