دورية أكاديمية
أعرض في EDS

Automatic lesion detection at Multiple Sclerosis patients - Comparison of 2D- and 3D-FLAIR-datasets.

التفاصيل البيبلوغرافية
العنوان: Automatic lesion detection at Multiple Sclerosis patients - Comparison of 2D- and 3D-FLAIR-datasets.
المؤلفون: Seehafer S; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany. Electronic address: svea.seehafer@uksh.de., Schmill LP; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Aludin S; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Huhndorf M; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Larsen N; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Jansen O; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Stürner K; Department of Neurology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany., Peters S; Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), D-24105 Kiel, Germany.
المصدر: Multiple sclerosis and related disorders [Mult Scler Relat Disord] 2024 Aug; Vol. 88, pp. 105728. Date of Electronic Publication: 2024 Jun 13.
نوع المنشور: Journal Article; Comparative Study
اللغة: English
بيانات الدورية: Publisher: Elsevier B. V Country of Publication: Netherlands NLM ID: 101580247 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2211-0356 (Electronic) Linking ISSN: 22110348 NLM ISO Abbreviation: Mult Scler Relat Disord Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [Amsterdam] : Elsevier B. V.
مواضيع طبية MeSH: Magnetic Resonance Imaging* , Multiple Sclerosis*/diagnostic imaging , Multiple Sclerosis*/pathology , Imaging, Three-Dimensional* , Brain*/diagnostic imaging , Brain*/pathology, Humans ; Female ; Adult ; Male ; Middle Aged ; Prospective Studies ; Image Interpretation, Computer-Assisted/methods ; Young Adult
مستخلص: Background: Multiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment.
Material and Methods: In this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t-test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated.
Results: There is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001).
Conclusion: Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
فهرسة مساهمة: Keywords: Artificial intelligence software; Lesion analysis; Magnetic resonance imaging; Multiple Sclerosis
تواريخ الأحداث: Date Created: 20240623 Date Completed: 20240727 Latest Revision: 20240727
رمز التحديث: 20240729
DOI: 10.1016/j.msard.2024.105728
PMID: 38909527
قاعدة البيانات: MEDLINE
الوصف
تدمد:2211-0356
DOI:10.1016/j.msard.2024.105728