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For: Sollmann N, Löffler MT, Kronthaler S, Böhm C, Dieckmeyer M, Ruschke S, Kirschke JS, Carballido-Gamio J, Karampinos DC, Krug R, Baum T. MRI-Based Quantitative Osteoporosis Imaging at the Spine and Femur. J Magn Reson Imaging 2021;54:12-35. [PMID: 32584496 DOI: 10.1002/jmri.27260] [Cited by in Crossref: 22] [Cited by in F6Publishing: 22] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
1 Sheppard AJ, Paravastu SS, Wojnowski NM, Osamor CC, Farhadi F, Collins MT, Saboury B. Emerging Role of 18F-NaF PET/Computed Tomographic Imaging in Osteoporosis. PET Clinics 2023;18:1-20. [DOI: 10.1016/j.cpet.2022.09.001] [Reference Citation Analysis]
2 Leonhardt Y, Ketschau J, Ruschke S, Gassert FT, Glanz L, Feuerriegel GC, Gassert FG, Baum T, Kirschke JS, Braren RF, Schwaiger BJ, Makowski MR, Karampinos DC, Gersing AS. Associations of incidental vertebral fractures and longitudinal changes of MR–based proton density fat fraction and T2* measurements of vertebral bone marrow. Front Endocrinol 2022;13. [DOI: 10.3389/fendo.2022.1046547] [Reference Citation Analysis]
3 Palomo T, Muszkat P, Weiler FG, Dreyer P, Brandão CMA, Silva BC. Update on trabecular bone score. Archives of Endocrinology and Metabolism 2022;66:694-706. [DOI: 10.20945/2359-3997000000559] [Reference Citation Analysis]
4 Wu W, Gong T, Niu J, Li W, Li J, Song X, Cui S, Bian W, Wang J. Study of bone marrow microstructure in healthy young adults using intravoxel incoherent motion diffusion-weighted MRI. Front Endocrinol 2022;13. [DOI: 10.3389/fendo.2022.958151] [Reference Citation Analysis]
5 Chang FX, Fan DH, Huang G, He JH. Lumbar Spine Bone Mineral Density Measurement: Comparison of Dual-Energy X-Ray Absorptiometry and Fat Content Evaluation by Dixon Chemical Shift MRI. Int J Gen Med 2022;15:6415-24. [PMID: 35957757 DOI: 10.2147/IJGM.S370814] [Reference Citation Analysis]
6 Carballido-Gamio J. Imaging techniques to study diabetic bone disease. Curr Opin Endocrinol Diabetes Obes 2022;29:350-60. [PMID: 35799458 DOI: 10.1097/MED.0000000000000749] [Reference Citation Analysis]
7 Greve T, Rayudu NM, Dieckmeyer M, Boehm C, Ruschke S, Burian E, Kloth C, Kirschke JS, Karampinos DC, Baum T, Subburaj K, Sollmann N. Finite Element Analysis of Osteoporotic and Osteoblastic Vertebrae and Its Association With the Proton Density Fat Fraction From Chemical Shift Encoding-Based Water-Fat MRI – A Preliminary Study. Front Endocrinol 2022;13:900356. [DOI: 10.3389/fendo.2022.900356] [Reference Citation Analysis]
8 Sollmann N, Baum T. Editorial on Special Issue “Spine Imaging: Novel Image Acquisition Techniques and Analysis Tools”. Diagnostics 2022;12:1361. [DOI: 10.3390/diagnostics12061361] [Reference Citation Analysis]
9 Sollmann N, Kirschke JS, Kronthaler S, Boehm C, Dieckmeyer M, Vogele D, Kloth C, Lisson CG, Carballido-Gamio J, Link TM, Karampinos DC, Karupppasamy S, Beer M, Krug R, Baum T. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. Rofo 2022. [PMID: 35545103 DOI: 10.1055/a-1770-4626] [Reference Citation Analysis]
10 Martel D, Monga A, Chang G. Osteoporosis Imaging. Radiologic Clinics of North America 2022. [DOI: 10.1016/j.rcl.2022.02.003] [Reference Citation Analysis]
11 Kronthaler S, Diefenbach MN, Boehm C, Zamskiy M, Makowski MR, Baum T, Sollmann N, Karampinos DC. On quantification errors of R 2 * $$ {R}_2^{\ast } $$ and proton density fat fraction mapping in trabecularized bone marrow in the static dephasing regime. Magn Reson Med 2022. [PMID: 35481686 DOI: 10.1002/mrm.29279] [Reference Citation Analysis]
