Laura Leukert 1, Fabian Bongratz2, Benita Schmitz-Koep1, Michael Dieckmeyer3, Jan S Kirschke1, Dennis M Hedderich1
1Institute of Diagnostic and Interventional Neuroradiology, TUM School of Medicine and Health, Munich, Germany
2Laboratory for Artificial Intelligence in Medical Imaging, Department of Radiology, Technical University of Munich, Munich 81675, Germany; Munich Center for Machine Learning, Munich, Germany
3Institute of Diagnostic, Interventional and Paediatric Radiology, Inselspital, University of Bern, Switzerland
Presenting Author: Laura Leukert
Synopsis
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1. Jung NY, Cho H, Kim YJ, et al. The impact of education on cortical thickness in amyloid-negative subcortical vascular dementia: cognitive reserve hypothesis. Alz Res Therapy. 2018;10(1). doi:10.1186/s13195-018-0432-5 [doi]
2. Dickerson BC, Wolk DA. MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology. 2012;78(2):84-90. doi:10.1212/wnl.0b013e31823efc6c [doi]
3. Khundrakpam BS, Lewis JD, Kostopoulos P, Carbonell F, Evans AC. Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large-Scale MRI Study. Cerebral Cortex. 2017;27(3):1721-1731. doi:10.1093/cercor/bhx038 [doi]
4. Narayana PA, Govindarajan KA, Goel P, et al. Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. NeuroImage: Clinical. 2013;2:120-131. doi:10.1016/j.nicl.2012.11.009 [doi]
5. Racine AM, Brickhouse M, Wolk DA, Dickerson BC, Alzheimer’s Disease Neuroimaging Initiative. The personalized Alzheimer’s disease cortical thickness index predicts likely pathology and clinical progression in mild cognitive impairment. Wolk D, Villemagne V, Dickerson B, eds. Alz & Dem Diag Ass & Dis Mo. 2018;10(1):301-310. doi:10.1016/j.dadm.2018.02.007 [doi]
6. Sun S, Xiao S, Guo Z, et al. Meta-analysis of cortical thickness reduction in adult schizophrenia. jpn. 2023;48(6):E461-E470. doi:10.1503/jpn.230081 [doi]
7. Lee H, Kim EY, Yang KS, Park J. Susceptibility-resistant variable-flip-angle turbo spin echo imaging for reliable estimation of cortical thickness: A feasibility study. NeuroImage. 2012;59(1):377-388. doi:10.1016/j.neuroimage.2011.07.070 [doi]
8. Thaler C, Sedlacik J, Forkert ND, et al. Effect of geometric distortion correction on thickness and volume measurements of cortical parcellations in 3D T1w gradient echo sequences. Pham D, ed. PLoS ONE. 2023;18(4):e0284440. doi:10.1371/journal.pone.0284440 [doi]
9. Kim BC, Park SJ, Choi SM, Yoon W, Seo SW, Na DL. P2‐388: Quantitative comparison of 3T and 1.5T MRI in the evaluation of cortical thickness and subcortical volume using FreeSurfer. Alzheimer’s & Dementia. 2010;6(4S_Part_14). doi:10.1016/j.jalz.2010.05.1440 [doi]
10. Yoon JH, Nickel MD, Peeters JM, Lee JM. Rapid Imaging: Recent Advances in Abdominal MRI for Reducing Acquisition Time and Its Clinical Applications. Korean J Radiol. 2019;20(12):1597. doi:10.3348/kjr.2018.0931 [doi]
11. Andre JB, Bresnahan BW, Mossa-Basha M, et al. Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations. Journal of the American College of Radiology. 2015;12(7):689-695. doi:10.1016/j.jacr.2015.03.007 [doi]
12. Vranic JE, Cross NM, Wang Y, Hippe DS, De Weerdt E, Mossa-Basha M. Compressed Sensing–Sensitivity Encoding (CS-SENSE) Accelerated Brain Imaging: Reduced Scan Time without Reduced Image Quality. AJNR Am J Neuroradiol. 2019;40(1):92-98. doi:10.3174/ajnr.a5905 [doi]
13. Mönch S, Sollmann N, Hock A, Zimmer C, Kirschke JS, Hedderich DM. Magnetic Resonance Imaging of the Brain Using Compressed Sensing – Quality Assessment in Daily Clinical Routine. Clin Neuroradiol. 2020;30(2):279-286. doi:10.1007/s00062-019-00789-x [doi]
14. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Med. 2007;58(6):1182-1195. doi:10.1002/mrm.21391 [doi]
16. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 1999;42(5):952-962.
17. Park HJ, Youn T, Jeong SO, Oh MK, Kim SY, Kim EY. SENSE factors for reliable cortical thickness measurement. NeuroImage. 2008;40(1):187-196. doi:10.1016/j.neuroimage.2007.11.013 [doi]
18. Knussmann GN, Anderson JS, Prigge MBD, et al. Test-retest reliability of FreeSurfer-derived volume, area and cortical thickness from MPRAGE and MP2RAGE brain MRI images. Neuroimage: Reports. 2022;2(2):100086. doi:10.1016/j.ynirp.2022.100086 [doi]
19. Fischl B. FreeSurfer. NeuroImage. 2012;62(2):774-781. doi:10.1016/j.neuroimage.2012.01.021 [doi]
20. Rebsamen M, Suter Y, Wiest R, Reyes M, Rummel C. Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Front Neurol. 2020;11:244. doi:10.3389/fneur.2020.00244 [doi]