12 Khalil YA, Becherucci EA, Kirschke JS, Karampinos DC, Breeuwer M, Baum T, Sollmann N. Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database. Sci Data 2022;9:97. [PMID: 35322028 DOI: 10.1038/s41597-022-01222-8] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Sollmann N, Bonnheim NB, Joseph GB, Chachad R, Zhou J, Akkaya Z, Pirmoazen AM, Bailey JF, Guo X, Lazar AA, Link TM, Fields AJ, Krug R. Paraspinal Muscle in Chronic Low Back Pain: Comparison Between Standard Parameters and Chemical Shift Encoding-Based Water-Fat MRI. J Magn Reson Imaging 2022. [PMID: 35285561 DOI: 10.1002/jmri.28145] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Flannery SW, Walsh EG, Sanborn RM, Chrostek CA, Costa MQ, Kaushal SG, Murray MM, Fleming BC, Kiapour AM. Reproducibility and Post-Acquisition Correction Methods for Quantitative Magnetic Resonance Imaging of the Anterior Cruciate Ligament (ACL). J Orthop Res 2022. [PMID: 35266588 DOI: 10.1002/jor.25319] [Reference Citation Analysis]
15 Sollmann N, Becherucci EA, Boehm C, Husseini ME, Ruschke S, Burian E, Kirschke JS, Link TM, Subburaj K, Karampinos DC, Krug R, Baum T, Dieckmeyer M. Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures. Front Endocrinol (Lausanne) 2021;12:778537. [PMID: 35058878 DOI: 10.3389/fendo.2021.778537] [Reference Citation Analysis]
16 Li X, Lu R, Xie Y, Li Q, Tao H, Chen S. Identification of abnormal BMD and osteoporosis in postmenopausal women with T2*-corrected Q-Dixon and reduced-FOV IVIM: correlation with QCT. Eur Radiol. [DOI: 10.1007/s00330-021-08531-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Kale H, Yadav S. Can routine MRI spine T1 sequences be used for prediction of decreased bone density? Acta Radiol 2022;:2841851211063008. [PMID: 34989249 DOI: 10.1177/02841851211063008] [Reference Citation Analysis]
18 Schmeel FC, Lakghomi A, Lehnen NC, Haase R, Banat M, Wach J, Handke N, Vatter H, Radbruch A, Attenberger U, Luetkens JA. Proton Density Fat Fraction Spine MRI for Differentiation of Erosive Vertebral Endplate Degeneration and Infectious Spondylitis. Diagnostics 2022;12:78. [DOI: 10.3390/diagnostics12010078] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
19 Probst FA, Burian E, Malenova Y, Lyutskanova P, Stumbaum MJ, Ritschl LM, Kronthaler S, Karampinos D, Probst M. Geometric accuracy of magnetic resonance imaging-derived virtual 3-dimensional bone surface models of the mandible in comparison to computed tomography and cone beam computed tomography: A porcine cadaver study. Clin Implant Dent Relat Res 2021. [PMID: 34318580 DOI: 10.1111/cid.13033] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
20 Talanki VR, Peng Q, Shamir SB, Baete SH, Duong TQ, Wake N. Three-Dimensional Printed Anatomic Models Derived From Magnetic Resonance Imaging Data: Current State and Image Acquisition Recommendations for Appropriate Clinical Scenarios. J Magn Reson Imaging 2021. [PMID: 34046959 DOI: 10.1002/jmri.27744] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
21 Schmeel FC, Enkirch SJ, Luetkens JA, Faron A, Lehnen N, Sprinkart AM, Schmeel LC, Radbruch A, Attenberger U, Kukuk GM, Mürtz P. Diagnostic Accuracy of Quantitative Imaging Biomarkers in the Differentiation of Benign and Malignant Vertebral Lesions : Combination of Diffusion-Weighted and Proton Density Fat Fraction Spine MRI. Clin Neuroradiol 2021. [PMID: 33787957 DOI: 10.1007/s00062-021-01009-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
22 Soldati E, Rossi F, Vicente J, Guenoun D, Pithioux M, Iotti S, Malucelli E, Bendahan D. Survey of MRI Usefulness for the Clinical Assessment of Bone Microstructure. Int J Mol Sci 2021;22:2509. [PMID: 33801539 DOI: 10.3390/ijms22052509] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 8.0] [Reference Citation Analysis]
23 Zaia A, Rossi R, Galeazzi R, Sallei M, Maponi P, Scendoni P. Fractal lacunarity of trabecular bone in vertebral MRI to predict osteoporotic fracture risk in over-fifties women. The LOTO study. BMC Musculoskelet Disord 2021;22:108. [PMID: 33485322 DOI: 10.1186/s12891-021-03966-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]